maxjhandsome
/
llvm-project
forked from OSchip/llvm-project
1
0
Fork 0
Code Issues Pull Requests Packages Releases Wiki Activity
llvm-project/polly/lib/CodeGen/PPCGCodeGeneration.cpp

3662 lines
131 KiB
C++
Raw Blame History

//===------ PPCGCodeGeneration.cpp - Polly Accelerator Code Generation. ---===//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
//
//===----------------------------------------------------------------------===//
//
// Take a scop created by ScopInfo and map it to GPU code using the ppcg
// GPU mapping strategy.
//
//===----------------------------------------------------------------------===//
#include "polly/CodeGen/PPCGCodeGeneration.h"
#include "polly/CodeGen/CodeGeneration.h"
#include "polly/CodeGen/IslAst.h"
#include "polly/CodeGen/IslNodeBuilder.h"
#include "polly/CodeGen/PerfMonitor.h"
#include "polly/CodeGen/Utils.h"
#include "polly/DependenceInfo.h"
#include "polly/LinkAllPasses.h"
#include "polly/Options.h"
#include "polly/ScopDetection.h"
#include "polly/ScopInfo.h"
#include "polly/Support/SCEVValidator.h"
#include "llvm/ADT/PostOrderIterator.h"
#include "llvm/Analysis/TargetTransformInfo.h"
#include "llvm/IR/IntrinsicsNVPTX.h"
#include "llvm/IR/LegacyPassManager.h"
#include "llvm/IR/Verifier.h"
#include "llvm/IRReader/IRReader.h"
#include "llvm/InitializePasses.h"
#include "llvm/Linker/Linker.h"
#include "llvm/Support/SourceMgr.h"
#include "llvm/Support/TargetRegistry.h"
#include "llvm/Target/TargetMachine.h"
#include "llvm/Transforms/IPO/PassManagerBuilder.h"
#include "llvm/Transforms/Utils/BasicBlockUtils.h"
#include "isl/union_map.h"
#include <algorithm>
extern "C" {
#include "ppcg/cuda.h"
#include "ppcg/gpu.h"
#include "ppcg/ppcg.h"
}
#include "llvm/Support/Debug.h"
using namespace polly;
using namespace llvm;
#define DEBUG_TYPE "polly-codegen-ppcg"
static cl::opt<bool> DumpSchedule("polly-acc-dump-schedule",
cl::desc("Dump the computed GPU Schedule"),
cl::Hidden, cl::init(false), cl::ZeroOrMore,
cl::cat(PollyCategory));
static cl::opt<bool>
DumpCode("polly-acc-dump-code",
cl::desc("Dump C code describing the GPU mapping"), cl::Hidden,
cl::init(false), cl::ZeroOrMore, cl::cat(PollyCategory));
static cl::opt<bool> DumpKernelIR("polly-acc-dump-kernel-ir",
cl::desc("Dump the kernel LLVM-IR"),
cl::Hidden, cl::init(false), cl::ZeroOrMore,
cl::cat(PollyCategory));
static cl::opt<bool> DumpKernelASM("polly-acc-dump-kernel-asm",
cl::desc("Dump the kernel assembly code"),
cl::Hidden, cl::init(false), cl::ZeroOrMore,
cl::cat(PollyCategory));
static cl::opt<bool> FastMath("polly-acc-fastmath",
cl::desc("Allow unsafe math optimizations"),
cl::Hidden, cl::init(false), cl::ZeroOrMore,
cl::cat(PollyCategory));
static cl::opt<bool> SharedMemory("polly-acc-use-shared",
cl::desc("Use shared memory"), cl::Hidden,
cl::init(false), cl::ZeroOrMore,
cl::cat(PollyCategory));
static cl::opt<bool> PrivateMemory("polly-acc-use-private",
cl::desc("Use private memory"), cl::Hidden,
cl::init(false), cl::ZeroOrMore,
cl::cat(PollyCategory));
bool polly::PollyManagedMemory;
static cl::opt<bool, true>
XManagedMemory("polly-acc-codegen-managed-memory",
cl::desc("Generate Host kernel code assuming"
" that all memory has been"
" declared as managed memory"),
cl::location(PollyManagedMemory), cl::Hidden,
cl::init(false), cl::ZeroOrMore, cl::cat(PollyCategory));
static cl::opt<bool>
FailOnVerifyModuleFailure("polly-acc-fail-on-verify-module-failure",
cl::desc("Fail and generate a backtrace if"
" verifyModule fails on the GPU "
" kernel module."),
cl::Hidden, cl::init(false), cl::ZeroOrMore,
cl::cat(PollyCategory));
static cl::opt<std::string> CUDALibDevice(
"polly-acc-libdevice", cl::desc("Path to CUDA libdevice"), cl::Hidden,
cl::init("/usr/local/cuda/nvvm/libdevice/libdevice.compute_20.10.ll"),
cl::ZeroOrMore, cl::cat(PollyCategory));
static cl::opt<std::string>
CudaVersion("polly-acc-cuda-version",
cl::desc("The CUDA version to compile for"), cl::Hidden,
cl::init("sm_30"), cl::ZeroOrMore, cl::cat(PollyCategory));
static cl::opt<int>
MinCompute("polly-acc-mincompute",
cl::desc("Minimal number of compute statements to run on GPU."),
cl::Hidden, cl::init(10 * 512 * 512));
GPURuntime polly::GPURuntimeChoice;
static cl::opt<GPURuntime, true> XGPURuntimeChoice(
"polly-gpu-runtime", cl::desc("The GPU Runtime API to target"),
cl::values(clEnumValN(GPURuntime::CUDA, "libcudart",
"use the CUDA Runtime API"),
clEnumValN(GPURuntime::OpenCL, "libopencl",
"use the OpenCL Runtime API")),
cl::location(polly::GPURuntimeChoice), cl::init(GPURuntime::CUDA),
cl::ZeroOrMore, cl::cat(PollyCategory));
GPUArch polly::GPUArchChoice;
static cl::opt<GPUArch, true>
XGPUArchChoice("polly-gpu-arch", cl::desc("The GPU Architecture to target"),
cl::values(clEnumValN(GPUArch::NVPTX64, "nvptx64",
"target NVIDIA 64-bit architecture"),
clEnumValN(GPUArch::SPIR32, "spir32",
"target SPIR 32-bit architecture"),
clEnumValN(GPUArch::SPIR64, "spir64",
"target SPIR 64-bit architecture")),
cl::location(polly::GPUArchChoice),
cl::init(GPUArch::NVPTX64), cl::ZeroOrMore,
cl::cat(PollyCategory));
extern bool polly::PerfMonitoring;
/// Return a unique name for a Scop, which is the scop region with the
/// function name.
std::string getUniqueScopName(const Scop *S) {
return "Scop Region: " + S->getNameStr() +
" | Function: " + std::string(S->getFunction().getName());
}
/// Used to store information PPCG wants for kills. This information is
/// used by live range reordering.
///
/// @see computeLiveRangeReordering
/// @see GPUNodeBuilder::createPPCGScop
/// @see GPUNodeBuilder::createPPCGProg
struct MustKillsInfo {
/// Collection of all kill statements that will be sequenced at the end of
/// PPCGScop->schedule.
///
/// The nodes in `KillsSchedule` will be merged using `isl_schedule_set`
/// which merges schedules in *arbitrary* order.
/// (we don't care about the order of the kills anyway).
isl::schedule KillsSchedule;
/// Map from kill statement instances to scalars that need to be
/// killed.
///
/// We currently derive kill information for:
/// 1. phi nodes. PHI nodes are not alive outside the scop and can
/// consequently all be killed.
/// 2. Scalar arrays that are not used outside the Scop. This is
/// checked by `isScalarUsesContainedInScop`.
/// [params] -> { [Stmt_phantom[] -> ref_phantom[]] -> scalar_to_kill[] }
isl::union_map TaggedMustKills;
/// Tagged must kills stripped of the tags.
/// [params] -> { Stmt_phantom[] -> scalar_to_kill[] }
isl::union_map MustKills;
MustKillsInfo() : KillsSchedule() {}
};
/// Check if SAI's uses are entirely contained within Scop S.
/// If a scalar is used only with a Scop, we are free to kill it, as no data
/// can flow in/out of the value any more.
/// @see computeMustKillsInfo
static bool isScalarUsesContainedInScop(const Scop &S,
const ScopArrayInfo *SAI) {
assert(SAI->isValueKind() && "this function only deals with scalars."
" Dealing with arrays required alias analysis");
const Region &R = S.getRegion();
for (User *U : SAI->getBasePtr()->users()) {
Instruction *I = dyn_cast<Instruction>(U);
assert(I && "invalid user of scop array info");
if (!R.contains(I))
return false;
}
return true;
}
/// Compute must-kills needed to enable live range reordering with PPCG.
///
/// @params S The Scop to compute live range reordering information
/// @returns live range reordering information that can be used to setup
/// PPCG.
static MustKillsInfo computeMustKillsInfo(const Scop &S) {
const isl::space ParamSpace = S.getParamSpace();
MustKillsInfo Info;
// 1. Collect all ScopArrayInfo that satisfy *any* of the criteria:
// 1.1 phi nodes in scop.
// 1.2 scalars that are only used within the scop
SmallVector<isl::id, 4> KillMemIds;
for (ScopArrayInfo *SAI : S.arrays()) {
if (SAI->isPHIKind() ||
(SAI->isValueKind() && isScalarUsesContainedInScop(S, SAI)))
KillMemIds.push_back(isl::manage(SAI->getBasePtrId().release()));
}
Info.TaggedMustKills = isl::union_map::empty(ParamSpace.ctx());
Info.MustKills = isl::union_map::empty(ParamSpace.ctx());
// Initialising KillsSchedule to `isl_set_empty` creates an empty node in the
// schedule:
// - filter: "[control] -> { }"
// So, we choose to not create this to keep the output a little nicer,
// at the cost of some code complexity.
Info.KillsSchedule = {};
for (isl::id &ToKillId : KillMemIds) {
isl::id KillStmtId = isl::id::alloc(
S.getIslCtx(),
std::string("SKill_phantom_").append(ToKillId.get_name()), nullptr);
// NOTE: construction of tagged_must_kill:
// 2. We need to construct a map:
// [param] -> { [Stmt_phantom[] -> ref_phantom[]] -> scalar_to_kill[] }
// To construct this, we use `isl_map_domain_product` on 2 maps`:
// 2a. StmtToScalar:
// [param] -> { Stmt_phantom[] -> scalar_to_kill[] }
// 2b. PhantomRefToScalar:
// [param] -> { ref_phantom[] -> scalar_to_kill[] }
//
// Combining these with `isl_map_domain_product` gives us
// TaggedMustKill:
// [param] -> { [Stmt[] -> phantom_ref[]] -> scalar_to_kill[] }
// 2a. [param] -> { Stmt[] -> scalar_to_kill[] }
isl::map StmtToScalar = isl::map::universe(ParamSpace);
StmtToScalar = StmtToScalar.set_tuple_id(isl::dim::in, isl::id(KillStmtId));
StmtToScalar = StmtToScalar.set_tuple_id(isl::dim::out, isl::id(ToKillId));
isl::id PhantomRefId = isl::id::alloc(
S.getIslCtx(), std::string("ref_phantom") + ToKillId.get_name(),
nullptr);
// 2b. [param] -> { phantom_ref[] -> scalar_to_kill[] }
isl::map PhantomRefToScalar = isl::map::universe(ParamSpace);
PhantomRefToScalar =
PhantomRefToScalar.set_tuple_id(isl::dim::in, PhantomRefId);
PhantomRefToScalar =
PhantomRefToScalar.set_tuple_id(isl::dim::out, ToKillId);
// 2. [param] -> { [Stmt[] -> phantom_ref[]] -> scalar_to_kill[] }
isl::map TaggedMustKill = StmtToScalar.domain_product(PhantomRefToScalar);
Info.TaggedMustKills = Info.TaggedMustKills.unite(TaggedMustKill);
// 2. [param] -> { Stmt[] -> scalar_to_kill[] }
Info.MustKills = Info.TaggedMustKills.domain_factor_domain();
// 3. Create the kill schedule of the form:
// "[param] -> { Stmt_phantom[] }"
// Then add this to Info.KillsSchedule.
isl::space KillStmtSpace = ParamSpace;
KillStmtSpace = KillStmtSpace.set_tuple_id(isl::dim::set, KillStmtId);
isl::union_set KillStmtDomain = isl::set::universe(KillStmtSpace);
isl::schedule KillSchedule = isl::schedule::from_domain(KillStmtDomain);
if (!Info.KillsSchedule.is_null())
Info.KillsSchedule = isl::manage(
isl_schedule_set(Info.KillsSchedule.release(), KillSchedule.copy()));
else
Info.KillsSchedule = KillSchedule;
}
return Info;
}
/// Create the ast expressions for a ScopStmt.
///
/// This function is a callback for to generate the ast expressions for each
/// of the scheduled ScopStmts.
static __isl_give isl_id_to_ast_expr *pollyBuildAstExprForStmt(
void *StmtT, __isl_take isl_ast_build *Build_C,
isl_multi_pw_aff *(*FunctionIndex)(__isl_take isl_multi_pw_aff *MPA,
isl_id *Id, void *User),
void *UserIndex,
isl_ast_expr *(*FunctionExpr)(isl_ast_expr *Expr, isl_id *Id, void *User),
void *UserExpr) {
ScopStmt *Stmt = (ScopStmt *)StmtT;
if (!Stmt || !Build_C)
return NULL;
isl::ast_build Build = isl::manage_copy(Build_C);
isl::ctx Ctx = Build.ctx();
isl::id_to_ast_expr RefToExpr = isl::id_to_ast_expr::alloc(Ctx, 0);
Stmt->setAstBuild(Build);
for (MemoryAccess *Acc : *Stmt) {
isl::map AddrFunc = Acc->getAddressFunction();
AddrFunc = AddrFunc.intersect_domain(Stmt->getDomain());
isl::id RefId = Acc->getId();
isl::pw_multi_aff PMA = isl::pw_multi_aff::from_map(AddrFunc);
isl::multi_pw_aff MPA = isl::multi_pw_aff(PMA);
MPA = MPA.coalesce();
MPA = isl::manage(FunctionIndex(MPA.release(), RefId.get(), UserIndex));
isl::ast_expr Access = Build.access_from(MPA);
Access = isl::manage(FunctionExpr(Access.release(), RefId.get(), UserExpr));
RefToExpr = RefToExpr.set(RefId, Access);
}
return RefToExpr.release();
}
/// Given a LLVM Type, compute its size in bytes,
static int computeSizeInBytes(const Type *T) {
int bytes = T->getPrimitiveSizeInBits() / 8;
if (bytes == 0)
bytes = T->getScalarSizeInBits() / 8;
return bytes;
}
/// Generate code for a GPU specific isl AST.
///
/// The GPUNodeBuilder augments the general existing IslNodeBuilder, which
/// generates code for general-purpose AST nodes, with special functionality
/// for generating GPU specific user nodes.
///
/// @see GPUNodeBuilder::createUser
class GPUNodeBuilder : public IslNodeBuilder {
public:
GPUNodeBuilder(PollyIRBuilder &Builder, ScopAnnotator &Annotator,
const DataLayout &DL, LoopInfo &LI, ScalarEvolution &SE,
DominatorTree &DT, Scop &S, BasicBlock *StartBlock,
gpu_prog *Prog, GPURuntime Runtime, GPUArch Arch)
: IslNodeBuilder(Builder, Annotator, DL, LI, SE, DT, S, StartBlock),
Prog(Prog), Runtime(Runtime), Arch(Arch) {
getExprBuilder().setIDToSAI(&IDToSAI);
}
/// Create after-run-time-check initialization code.
void initializeAfterRTH();
/// Finalize the generated scop.
void finalize() override;
/// Track if the full build process was successful.
///
/// This value is set to false, if throughout the build process an error
/// occurred which prevents us from generating valid GPU code.
bool BuildSuccessful = true;
/// The maximal number of loops surrounding a sequential kernel.
unsigned DeepestSequential = 0;
/// The maximal number of loops surrounding a parallel kernel.
unsigned DeepestParallel = 0;
/// Return the name to set for the ptx_kernel.
std::string getKernelFuncName(int Kernel_id);
private:
/// A vector of array base pointers for which a new ScopArrayInfo was created.
///
/// This vector is used to delete the ScopArrayInfo when it is not needed any
/// more.
std::vector<Value *> LocalArrays;
/// A map from ScopArrays to their corresponding device allocations.
std::map<ScopArrayInfo *, Value *> DeviceAllocations;
/// The current GPU context.
Value *GPUContext;
/// The set of isl_ids allocated in the kernel
std::vector<isl_id *> KernelIds;
/// A module containing GPU code.
///
/// This pointer is only set in case we are currently generating GPU code.
std::unique_ptr<Module> GPUModule;
/// The GPU program we generate code for.
gpu_prog *Prog;
/// The GPU Runtime implementation to use (OpenCL or CUDA).
GPURuntime Runtime;
/// The GPU Architecture to target.
GPUArch Arch;
/// Class to free isl_ids.
class IslIdDeleter {
public:
void operator()(__isl_take isl_id *Id) { isl_id_free(Id); };
};
/// A set containing all isl_ids allocated in a GPU kernel.
///
/// By releasing this set all isl_ids will be freed.
std::set<std::unique_ptr<isl_id, IslIdDeleter>> KernelIDs;
IslExprBuilder::IDToScopArrayInfoTy IDToSAI;
/// Create code for user-defined AST nodes.
///
/// These AST nodes can be of type:
///
/// - ScopStmt: A computational statement (TODO)
/// - Kernel: A GPU kernel call (TODO)
/// - Data-Transfer: A GPU <-> CPU data-transfer
/// - In-kernel synchronization
/// - In-kernel memory copy statement
///
/// @param UserStmt The ast node to generate code for.
void createUser(__isl_take isl_ast_node *UserStmt) override;
void createFor(__isl_take isl_ast_node *Node) override;
enum DataDirection { HOST_TO_DEVICE, DEVICE_TO_HOST };
/// Create code for a data transfer statement
///
/// @param TransferStmt The data transfer statement.
/// @param Direction The direction in which to transfer data.
void createDataTransfer(__isl_take isl_ast_node *TransferStmt,
enum DataDirection Direction);
/// Find llvm::Values referenced in GPU kernel.
///
/// @param Kernel The kernel to scan for llvm::Values
///
/// @returns A tuple, whose:
/// - First element contains the set of values referenced by the
/// kernel
/// - Second element contains the set of functions referenced by the
/// kernel. All functions in the set satisfy
/// `isValidFunctionInKernel`.
/// - Third element contains loops that have induction variables
/// which are used in the kernel, *and* these loops are *neither*
/// in the scop, nor do they immediately surroung the Scop.
/// See [Code generation of induction variables of loops outside
/// Scops]
std::tuple<SetVector<Value *>, SetVector<Function *>, SetVector<const Loop *>,
isl::space>
getReferencesInKernel(ppcg_kernel *Kernel);
/// Compute the sizes of the execution grid for a given kernel.
///
/// @param Kernel The kernel to compute grid sizes for.
///
/// @returns A tuple with grid sizes for X and Y dimension
std::tuple<Value *, Value *> getGridSizes(ppcg_kernel *Kernel);
/// Get the managed array pointer for sending host pointers to the device.
/// \note
/// This is to be used only with managed memory
Value *getManagedDeviceArray(gpu_array_info *Array, ScopArrayInfo *ArrayInfo);
/// Compute the sizes of the thread blocks for a given kernel.
///
/// @param Kernel The kernel to compute thread block sizes for.
///
/// @returns A tuple with thread block sizes for X, Y, and Z dimensions.
std::tuple<Value *, Value *, Value *> getBlockSizes(ppcg_kernel *Kernel);
/// Store a specific kernel launch parameter in the array of kernel launch
/// parameters.
///
/// @param Parameters The list of parameters in which to store.
/// @param Param The kernel launch parameter to store.
/// @param Index The index in the parameter list, at which to store the
/// parameter.
void insertStoreParameter(Instruction *Parameters, Instruction *Param,
int Index);
/// Create kernel launch parameters.
///
/// @param Kernel The kernel to create parameters for.
/// @param F The kernel function that has been created.
/// @param SubtreeValues The set of llvm::Values referenced by this kernel.
///
/// @returns A stack allocated array with pointers to the parameter
/// values that are passed to the kernel.
Value *createLaunchParameters(ppcg_kernel *Kernel, Function *F,
SetVector<Value *> SubtreeValues);
/// Create declarations for kernel variable.
///
/// This includes shared memory declarations.
///
/// @param Kernel The kernel definition to create variables for.
/// @param FN The function into which to generate the variables.
void createKernelVariables(ppcg_kernel *Kernel, Function *FN);
/// Add CUDA annotations to module.
///
/// Add a set of CUDA annotations that declares the maximal block dimensions
/// that will be used to execute the CUDA kernel. This allows the NVIDIA
/// PTX compiler to bound the number of allocated registers to ensure the
/// resulting kernel is known to run with up to as many block dimensions
/// as specified here.
///
/// @param M The module to add the annotations to.
/// @param BlockDimX The size of block dimension X.
/// @param BlockDimY The size of block dimension Y.
/// @param BlockDimZ The size of block dimension Z.
void addCUDAAnnotations(Module *M, Value *BlockDimX, Value *BlockDimY,
Value *BlockDimZ);
/// Create GPU kernel.
///
/// Code generate the kernel described by @p KernelStmt.
///
/// @param KernelStmt The ast node to generate kernel code for.
void createKernel(__isl_take isl_ast_node *KernelStmt);
/// Generate code that computes the size of an array.
///
/// @param Array The array for which to compute a size.
Value *getArraySize(gpu_array_info *Array);
/// Generate code to compute the minimal offset at which an array is accessed.
///
/// The offset of an array is the minimal array location accessed in a scop.
///
/// Example:
///
/// for (long i = 0; i < 100; i++)
/// A[i + 42] += ...
///
/// getArrayOffset(A) results in 42.
///
/// @param Array The array for which to compute the offset.
/// @returns An llvm::Value that contains the offset of the array.
Value *getArrayOffset(gpu_array_info *Array);
/// Prepare the kernel arguments for kernel code generation
///
/// @param Kernel The kernel to generate code for.
/// @param FN The function created for the kernel.
void prepareKernelArguments(ppcg_kernel *Kernel, Function *FN);
/// Create kernel function.
///
/// Create a kernel function located in a newly created module that can serve
/// as target for device code generation. Set the Builder to point to the
/// start block of this newly created function.
///
/// @param Kernel The kernel to generate code for.
/// @param SubtreeValues The set of llvm::Values referenced by this kernel.
/// @param SubtreeFunctions The set of llvm::Functions referenced by this
/// kernel.
void createKernelFunction(ppcg_kernel *Kernel,
SetVector<Value *> &SubtreeValues,
SetVector<Function *> &SubtreeFunctions);
/// Create the declaration of a kernel function.
///
/// The kernel function takes as arguments:
///
/// - One i8 pointer for each external array reference used in the kernel.
/// - Host iterators
/// - Parameters
/// - Other LLVM Value references (TODO)
///
/// @param Kernel The kernel to generate the function declaration for.
/// @param SubtreeValues The set of llvm::Values referenced by this kernel.
///
/// @returns The newly declared function.
Function *createKernelFunctionDecl(ppcg_kernel *Kernel,
SetVector<Value *> &SubtreeValues);
/// Insert intrinsic functions to obtain thread and block ids.
///
/// @param The kernel to generate the intrinsic functions for.
void insertKernelIntrinsics(ppcg_kernel *Kernel);
/// Insert function calls to retrieve the SPIR group/local ids.
///
/// @param Kernel The kernel to generate the function calls for.
/// @param SizeTypeIs64Bit Whether size_t of the openCl device is 64bit.
void insertKernelCallsSPIR(ppcg_kernel *Kernel, bool SizeTypeIs64bit);
/// Setup the creation of functions referenced by the GPU kernel.
///
/// 1. Create new function declarations in GPUModule which are the same as
/// SubtreeFunctions.
///
/// 2. Populate IslNodeBuilder::ValueMap with mappings from
/// old functions (that come from the original module) to new functions
/// (that are created within GPUModule). That way, we generate references
/// to the correct function (in GPUModule) in BlockGenerator.
///
/// @see IslNodeBuilder::ValueMap
/// @see BlockGenerator::GlobalMap
/// @see BlockGenerator::getNewValue
/// @see GPUNodeBuilder::getReferencesInKernel.
///
/// @param SubtreeFunctions The set of llvm::Functions referenced by
/// this kernel.
void setupKernelSubtreeFunctions(SetVector<Function *> SubtreeFunctions);
/// Create a global-to-shared or shared-to-global copy statement.
///
/// @param CopyStmt The copy statement to generate code for
void createKernelCopy(ppcg_kernel_stmt *CopyStmt);
/// Create code for a ScopStmt called in @p Expr.
///
/// @param Expr The expression containing the call.
/// @param KernelStmt The kernel statement referenced in the call.
void createScopStmt(isl_ast_expr *Expr, ppcg_kernel_stmt *KernelStmt);
/// Create an in-kernel synchronization call.
void createKernelSync();
/// Create a PTX assembly string for the current GPU kernel.
///
/// @returns A string containing the corresponding PTX assembly code.
std::string createKernelASM();
/// Remove references from the dominator tree to the kernel function @p F.
///
/// @param F The function to remove references to.
void clearDominators(Function *F);
/// Remove references from scalar evolution to the kernel function @p F.
///
/// @param F The function to remove references to.
void clearScalarEvolution(Function *F);
/// Remove references from loop info to the kernel function @p F.
///
/// @param F The function to remove references to.
void clearLoops(Function *F);
/// Check if the scop requires to be linked with CUDA's libdevice.
bool requiresCUDALibDevice();
/// Link with the NVIDIA libdevice library (if needed and available).
void addCUDALibDevice();
/// Finalize the generation of the kernel function.
///
/// Free the LLVM-IR module corresponding to the kernel and -- if requested --
/// dump its IR to stderr.
///
/// @returns The Assembly string of the kernel.
std::string finalizeKernelFunction();
/// Finalize the generation of the kernel arguments.
///
/// This function ensures that not-read-only scalars used in a kernel are
/// stored back to the global memory location they are backed with before
/// the kernel terminates.
///
/// @params Kernel The kernel to finalize kernel arguments for.
void finalizeKernelArguments(ppcg_kernel *Kernel);
/// Create code that allocates memory to store arrays on device.
void allocateDeviceArrays();
/// Create code to prepare the managed device pointers.
void prepareManagedDeviceArrays();
/// Free all allocated device arrays.
void freeDeviceArrays();
/// Create a call to initialize the GPU context.
///
/// @returns A pointer to the newly initialized context.
Value *createCallInitContext();
/// Create a call to get the device pointer for a kernel allocation.
///
/// @param Allocation The Polly GPU allocation
///
/// @returns The device parameter corresponding to this allocation.
Value *createCallGetDevicePtr(Value *Allocation);
/// Create a call to free the GPU context.
///
/// @param Context A pointer to an initialized GPU context.
void createCallFreeContext(Value *Context);
/// Create a call to allocate memory on the device.
///
/// @param Size The size of memory to allocate
///
/// @returns A pointer that identifies this allocation.
Value *createCallAllocateMemoryForDevice(Value *Size);
/// Create a call to free a device array.
///
/// @param Array The device array to free.
void createCallFreeDeviceMemory(Value *Array);
/// Create a call to copy data from host to device.
///
/// @param HostPtr A pointer to the host data that should be copied.
/// @param DevicePtr A device pointer specifying the location to copy to.
void createCallCopyFromHostToDevice(Value *HostPtr, Value *DevicePtr,
Value *Size);
/// Create a call to copy data from device to host.
///
/// @param DevicePtr A pointer to the device data that should be copied.
/// @param HostPtr A host pointer specifying the location to copy to.
void createCallCopyFromDeviceToHost(Value *DevicePtr, Value *HostPtr,
Value *Size);
/// Create a call to synchronize Host & Device.
/// \note
/// This is to be used only with managed memory.
void createCallSynchronizeDevice();
/// Create a call to get a kernel from an assembly string.
///
/// @param Buffer The string describing the kernel.
/// @param Entry The name of the kernel function to call.
///
/// @returns A pointer to a kernel object
Value *createCallGetKernel(Value *Buffer, Value *Entry);
/// Create a call to free a GPU kernel.
///
/// @param GPUKernel THe kernel to free.
void createCallFreeKernel(Value *GPUKernel);
/// Create a call to launch a GPU kernel.
///
/// @param GPUKernel The kernel to launch.
/// @param GridDimX The size of the first grid dimension.
/// @param GridDimY The size of the second grid dimension.
/// @param GridBlockX The size of the first block dimension.
/// @param GridBlockY The size of the second block dimension.
/// @param GridBlockZ The size of the third block dimension.
/// @param Parameters A pointer to an array that contains itself pointers to
/// the parameter values passed for each kernel argument.
void createCallLaunchKernel(Value *GPUKernel, Value *GridDimX,
Value *GridDimY, Value *BlockDimX,
Value *BlockDimY, Value *BlockDimZ,
Value *Parameters);
};
std::string GPUNodeBuilder::getKernelFuncName(int Kernel_id) {
return "FUNC_" + S.getFunction().getName().str() + "_SCOP_" +
std::to_string(S.getID()) + "_KERNEL_" + std::to_string(Kernel_id);
}
void GPUNodeBuilder::initializeAfterRTH() {
BasicBlock *NewBB = SplitBlock(Builder.GetInsertBlock(),
&*Builder.GetInsertPoint(), &DT, &LI);
NewBB->setName("polly.acc.initialize");
Builder.SetInsertPoint(&NewBB->front());
GPUContext = createCallInitContext();
if (!PollyManagedMemory)
allocateDeviceArrays();
else
prepareManagedDeviceArrays();
}
void GPUNodeBuilder::finalize() {
if (!PollyManagedMemory)
freeDeviceArrays();
createCallFreeContext(GPUContext);
IslNodeBuilder::finalize();
}
void GPUNodeBuilder::allocateDeviceArrays() {
assert(!PollyManagedMemory &&
"Managed memory will directly send host pointers "
"to the kernel. There is no need for device arrays");
isl_ast_build *Build = isl_ast_build_from_context(S.getContext().release());
for (int i = 0; i < Prog->n_array; ++i) {
gpu_array_info *Array = &Prog->array[i];
auto *ScopArray = (ScopArrayInfo *)Array->user;
std::string DevArrayName("p_dev_array_");
DevArrayName.append(Array->name);
Value *ArraySize = getArraySize(Array);
Value *Offset = getArrayOffset(Array);
if (Offset)
ArraySize = Builder.CreateSub(
ArraySize,
Builder.CreateMul(Offset,
Builder.getInt64(ScopArray->getElemSizeInBytes())));
const SCEV *SizeSCEV = SE.getSCEV(ArraySize);
// It makes no sense to have an array of size 0. The CUDA API will
// throw an error anyway if we invoke `cuMallocManaged` with size `0`. We
// choose to be defensive and catch this at the compile phase. It is
// most likely that we are doing something wrong with size computation.
if (SizeSCEV->isZero()) {
errs() << getUniqueScopName(&S)
<< " has computed array size 0: " << *ArraySize
<< " | for array: " << *(ScopArray->getBasePtr())
<< ". This is illegal, exiting.\n";
report_fatal_error("array size was computed to be 0");
}
Value *DevArray = createCallAllocateMemoryForDevice(ArraySize);
DevArray->setName(DevArrayName);
DeviceAllocations[ScopArray] = DevArray;
}
isl_ast_build_free(Build);
}
void GPUNodeBuilder::prepareManagedDeviceArrays() {
assert(PollyManagedMemory &&
"Device array most only be prepared in managed-memory mode");
for (int i = 0; i < Prog->n_array; ++i) {
gpu_array_info *Array = &Prog->array[i];
ScopArrayInfo *ScopArray = (ScopArrayInfo *)Array->user;
Value *HostPtr;
if (gpu_array_is_scalar(Array))
HostPtr = BlockGen.getOrCreateAlloca(ScopArray);
else
HostPtr = ScopArray->getBasePtr();
HostPtr = getLatestValue(HostPtr);
Value *Offset = getArrayOffset(Array);
if (Offset) {
HostPtr = Builder.CreatePointerCast(
HostPtr, ScopArray->getElementType()->getPointerTo());
HostPtr = Builder.CreateGEP(ScopArray->getElementType(), HostPtr, Offset);
}
HostPtr = Builder.CreatePointerCast(HostPtr, Builder.getInt8PtrTy());
DeviceAllocations[ScopArray] = HostPtr;
}
}
void GPUNodeBuilder::addCUDAAnnotations(Module *M, Value *BlockDimX,
Value *BlockDimY, Value *BlockDimZ) {
auto AnnotationNode = M->getOrInsertNamedMetadata("nvvm.annotations");
for (auto &F : *M) {
if (F.getCallingConv() != CallingConv::PTX_Kernel)
continue;
Value *V[] = {BlockDimX, BlockDimY, BlockDimZ};
Metadata *Elements[] = {
ValueAsMetadata::get(&F), MDString::get(M->getContext(), "maxntidx"),
ValueAsMetadata::get(V[0]), MDString::get(M->getContext(), "maxntidy"),
ValueAsMetadata::get(V[1]), MDString::get(M->getContext(), "maxntidz"),
ValueAsMetadata::get(V[2]),
};
MDNode *Node = MDNode::get(M->getContext(), Elements);
AnnotationNode->addOperand(Node);
}
}
void GPUNodeBuilder::freeDeviceArrays() {
assert(!PollyManagedMemory && "Managed memory does not use device arrays");
for (auto &Array : DeviceAllocations)
createCallFreeDeviceMemory(Array.second);
}
Value *GPUNodeBuilder::createCallGetKernel(Value *Buffer, Value *Entry) {
const char *Name = "polly_getKernel";
Module *M = Builder.GetInsertBlock()->getParent()->getParent();
Function *F = M->getFunction(Name);
// If F is not available, declare it.
if (!F) {
GlobalValue::LinkageTypes Linkage = Function::ExternalLinkage;
std::vector<Type *> Args;
Args.push_back(Builder.getInt8PtrTy());
Args.push_back(Builder.getInt8PtrTy());
FunctionType *Ty = FunctionType::get(Builder.getInt8PtrTy(), Args, false);
F = Function::Create(Ty, Linkage, Name, M);
}
return Builder.CreateCall(F, {Buffer, Entry});
}
Value *GPUNodeBuilder::createCallGetDevicePtr(Value *Allocation) {
const char *Name = "polly_getDevicePtr";
Module *M = Builder.GetInsertBlock()->getParent()->getParent();
Function *F = M->getFunction(Name);
// If F is not available, declare it.
if (!F) {
GlobalValue::LinkageTypes Linkage = Function::ExternalLinkage;
std::vector<Type *> Args;
Args.push_back(Builder.getInt8PtrTy());
FunctionType *Ty = FunctionType::get(Builder.getInt8PtrTy(), Args, false);
F = Function::Create(Ty, Linkage, Name, M);
}
return Builder.CreateCall(F, {Allocation});
}
void GPUNodeBuilder::createCallLaunchKernel(Value *GPUKernel, Value *GridDimX,
Value *GridDimY, Value *BlockDimX,
Value *BlockDimY, Value *BlockDimZ,
Value *Parameters) {
const char *Name = "polly_launchKernel";
Module *M = Builder.GetInsertBlock()->getParent()->getParent();
Function *F = M->getFunction(Name);
// If F is not available, declare it.
if (!F) {
GlobalValue::LinkageTypes Linkage = Function::ExternalLinkage;
std::vector<Type *> Args;
Args.push_back(Builder.getInt8PtrTy());
Args.push_back(Builder.getInt32Ty());
Args.push_back(Builder.getInt32Ty());
Args.push_back(Builder.getInt32Ty());
Args.push_back(Builder.getInt32Ty());
Args.push_back(Builder.getInt32Ty());
Args.push_back(Builder.getInt8PtrTy());
FunctionType *Ty = FunctionType::get(Builder.getVoidTy(), Args, false);
F = Function::Create(Ty, Linkage, Name, M);
}
Builder.CreateCall(F, {GPUKernel, GridDimX, GridDimY, BlockDimX, BlockDimY,
BlockDimZ, Parameters});
}
void GPUNodeBuilder::createCallFreeKernel(Value *GPUKernel) {
const char *Name = "polly_freeKernel";
Module *M = Builder.GetInsertBlock()->getParent()->getParent();
Function *F = M->getFunction(Name);
// If F is not available, declare it.
if (!F) {
GlobalValue::LinkageTypes Linkage = Function::ExternalLinkage;
std::vector<Type *> Args;
Args.push_back(Builder.getInt8PtrTy());
FunctionType *Ty = FunctionType::get(Builder.getVoidTy(), Args, false);
F = Function::Create(Ty, Linkage, Name, M);
}
Builder.CreateCall(F, {GPUKernel});
}
void GPUNodeBuilder::createCallFreeDeviceMemory(Value *Array) {
assert(!PollyManagedMemory &&
"Managed memory does not allocate or free memory "
"for device");
const char *Name = "polly_freeDeviceMemory";
Module *M = Builder.GetInsertBlock()->getParent()->getParent();
Function *F = M->getFunction(Name);
// If F is not available, declare it.
if (!F) {
GlobalValue::LinkageTypes Linkage = Function::ExternalLinkage;
std::vector<Type *> Args;
Args.push_back(Builder.getInt8PtrTy());
FunctionType *Ty = FunctionType::get(Builder.getVoidTy(), Args, false);
F = Function::Create(Ty, Linkage, Name, M);
}
Builder.CreateCall(F, {Array});
}
Value *GPUNodeBuilder::createCallAllocateMemoryForDevice(Value *Size) {
assert(!PollyManagedMemory &&
"Managed memory does not allocate or free memory "
"for device");
const char *Name = "polly_allocateMemoryForDevice";
Module *M = Builder.GetInsertBlock()->getParent()->getParent();
Function *F = M->getFunction(Name);
// If F is not available, declare it.
if (!F) {
GlobalValue::LinkageTypes Linkage = Function::ExternalLinkage;
std::vector<Type *> Args;
Args.push_back(Builder.getInt64Ty());
FunctionType *Ty = FunctionType::get(Builder.getInt8PtrTy(), Args, false);
F = Function::Create(Ty, Linkage, Name, M);
}
return Builder.CreateCall(F, {Size});
}
void GPUNodeBuilder::createCallCopyFromHostToDevice(Value *HostData,
Value *DeviceData,
Value *Size) {
assert(!PollyManagedMemory &&
"Managed memory does not transfer memory between "
"device and host");
const char *Name = "polly_copyFromHostToDevice";
Module *M = Builder.GetInsertBlock()->getParent()->getParent();
Function *F = M->getFunction(Name);
// If F is not available, declare it.
if (!F) {
GlobalValue::LinkageTypes Linkage = Function::ExternalLinkage;
std::vector<Type *> Args;
Args.push_back(Builder.getInt8PtrTy());
Args.push_back(Builder.getInt8PtrTy());
Args.push_back(Builder.getInt64Ty());
FunctionType *Ty = FunctionType::get(Builder.getVoidTy(), Args, false);
F = Function::Create(Ty, Linkage, Name, M);
}
Builder.CreateCall(F, {HostData, DeviceData, Size});
}
void GPUNodeBuilder::createCallCopyFromDeviceToHost(Value *DeviceData,
Value *HostData,
Value *Size) {
assert(!PollyManagedMemory &&
"Managed memory does not transfer memory between "
"device and host");
const char *Name = "polly_copyFromDeviceToHost";
Module *M = Builder.GetInsertBlock()->getParent()->getParent();
Function *F = M->getFunction(Name);
// If F is not available, declare it.
if (!F) {
GlobalValue::LinkageTypes Linkage = Function::ExternalLinkage;
std::vector<Type *> Args;
Args.push_back(Builder.getInt8PtrTy());
Args.push_back(Builder.getInt8PtrTy());
Args.push_back(Builder.getInt64Ty());
FunctionType *Ty = FunctionType::get(Builder.getVoidTy(), Args, false);
F = Function::Create(Ty, Linkage, Name, M);
}
Builder.CreateCall(F, {DeviceData, HostData, Size});
}
void GPUNodeBuilder::createCallSynchronizeDevice() {
assert(PollyManagedMemory && "explicit synchronization is only necessary for "
"managed memory");
const char *Name = "polly_synchronizeDevice";
Module *M = Builder.GetInsertBlock()->getParent()->getParent();
Function *F = M->getFunction(Name);
// If F is not available, declare it.
if (!F) {
GlobalValue::LinkageTypes Linkage = Function::ExternalLinkage;
FunctionType *Ty = FunctionType::get(Builder.getVoidTy(), false);
F = Function::Create(Ty, Linkage, Name, M);
}
Builder.CreateCall(F);
}
Value *GPUNodeBuilder::createCallInitContext() {
const char *Name;
switch (Runtime) {
case GPURuntime::CUDA:
Name = "polly_initContextCUDA";
break;
case GPURuntime::OpenCL:
Name = "polly_initContextCL";
break;
}
Module *M = Builder.GetInsertBlock()->getParent()->getParent();
Function *F = M->getFunction(Name);
// If F is not available, declare it.
if (!F) {
GlobalValue::LinkageTypes Linkage = Function::ExternalLinkage;
std::vector<Type *> Args;
FunctionType *Ty = FunctionType::get(Builder.getInt8PtrTy(), Args, false);
F = Function::Create(Ty, Linkage, Name, M);
}
return Builder.CreateCall(F, {});
}
void GPUNodeBuilder::createCallFreeContext(Value *Context) {
const char *Name = "polly_freeContext";
Module *M = Builder.GetInsertBlock()->getParent()->getParent();
Function *F = M->getFunction(Name);
// If F is not available, declare it.
if (!F) {
GlobalValue::LinkageTypes Linkage = Function::ExternalLinkage;
std::vector<Type *> Args;
Args.push_back(Builder.getInt8PtrTy());
FunctionType *Ty = FunctionType::get(Builder.getVoidTy(), Args, false);
F = Function::Create(Ty, Linkage, Name, M);
}
Builder.CreateCall(F, {Context});
}
/// Check if one string is a prefix of another.
///
/// @param String The string in which to look for the prefix.
/// @param Prefix The prefix to look for.
static bool isPrefix(std::string String, std::string Prefix) {
return String.find(Prefix) == 0;
}
Value *GPUNodeBuilder::getArraySize(gpu_array_info *Array) {
isl::ast_build Build = isl::ast_build::from_context(S.getContext());
Value *ArraySize = ConstantInt::get(Builder.getInt64Ty(), Array->size);
if (!gpu_array_is_scalar(Array)) {
isl::multi_pw_aff ArrayBound = isl::manage_copy(Array->bound);
isl::pw_aff OffsetDimZero = ArrayBound.at(0);
isl::ast_expr Res = Build.expr_from(OffsetDimZero);
for (unsigned int i = 1; i < Array->n_index; i++) {
isl::pw_aff Bound_I = ArrayBound.at(i);
isl::ast_expr Expr = Build.expr_from(Bound_I);
Res = Res.mul(Expr);
}
Value *NumElements = ExprBuilder.create(Res.release());
if (NumElements->getType() != ArraySize->getType())
NumElements = Builder.CreateSExt(NumElements, ArraySize->getType());
ArraySize = Builder.CreateMul(ArraySize, NumElements);
}
return ArraySize;
}
Value *GPUNodeBuilder::getArrayOffset(gpu_array_info *Array) {
if (gpu_array_is_scalar(Array))
return nullptr;
isl::ast_build Build = isl::ast_build::from_context(S.getContext());
isl::set Min = isl::manage_copy(Array->extent).lexmin();
isl::set ZeroSet = isl::set::universe(Min.get_space());
for (long i = 0, n = Min.tuple_dim().release(); i < n; i++)
ZeroSet = ZeroSet.fix_si(isl::dim::set, i, 0);
if (Min.is_subset(ZeroSet)) {
return nullptr;
}
isl::ast_expr Result = isl::ast_expr::from_val(isl::val(Min.ctx(), 0));
for (long i = 0, n = Min.tuple_dim().release(); i < n; i++) {
if (i > 0) {
isl::pw_aff Bound_I =
isl::manage(isl_multi_pw_aff_get_pw_aff(Array->bound, i - 1));
isl::ast_expr BExpr = Build.expr_from(Bound_I);
Result = Result.mul(BExpr);
}
isl::pw_aff DimMin = Min.dim_min(i);
isl::ast_expr MExpr = Build.expr_from(DimMin);
Result = Result.add(MExpr);
}
return ExprBuilder.create(Result.release());
}
Value *GPUNodeBuilder::getManagedDeviceArray(gpu_array_info *Array,
ScopArrayInfo *ArrayInfo) {
assert(PollyManagedMemory && "Only used when you wish to get a host "
"pointer for sending data to the kernel, "
"with managed memory");
std::map<ScopArrayInfo *, Value *>::iterator it;
it = DeviceAllocations.find(ArrayInfo);
assert(it != DeviceAllocations.end() &&
"Device array expected to be available");
return it->second;
}
void GPUNodeBuilder::createDataTransfer(__isl_take isl_ast_node *TransferStmt,
enum DataDirection Direction) {
assert(!PollyManagedMemory && "Managed memory needs no data transfers");
isl_ast_expr *Expr = isl_ast_node_user_get_expr(TransferStmt);
isl_ast_expr *Arg = isl_ast_expr_get_op_arg(Expr, 0);
isl_id *Id = isl_ast_expr_get_id(Arg);
auto Array = (gpu_array_info *)isl_id_get_user(Id);
auto ScopArray = (ScopArrayInfo *)(Array->user);
Value *Size = getArraySize(Array);
Value *Offset = getArrayOffset(Array);
Value *DevPtr = DeviceAllocations[ScopArray];
Value *HostPtr;
if (gpu_array_is_scalar(Array))
HostPtr = BlockGen.getOrCreateAlloca(ScopArray);
else
HostPtr = ScopArray->getBasePtr();
HostPtr = getLatestValue(HostPtr);
if (Offset) {
HostPtr = Builder.CreatePointerCast(
HostPtr, ScopArray->getElementType()->getPointerTo());
HostPtr = Builder.CreateGEP(ScopArray->getElementType(), HostPtr, Offset);
}
HostPtr = Builder.CreatePointerCast(HostPtr, Builder.getInt8PtrTy());
if (Offset) {
Size = Builder.CreateSub(
Size, Builder.CreateMul(
Offset, Builder.getInt64(ScopArray->getElemSizeInBytes())));
}
if (Direction == HOST_TO_DEVICE)
createCallCopyFromHostToDevice(HostPtr, DevPtr, Size);
else
createCallCopyFromDeviceToHost(DevPtr, HostPtr, Size);
isl_id_free(Id);
isl_ast_expr_free(Arg);
isl_ast_expr_free(Expr);
isl_ast_node_free(TransferStmt);
}
void GPUNodeBuilder::createUser(__isl_take isl_ast_node *UserStmt) {
isl_ast_expr *Expr = isl_ast_node_user_get_expr(UserStmt);
isl_ast_expr *StmtExpr = isl_ast_expr_get_op_arg(Expr, 0);
isl_id *Id = isl_ast_expr_get_id(StmtExpr);
isl_id_free(Id);
isl_ast_expr_free(StmtExpr);
const char *Str = isl_id_get_name(Id);
if (!strcmp(Str, "kernel")) {
createKernel(UserStmt);
if (PollyManagedMemory)
createCallSynchronizeDevice();
isl_ast_expr_free(Expr);
return;
}
if (!strcmp(Str, "init_device")) {
initializeAfterRTH();
isl_ast_node_free(UserStmt);
isl_ast_expr_free(Expr);
return;
}
if (!strcmp(Str, "clear_device")) {
finalize();
isl_ast_node_free(UserStmt);
isl_ast_expr_free(Expr);
return;
}
if (isPrefix(Str, "to_device")) {
if (!PollyManagedMemory)
createDataTransfer(UserStmt, HOST_TO_DEVICE);
else
isl_ast_node_free(UserStmt);
isl_ast_expr_free(Expr);
return;
}
if (isPrefix(Str, "from_device")) {
if (!PollyManagedMemory) {
createDataTransfer(UserStmt, DEVICE_TO_HOST);
} else {
isl_ast_node_free(UserStmt);
}
isl_ast_expr_free(Expr);
return;
}
isl_id *Anno = isl_ast_node_get_annotation(UserStmt);
struct ppcg_kernel_stmt *KernelStmt =
(struct ppcg_kernel_stmt *)isl_id_get_user(Anno);
isl_id_free(Anno);
switch (KernelStmt->type) {
case ppcg_kernel_domain:
createScopStmt(Expr, KernelStmt);
isl_ast_node_free(UserStmt);
return;
case ppcg_kernel_copy:
createKernelCopy(KernelStmt);
isl_ast_expr_free(Expr);
isl_ast_node_free(UserStmt);
return;
case ppcg_kernel_sync:
createKernelSync();
isl_ast_expr_free(Expr);
isl_ast_node_free(UserStmt);
return;
}
isl_ast_expr_free(Expr);
isl_ast_node_free(UserStmt);
}
void GPUNodeBuilder::createFor(__isl_take isl_ast_node *Node) {
createForSequential(isl::manage(Node).as<isl::ast_node_for>(), false);
}
void GPUNodeBuilder::createKernelCopy(ppcg_kernel_stmt *KernelStmt) {
isl_ast_expr *LocalIndex = isl_ast_expr_copy(KernelStmt->u.c.local_index);
LocalIndex = isl_ast_expr_address_of(LocalIndex);
Value *LocalAddr = ExprBuilder.create(LocalIndex);
isl_ast_expr *Index = isl_ast_expr_copy(KernelStmt->u.c.index);
Index = isl_ast_expr_address_of(Index);
Value *GlobalAddr = ExprBuilder.create(Index);
Type *IndexTy = cast<PointerType>(GlobalAddr->getType())->getElementType();
if (KernelStmt->u.c.read) {
LoadInst *Load = Builder.CreateLoad(IndexTy, GlobalAddr, "shared.read");
Builder.CreateStore(Load, LocalAddr);
} else {
LoadInst *Load = Builder.CreateLoad(IndexTy, LocalAddr, "shared.write");
Builder.CreateStore(Load, GlobalAddr);
}
}
void GPUNodeBuilder::createScopStmt(isl_ast_expr *Expr,
ppcg_kernel_stmt *KernelStmt) {
auto Stmt = (ScopStmt *)KernelStmt->u.d.stmt->stmt;
isl_id_to_ast_expr *Indexes = KernelStmt->u.d.ref2expr;
LoopToScevMapT LTS;
LTS.insert(OutsideLoopIterations.begin(), OutsideLoopIterations.end());
createSubstitutions(Expr, Stmt, LTS);
if (Stmt->isBlockStmt())
BlockGen.copyStmt(*Stmt, LTS, Indexes);
else
RegionGen.copyStmt(*Stmt, LTS, Indexes);
}
void GPUNodeBuilder::createKernelSync() {
Module *M = Builder.GetInsertBlock()->getParent()->getParent();
const char *SpirName = "__gen_ocl_barrier_global";
Function *Sync;
switch (Arch) {
case GPUArch::SPIR64:
case GPUArch::SPIR32:
Sync = M->getFunction(SpirName);
// If Sync is not available, declare it.
if (!Sync) {
GlobalValue::LinkageTypes Linkage = Function::ExternalLinkage;
std::vector<Type *> Args;
FunctionType *Ty = FunctionType::get(Builder.getVoidTy(), Args, false);
Sync = Function::Create(Ty, Linkage, SpirName, M);
Sync->setCallingConv(CallingConv::SPIR_FUNC);
}
break;
case GPUArch::NVPTX64:
Sync = Intrinsic::getDeclaration(M, Intrinsic::nvvm_barrier0);
break;
}
Builder.CreateCall(Sync, {});
}
/// Collect llvm::Values referenced from @p Node
///
/// This function only applies to isl_ast_nodes that are user_nodes referring
/// to a ScopStmt. All other node types are ignore.
///
/// @param Node The node to collect references for.
/// @param User A user pointer used as storage for the data that is collected.
///
/// @returns isl_bool_true if data could be collected successfully.
isl_bool collectReferencesInGPUStmt(__isl_keep isl_ast_node *Node, void *User) {
if (isl_ast_node_get_type(Node) != isl_ast_node_user)
return isl_bool_true;
isl_ast_expr *Expr = isl_ast_node_user_get_expr(Node);
isl_ast_expr *StmtExpr = isl_ast_expr_get_op_arg(Expr, 0);
isl_id *Id = isl_ast_expr_get_id(StmtExpr);
const char *Str = isl_id_get_name(Id);
isl_id_free(Id);
isl_ast_expr_free(StmtExpr);
isl_ast_expr_free(Expr);
if (!isPrefix(Str, "Stmt"))
return isl_bool_true;
Id = isl_ast_node_get_annotation(Node);
auto *KernelStmt = (ppcg_kernel_stmt *)isl_id_get_user(Id);
auto Stmt = (ScopStmt *)KernelStmt->u.d.stmt->stmt;
isl_id_free(Id);
addReferencesFromStmt(Stmt, User, false /* CreateScalarRefs */);
return isl_bool_true;
}
/// A list of functions that are available in NVIDIA's libdevice.
const std::set<std::string> CUDALibDeviceFunctions = {
"exp", "expf", "expl", "cos", "cosf", "sqrt", "sqrtf",
"copysign", "copysignf", "copysignl", "log", "logf", "powi", "powif"};
// A map from intrinsics to their corresponding libdevice functions.
const std::map<std::string, std::string> IntrinsicToLibdeviceFunc = {
{"llvm.exp.f64", "exp"},
{"llvm.exp.f32", "expf"},
{"llvm.powi.f64.i32", "powi"},
{"llvm.powi.f32.i32", "powif"}};
/// Return the corresponding CUDA libdevice function name @p Name.
/// Note that this function will try to convert instrinsics in the list
/// IntrinsicToLibdeviceFunc into libdevice functions.
/// This is because some intrinsics such as `exp`
/// are not supported by the NVPTX backend.
/// If this restriction of the backend is lifted, we should refactor our code
/// so that we use intrinsics whenever possible.
///
/// Return "" if we are not compiling for CUDA.
std::string getCUDALibDeviceFuntion(StringRef NameRef) {
std::string Name = NameRef.str();
auto It = IntrinsicToLibdeviceFunc.find(Name);
if (It != IntrinsicToLibdeviceFunc.end())
return getCUDALibDeviceFuntion(It->second);
if (CUDALibDeviceFunctions.count(Name))
return ("__nv_" + Name);
return "";
}
/// Check if F is a function that we can code-generate in a GPU kernel.
static bool isValidFunctionInKernel(llvm::Function *F, bool AllowLibDevice) {
assert(F && "F is an invalid pointer");
// We string compare against the name of the function to allow
// all variants of the intrinsic "llvm.sqrt.*", "llvm.fabs", and
// "llvm.copysign".
const StringRef Name = F->getName();
if (AllowLibDevice && getCUDALibDeviceFuntion(Name).length() > 0)
return true;
return F->isIntrinsic() &&
(Name.startswith("llvm.sqrt") || Name.startswith("llvm.fabs") ||
Name.startswith("llvm.copysign"));
}
/// Do not take `Function` as a subtree value.
///
/// We try to take the reference of all subtree values and pass them along
/// to the kernel from the host. Taking an address of any function and
/// trying to pass along is nonsensical. Only allow `Value`s that are not
/// `Function`s.
static bool isValidSubtreeValue(llvm::Value *V) { return !isa<Function>(V); }
/// Return `Function`s from `RawSubtreeValues`.
static SetVector<Function *>
getFunctionsFromRawSubtreeValues(SetVector<Value *> RawSubtreeValues,
bool AllowCUDALibDevice) {
SetVector<Function *> SubtreeFunctions;
for (Value *It : RawSubtreeValues) {
Function *F = dyn_cast<Function>(It);
if (F) {
assert(isValidFunctionInKernel(F, AllowCUDALibDevice) &&
"Code should have bailed out by "
"this point if an invalid function "
"were present in a kernel.");
SubtreeFunctions.insert(F);
}
}
return SubtreeFunctions;
}
std::tuple<SetVector<Value *>, SetVector<Function *>, SetVector<const Loop *>,
isl::space>
GPUNodeBuilder::getReferencesInKernel(ppcg_kernel *Kernel) {
SetVector<Value *> SubtreeValues;
SetVector<const SCEV *> SCEVs;
SetVector<const Loop *> Loops;
isl::space ParamSpace = isl::space(S.getIslCtx(), 0, 0).params();
SubtreeReferences References = {
LI, SE, S, ValueMap, SubtreeValues, SCEVs, getBlockGenerator(),
&ParamSpace};
for (const auto &I : IDToValue)
SubtreeValues.insert(I.second);
// NOTE: this is populated in IslNodeBuilder::addParameters
// See [Code generation of induction variables of loops outside Scops].
for (const auto &I : OutsideLoopIterations)
SubtreeValues.insert(cast<SCEVUnknown>(I.second)->getValue());
isl_ast_node_foreach_descendant_top_down(
Kernel->tree, collectReferencesInGPUStmt, &References);
for (const SCEV *Expr : SCEVs) {
findValues(Expr, SE, SubtreeValues);
findLoops(Expr, Loops);
}
Loops.remove_if([this](const Loop *L) {
return S.contains(L) || L->contains(S.getEntry());
});
for (auto &SAI : S.arrays())
SubtreeValues.remove(SAI->getBasePtr());
isl_space *Space = S.getParamSpace().release();
for (long i = 0, n = isl_space_dim(Space, isl_dim_param); i < n; i++) {
isl_id *Id = isl_space_get_dim_id(Space, isl_dim_param, i);
assert(IDToValue.count(Id));
Value *Val = IDToValue[Id];
SubtreeValues.remove(Val);
isl_id_free(Id);
}
isl_space_free(Space);
for (long i = 0, n = isl_space_dim(Kernel->space, isl_dim_set); i < n; i++) {
isl_id *Id = isl_space_get_dim_id(Kernel->space, isl_dim_set, i);
assert(IDToValue.count(Id));
Value *Val = IDToValue[Id];
SubtreeValues.remove(Val);
isl_id_free(Id);
}
// Note: { ValidSubtreeValues, ValidSubtreeFunctions } partitions
// SubtreeValues. This is important, because we should not lose any
// SubtreeValues in the process of constructing the
// "ValidSubtree{Values, Functions} sets. Nor should the set
// ValidSubtree{Values, Functions} have any common element.
auto ValidSubtreeValuesIt =
make_filter_range(SubtreeValues, isValidSubtreeValue);
SetVector<Value *> ValidSubtreeValues(ValidSubtreeValuesIt.begin(),
ValidSubtreeValuesIt.end());
bool AllowCUDALibDevice = Arch == GPUArch::NVPTX64;
SetVector<Function *> ValidSubtreeFunctions(
getFunctionsFromRawSubtreeValues(SubtreeValues, AllowCUDALibDevice));
// @see IslNodeBuilder::getReferencesInSubtree
SetVector<Value *> ReplacedValues;
for (Value *V : ValidSubtreeValues) {
auto It = ValueMap.find(V);
if (It == ValueMap.end())
ReplacedValues.insert(V);
else
ReplacedValues.insert(It->second);
}
return std::make_tuple(ReplacedValues, ValidSubtreeFunctions, Loops,
ParamSpace);
}
void GPUNodeBuilder::clearDominators(Function *F) {
DomTreeNode *N = DT.getNode(&F->getEntryBlock());
std::vector<BasicBlock *> Nodes;
for (po_iterator<DomTreeNode *> I = po_begin(N), E = po_end(N); I != E; ++I)
Nodes.push_back(I->getBlock());
for (BasicBlock *BB : Nodes)
DT.eraseNode(BB);
}
void GPUNodeBuilder::clearScalarEvolution(Function *F) {
for (BasicBlock &BB : *F) {
Loop *L = LI.getLoopFor(&BB);
if (L)
SE.forgetLoop(L);
}
}
void GPUNodeBuilder::clearLoops(Function *F) {
SmallSet<Loop *, 1> WorkList;
for (BasicBlock &BB : *F) {
Loop *L = LI.getLoopFor(&BB);
if (L)
WorkList.insert(L);
}
for (auto *L : WorkList)
LI.erase(L);
}
std::tuple<Value *, Value *> GPUNodeBuilder::getGridSizes(ppcg_kernel *Kernel) {
std::vector<Value *> Sizes;
isl::ast_build Context = isl::ast_build::from_context(S.getContext());
isl::multi_pw_aff GridSizePwAffs = isl::manage_copy(Kernel->grid_size);
for (long i = 0; i < Kernel->n_grid; i++) {
isl::pw_aff Size = GridSizePwAffs.at(i);
isl::ast_expr GridSize = Context.expr_from(Size);
Value *Res = ExprBuilder.create(GridSize.release());
Res = Builder.CreateTrunc(Res, Builder.getInt32Ty());
Sizes.push_back(Res);
}
for (long i = Kernel->n_grid; i < 3; i++)
Sizes.push_back(ConstantInt::get(Builder.getInt32Ty(), 1));
return std::make_tuple(Sizes[0], Sizes[1]);
}
std::tuple<Value *, Value *, Value *>
GPUNodeBuilder::getBlockSizes(ppcg_kernel *Kernel) {
std::vector<Value *> Sizes;
for (long i = 0; i < Kernel->n_block; i++) {
Value *Res = ConstantInt::get(Builder.getInt32Ty(), Kernel->block_dim[i]);
Sizes.push_back(Res);
}
for (long i = Kernel->n_block; i < 3; i++)
Sizes.push_back(ConstantInt::get(Builder.getInt32Ty(), 1));
return std::make_tuple(Sizes[0], Sizes[1], Sizes[2]);
}
void GPUNodeBuilder::insertStoreParameter(Instruction *Parameters,
Instruction *Param, int Index) {
Value *Slot = Builder.CreateGEP(
Parameters->getType()->getPointerElementType(), Parameters,
{Builder.getInt64(0), Builder.getInt64(Index)});
Value *ParamTyped = Builder.CreatePointerCast(Param, Builder.getInt8PtrTy());
Builder.CreateStore(ParamTyped, Slot);
}
Value *
GPUNodeBuilder::createLaunchParameters(ppcg_kernel *Kernel, Function *F,
SetVector<Value *> SubtreeValues) {
const int NumArgs = F->arg_size();
std::vector<int> ArgSizes(NumArgs);
// If we are using the OpenCL Runtime, we need to add the kernel argument
// sizes to the end of the launch-parameter list, so OpenCL can determine
// how big the respective kernel arguments are.
// Here we need to reserve adequate space for that.
Type *ArrayTy;
if (Runtime == GPURuntime::OpenCL)
ArrayTy = ArrayType::get(Builder.getInt8PtrTy(), 2 * NumArgs);
else
ArrayTy = ArrayType::get(Builder.getInt8PtrTy(), NumArgs);
BasicBlock *EntryBlock =
&Builder.GetInsertBlock()->getParent()->getEntryBlock();
auto AddressSpace = F->getParent()->getDataLayout().getAllocaAddrSpace();
std::string Launch = "polly_launch_" + std::to_string(Kernel->id);
Instruction *Parameters = new AllocaInst(
ArrayTy, AddressSpace, Launch + "_params", EntryBlock->getTerminator());
int Index = 0;
for (long i = 0; i < Prog->n_array; i++) {
if (!ppcg_kernel_requires_array_argument(Kernel, i))
continue;
isl_id *Id = isl_space_get_tuple_id(Prog->array[i].space, isl_dim_set);
const ScopArrayInfo *SAI = ScopArrayInfo::getFromId(isl::manage(Id));
if (Runtime == GPURuntime::OpenCL)
ArgSizes[Index] = SAI->getElemSizeInBytes();
Value *DevArray = nullptr;
if (PollyManagedMemory) {
DevArray = getManagedDeviceArray(&Prog->array[i],
const_cast<ScopArrayInfo *>(SAI));
} else {
DevArray = DeviceAllocations[const_cast<ScopArrayInfo *>(SAI)];
DevArray = createCallGetDevicePtr(DevArray);
}
assert(DevArray != nullptr && "Array to be offloaded to device not "
"initialized");
Value *Offset = getArrayOffset(&Prog->array[i]);
if (Offset) {
DevArray = Builder.CreatePointerCast(
DevArray, SAI->getElementType()->getPointerTo());
DevArray = Builder.CreateGEP(SAI->getElementType(), DevArray,
Builder.CreateNeg(Offset));
DevArray = Builder.CreatePointerCast(DevArray, Builder.getInt8PtrTy());
}
Value *Slot = Builder.CreateGEP(
ArrayTy, Parameters, {Builder.getInt64(0), Builder.getInt64(Index)});
if (gpu_array_is_read_only_scalar(&Prog->array[i])) {
Value *ValPtr = nullptr;
if (PollyManagedMemory)
ValPtr = DevArray;
else
ValPtr = BlockGen.getOrCreateAlloca(SAI);
assert(ValPtr != nullptr && "ValPtr that should point to a valid object"
" to be stored into Parameters");
Value *ValPtrCast =
Builder.CreatePointerCast(ValPtr, Builder.getInt8PtrTy());
Builder.CreateStore(ValPtrCast, Slot);
} else {
Instruction *Param =
new AllocaInst(Builder.getInt8PtrTy(), AddressSpace,
Launch + "_param_" + std::to_string(Index),
EntryBlock->getTerminator());
Builder.CreateStore(DevArray, Param);
Value *ParamTyped =
Builder.CreatePointerCast(Param, Builder.getInt8PtrTy());
Builder.CreateStore(ParamTyped, Slot);
}
Index++;
}
int NumHostIters = isl_space_dim(Kernel->space, isl_dim_set);
for (long i = 0; i < NumHostIters; i++) {
isl_id *Id = isl_space_get_dim_id(Kernel->space, isl_dim_set, i);
Value *Val = IDToValue[Id];
isl_id_free(Id);
if (Runtime == GPURuntime::OpenCL)
ArgSizes[Index] = computeSizeInBytes(Val->getType());
Instruction *Param =
new AllocaInst(Val->getType(), AddressSpace,
Launch + "_param_" + std::to_string(Index),
EntryBlock->getTerminator());
Builder.CreateStore(Val, Param);
insertStoreParameter(Parameters, Param, Index);
Index++;
}
int NumVars = isl_space_dim(Kernel->space, isl_dim_param);
for (long i = 0; i < NumVars; i++) {
isl_id *Id = isl_space_get_dim_id(Kernel->space, isl_dim_param, i);
Value *Val = IDToValue[Id];
if (ValueMap.count(Val))
Val = ValueMap[Val];
isl_id_free(Id);
if (Runtime == GPURuntime::OpenCL)
ArgSizes[Index] = computeSizeInBytes(Val->getType());
Instruction *Param =
new AllocaInst(Val->getType(), AddressSpace,
Launch + "_param_" + std::to_string(Index),
EntryBlock->getTerminator());
Builder.CreateStore(Val, Param);
insertStoreParameter(Parameters, Param, Index);
Index++;
}
for (auto Val : SubtreeValues) {
if (Runtime == GPURuntime::OpenCL)
ArgSizes[Index] = computeSizeInBytes(Val->getType());
Instruction *Param =
new AllocaInst(Val->getType(), AddressSpace,
Launch + "_param_" + std::to_string(Index),
EntryBlock->getTerminator());
Builder.CreateStore(Val, Param);
insertStoreParameter(Parameters, Param, Index);
Index++;
}
if (Runtime == GPURuntime::OpenCL) {
for (int i = 0; i < NumArgs; i++) {
Value *Val = ConstantInt::get(Builder.getInt32Ty(), ArgSizes[i]);
Instruction *Param =
new AllocaInst(Builder.getInt32Ty(), AddressSpace,
Launch + "_param_size_" + std::to_string(i),
EntryBlock->getTerminator());
Builder.CreateStore(Val, Param);
insertStoreParameter(Parameters, Param, Index);
Index++;
}
}
auto Location = EntryBlock->getTerminator();
return new BitCastInst(Parameters, Builder.getInt8PtrTy(),
Launch + "_params_i8ptr", Location);
}
void GPUNodeBuilder::setupKernelSubtreeFunctions(
SetVector<Function *> SubtreeFunctions) {
for (auto Fn : SubtreeFunctions) {
const std::string ClonedFnName = Fn->getName().str();
Function *Clone = GPUModule->getFunction(ClonedFnName);
if (!Clone)
Clone =
Function::Create(Fn->getFunctionType(), GlobalValue::ExternalLinkage,
ClonedFnName, GPUModule.get());
assert(Clone && "Expected cloned function to be initialized.");
assert(ValueMap.find(Fn) == ValueMap.end() &&
"Fn already present in ValueMap");
ValueMap[Fn] = Clone;
}
}
void GPUNodeBuilder::createKernel(__isl_take isl_ast_node *KernelStmt) {
isl_id *Id = isl_ast_node_get_annotation(KernelStmt);
ppcg_kernel *Kernel = (ppcg_kernel *)isl_id_get_user(Id);
isl_id_free(Id);
isl_ast_node_free(KernelStmt);
if (Kernel->n_grid > 1)
DeepestParallel = std::max(
DeepestParallel, (unsigned)isl_space_dim(Kernel->space, isl_dim_set));
else
DeepestSequential = std::max(
DeepestSequential, (unsigned)isl_space_dim(Kernel->space, isl_dim_set));
Value *BlockDimX, *BlockDimY, *BlockDimZ;
std::tie(BlockDimX, BlockDimY, BlockDimZ) = getBlockSizes(Kernel);
SetVector<Value *> SubtreeValues;
SetVector<Function *> SubtreeFunctions;
SetVector<const Loop *> Loops;
isl::space ParamSpace;
std::tie(SubtreeValues, SubtreeFunctions, Loops, ParamSpace) =
getReferencesInKernel(Kernel);
// Add parameters that appear only in the access function to the kernel
// space. This is important to make sure that all isl_ids are passed as
// parameters to the kernel, even though we may not have all parameters
// in the context to improve compile time.
Kernel->space = isl_space_align_params(Kernel->space, ParamSpace.release());
assert(Kernel->tree && "Device AST of kernel node is empty");
Instruction &HostInsertPoint = *Builder.GetInsertPoint();
IslExprBuilder::IDToValueTy HostIDs = IDToValue;
ValueMapT HostValueMap = ValueMap;
BlockGenerator::AllocaMapTy HostScalarMap = ScalarMap;
ScalarMap.clear();
BlockGenerator::EscapeUsersAllocaMapTy HostEscapeMap = EscapeMap;
EscapeMap.clear();
// Create for all loops we depend on values that contain the current loop
// iteration. These values are necessary to generate code for SCEVs that
// depend on such loops. As a result we need to pass them to the subfunction.
for (const Loop *L : Loops) {
const SCEV *OuterLIV = SE.getAddRecExpr(SE.getUnknown(Builder.getInt64(0)),
SE.getUnknown(Builder.getInt64(1)),
L, SCEV::FlagAnyWrap);
Value *V = generateSCEV(OuterLIV);
OutsideLoopIterations[L] = SE.getUnknown(V);
SubtreeValues.insert(V);
}
createKernelFunction(Kernel, SubtreeValues, SubtreeFunctions);
setupKernelSubtreeFunctions(SubtreeFunctions);
create(isl_ast_node_copy(Kernel->tree));
finalizeKernelArguments(Kernel);
Function *F = Builder.GetInsertBlock()->getParent();
if (Arch == GPUArch::NVPTX64)
addCUDAAnnotations(F->getParent(), BlockDimX, BlockDimY, BlockDimZ);
clearDominators(F);
clearScalarEvolution(F);
clearLoops(F);
IDToValue = HostIDs;
ValueMap = std::move(HostValueMap);
ScalarMap = std::move(HostScalarMap);
EscapeMap = std::move(HostEscapeMap);
IDToSAI.clear();
Annotator.resetAlternativeAliasBases();
for (auto &BasePtr : LocalArrays)
S.invalidateScopArrayInfo(BasePtr, MemoryKind::Array);
LocalArrays.clear();
std::string ASMString = finalizeKernelFunction();
Builder.SetInsertPoint(&HostInsertPoint);
Value *Parameters = createLaunchParameters(Kernel, F, SubtreeValues);
std::string Name = getKernelFuncName(Kernel->id);
Value *KernelString = Builder.CreateGlobalStringPtr(ASMString, Name);
Value *NameString = Builder.CreateGlobalStringPtr(Name, Name + "_name");
Value *GPUKernel = createCallGetKernel(KernelString, NameString);
Value *GridDimX, *GridDimY;
std::tie(GridDimX, GridDimY) = getGridSizes(Kernel);
createCallLaunchKernel(GPUKernel, GridDimX, GridDimY, BlockDimX, BlockDimY,
BlockDimZ, Parameters);
createCallFreeKernel(GPUKernel);
for (auto Id : KernelIds)
isl_id_free(Id);
KernelIds.clear();
}
/// Compute the DataLayout string for the NVPTX backend.
///
/// @param is64Bit Are we looking for a 64 bit architecture?
static std::string computeNVPTXDataLayout(bool is64Bit) {
std::string Ret = "";
if (!is64Bit) {
Ret += "e-p:32:32:32-i1:8:8-i8:8:8-i16:16:16-i32:32:32-i64:64:"
"64-i128:128:128-f32:32:32-f64:64:64-v16:16:16-v32:32:32-v64:64:"
"64-v128:128:128-n16:32:64";
} else {
Ret += "e-p:64:64:64-i1:8:8-i8:8:8-i16:16:16-i32:32:32-i64:64:"
"64-i128:128:128-f32:32:32-f64:64:64-v16:16:16-v32:32:32-v64:64:"
"64-v128:128:128-n16:32:64";
}
return Ret;
}
/// Compute the DataLayout string for a SPIR kernel.
///
/// @param is64Bit Are we looking for a 64 bit architecture?
static std::string computeSPIRDataLayout(bool is64Bit) {
std::string Ret = "";
if (!is64Bit) {
Ret += "e-p:32:32:32-i1:8:8-i8:8:8-i16:16:16-i32:32:32-i64:64:"
"64-i128:128:128-f32:32:32-f64:64:64-v16:16:16-v24:32:32-v32:32:"
"32-v48:64:64-v64:64:64-v96:128:128-v128:128:128-v192:"
"256:256-v256:256:256-v512:512:512-v1024:1024:1024";
} else {
Ret += "e-p:64:64:64-i1:8:8-i8:8:8-i16:16:16-i32:32:32-i64:64:"
"64-i128:128:128-f32:32:32-f64:64:64-v16:16:16-v24:32:32-v32:32:"
"32-v48:64:64-v64:64:64-v96:128:128-v128:128:128-v192:"
"256:256-v256:256:256-v512:512:512-v1024:1024:1024";
}
return Ret;
}
Function *
GPUNodeBuilder::createKernelFunctionDecl(ppcg_kernel *Kernel,
SetVector<Value *> &SubtreeValues) {
std::vector<Type *> Args;
std::string Identifier = getKernelFuncName(Kernel->id);
std::vector<Metadata *> MemoryType;
for (long i = 0; i < Prog->n_array; i++) {
if (!ppcg_kernel_requires_array_argument(Kernel, i))
continue;
if (gpu_array_is_read_only_scalar(&Prog->array[i])) {
isl_id *Id = isl_space_get_tuple_id(Prog->array[i].space, isl_dim_set);
const ScopArrayInfo *SAI = ScopArrayInfo::getFromId(isl::manage(Id));
Args.push_back(SAI->getElementType());
MemoryType.push_back(
ConstantAsMetadata::get(ConstantInt::get(Builder.getInt32Ty(), 0)));
} else {
static const int UseGlobalMemory = 1;
Args.push_back(Builder.getInt8PtrTy(UseGlobalMemory));
MemoryType.push_back(
ConstantAsMetadata::get(ConstantInt::get(Builder.getInt32Ty(), 1)));
}
}
int NumHostIters = isl_space_dim(Kernel->space, isl_dim_set);
for (long i = 0; i < NumHostIters; i++) {
Args.push_back(Builder.getInt64Ty());
MemoryType.push_back(
ConstantAsMetadata::get(ConstantInt::get(Builder.getInt32Ty(), 0)));
}
int NumVars = isl_space_dim(Kernel->space, isl_dim_param);
for (long i = 0; i < NumVars; i++) {
isl_id *Id = isl_space_get_dim_id(Kernel->space, isl_dim_param, i);
Value *Val = IDToValue[Id];
isl_id_free(Id);
Args.push_back(Val->getType());
MemoryType.push_back(
ConstantAsMetadata::get(ConstantInt::get(Builder.getInt32Ty(), 0)));
}
for (auto *V : SubtreeValues) {
Args.push_back(V->getType());
MemoryType.push_back(
ConstantAsMetadata::get(ConstantInt::get(Builder.getInt32Ty(), 0)));
}
auto *FT = FunctionType::get(Builder.getVoidTy(), Args, false);
auto *FN = Function::Create(FT, Function::ExternalLinkage, Identifier,
GPUModule.get());
std::vector<Metadata *> EmptyStrings;
for (unsigned int i = 0; i < MemoryType.size(); i++) {
EmptyStrings.push_back(MDString::get(FN->getContext(), ""));
}
if (Arch == GPUArch::SPIR32 || Arch == GPUArch::SPIR64) {
FN->setMetadata("kernel_arg_addr_space",
MDNode::get(FN->getContext(), MemoryType));
FN->setMetadata("kernel_arg_name",
MDNode::get(FN->getContext(), EmptyStrings));
FN->setMetadata("kernel_arg_access_qual",
MDNode::get(FN->getContext(), EmptyStrings));
FN->setMetadata("kernel_arg_type",
MDNode::get(FN->getContext(), EmptyStrings));
FN->setMetadata("kernel_arg_type_qual",
MDNode::get(FN->getContext(), EmptyStrings));
FN->setMetadata("kernel_arg_base_type",
MDNode::get(FN->getContext(), EmptyStrings));
}
switch (Arch) {
case GPUArch::NVPTX64:
FN->setCallingConv(CallingConv::PTX_Kernel);
break;
case GPUArch::SPIR32:
case GPUArch::SPIR64:
FN->setCallingConv(CallingConv::SPIR_KERNEL);
break;
}
auto Arg = FN->arg_begin();
for (long i = 0; i < Kernel->n_array; i++) {
if (!ppcg_kernel_requires_array_argument(Kernel, i))
continue;
Arg->setName(Kernel->array[i].array->name);
isl_id *Id = isl_space_get_tuple_id(Prog->array[i].space, isl_dim_set);
const ScopArrayInfo *SAI = ScopArrayInfo::getFromId(isl::manage_copy(Id));
Type *EleTy = SAI->getElementType();
Value *Val = &*Arg;
SmallVector<const SCEV *, 4> Sizes;
isl_ast_build *Build =
isl_ast_build_from_context(isl_set_copy(Prog->context));
Sizes.push_back(nullptr);
for (long j = 1, n = Kernel->array[i].array->n_index; j < n; j++) {
isl_ast_expr *DimSize = isl_ast_build_expr_from_pw_aff(
Build, isl_multi_pw_aff_get_pw_aff(Kernel->array[i].array->bound, j));
auto V = ExprBuilder.create(DimSize);
Sizes.push_back(SE.getSCEV(V));
}
const ScopArrayInfo *SAIRep =
S.getOrCreateScopArrayInfo(Val, EleTy, Sizes, MemoryKind::Array);
LocalArrays.push_back(Val);
isl_ast_build_free(Build);
KernelIds.push_back(Id);
IDToSAI[Id] = SAIRep;
Arg++;
}
for (long i = 0; i < NumHostIters; i++) {
isl_id *Id = isl_space_get_dim_id(Kernel->space, isl_dim_set, i);
Arg->setName(isl_id_get_name(Id));
IDToValue[Id] = &*Arg;
KernelIDs.insert(std::unique_ptr<isl_id, IslIdDeleter>(Id));
Arg++;
}
for (long i = 0; i < NumVars; i++) {
isl_id *Id = isl_space_get_dim_id(Kernel->space, isl_dim_param, i);
Arg->setName(isl_id_get_name(Id));
Value *Val = IDToValue[Id];
ValueMap[Val] = &*Arg;
IDToValue[Id] = &*Arg;
KernelIDs.insert(std::unique_ptr<isl_id, IslIdDeleter>(Id));
Arg++;
}
for (auto *V : SubtreeValues) {
Arg->setName(V->getName());
ValueMap[V] = &*Arg;
Arg++;
}
return FN;
}
void GPUNodeBuilder::insertKernelIntrinsics(ppcg_kernel *Kernel) {
Intrinsic::ID IntrinsicsBID[2];
Intrinsic::ID IntrinsicsTID[3];
switch (Arch) {
case GPUArch::SPIR64:
case GPUArch::SPIR32:
llvm_unreachable("Cannot generate NVVM intrinsics for SPIR");
case GPUArch::NVPTX64:
IntrinsicsBID[0] = Intrinsic::nvvm_read_ptx_sreg_ctaid_x;
IntrinsicsBID[1] = Intrinsic::nvvm_read_ptx_sreg_ctaid_y;
IntrinsicsTID[0] = Intrinsic::nvvm_read_ptx_sreg_tid_x;
IntrinsicsTID[1] = Intrinsic::nvvm_read_ptx_sreg_tid_y;
IntrinsicsTID[2] = Intrinsic::nvvm_read_ptx_sreg_tid_z;
break;
}
auto addId = [this](__isl_take isl_id *Id, Intrinsic::ID Intr) mutable {
std::string Name = isl_id_get_name(Id);
Module *M = Builder.GetInsertBlock()->getParent()->getParent();
Function *IntrinsicFn = Intrinsic::getDeclaration(M, Intr);
Value *Val = Builder.CreateCall(IntrinsicFn, {});
Val = Builder.CreateIntCast(Val, Builder.getInt64Ty(), false, Name);
IDToValue[Id] = Val;
KernelIDs.insert(std::unique_ptr<isl_id, IslIdDeleter>(Id));
};
for (int i = 0; i < Kernel->n_grid; ++i) {
isl_id *Id = isl_id_list_get_id(Kernel->block_ids, i);
addId(Id, IntrinsicsBID[i]);
}
for (int i = 0; i < Kernel->n_block; ++i) {
isl_id *Id = isl_id_list_get_id(Kernel->thread_ids, i);
addId(Id, IntrinsicsTID[i]);
}
}
void GPUNodeBuilder::insertKernelCallsSPIR(ppcg_kernel *Kernel,
bool SizeTypeIs64bit) {
const char *GroupName[3] = {"__gen_ocl_get_group_id0",
"__gen_ocl_get_group_id1",
"__gen_ocl_get_group_id2"};
const char *LocalName[3] = {"__gen_ocl_get_local_id0",
"__gen_ocl_get_local_id1",
"__gen_ocl_get_local_id2"};
IntegerType *SizeT =
SizeTypeIs64bit ? Builder.getInt64Ty() : Builder.getInt32Ty();
auto createFunc = [this](const char *Name, __isl_take isl_id *Id,
IntegerType *SizeT) mutable {
Module *M = Builder.GetInsertBlock()->getParent()->getParent();
Function *FN = M->getFunction(Name);
// If FN is not available, declare it.
if (!FN) {
GlobalValue::LinkageTypes Linkage = Function::ExternalLinkage;
std::vector<Type *> Args;
FunctionType *Ty = FunctionType::get(SizeT, Args, false);
FN = Function::Create(Ty, Linkage, Name, M);
FN->setCallingConv(CallingConv::SPIR_FUNC);
}
Value *Val = Builder.CreateCall(FN, {});
if (SizeT == Builder.getInt32Ty())
Val = Builder.CreateIntCast(Val, Builder.getInt64Ty(), false, Name);
IDToValue[Id] = Val;
KernelIDs.insert(std::unique_ptr<isl_id, IslIdDeleter>(Id));
};
for (int i = 0; i < Kernel->n_grid; ++i)
createFunc(GroupName[i], isl_id_list_get_id(Kernel->block_ids, i), SizeT);
for (int i = 0; i < Kernel->n_block; ++i)
createFunc(LocalName[i], isl_id_list_get_id(Kernel->thread_ids, i), SizeT);
}
void GPUNodeBuilder::prepareKernelArguments(ppcg_kernel *Kernel, Function *FN) {
auto Arg = FN->arg_begin();
for (long i = 0; i < Kernel->n_array; i++) {
if (!ppcg_kernel_requires_array_argument(Kernel, i))
continue;
isl_id *Id = isl_space_get_tuple_id(Prog->array[i].space, isl_dim_set);
const ScopArrayInfo *SAI = ScopArrayInfo::getFromId(isl::manage_copy(Id));
isl_id_free(Id);
if (SAI->getNumberOfDimensions() > 0) {
Arg++;
continue;
}
Value *Val = &*Arg;
if (!gpu_array_is_read_only_scalar(&Prog->array[i])) {
Type *TypePtr = SAI->getElementType()->getPointerTo();
Value *TypedArgPtr = Builder.CreatePointerCast(Val, TypePtr);
Val = Builder.CreateLoad(SAI->getElementType(), TypedArgPtr);
}
Value *Alloca = BlockGen.getOrCreateAlloca(SAI);
Builder.CreateStore(Val, Alloca);
Arg++;
}
}
void GPUNodeBuilder::finalizeKernelArguments(ppcg_kernel *Kernel) {
auto *FN = Builder.GetInsertBlock()->getParent();
auto Arg = FN->arg_begin();
bool StoredScalar = false;
for (long i = 0; i < Kernel->n_array; i++) {
if (!ppcg_kernel_requires_array_argument(Kernel, i))
continue;
isl_id *Id = isl_space_get_tuple_id(Prog->array[i].space, isl_dim_set);
const ScopArrayInfo *SAI = ScopArrayInfo::getFromId(isl::manage_copy(Id));
isl_id_free(Id);
if (SAI->getNumberOfDimensions() > 0) {
Arg++;
continue;
}
if (gpu_array_is_read_only_scalar(&Prog->array[i])) {
Arg++;
continue;
}
Value *Alloca = BlockGen.getOrCreateAlloca(SAI);
Value *ArgPtr = &*Arg;
Type *TypePtr = SAI->getElementType()->getPointerTo();
Value *TypedArgPtr = Builder.CreatePointerCast(ArgPtr, TypePtr);
Value *Val = Builder.CreateLoad(SAI->getElementType(), Alloca);
Builder.CreateStore(Val, TypedArgPtr);
StoredScalar = true;
Arg++;
}
if (StoredScalar) {
/// In case more than one thread contains scalar stores, the generated
/// code might be incorrect, if we only store at the end of the kernel.
/// To support this case we need to store these scalars back at each
/// memory store or at least before each kernel barrier.
if (Kernel->n_block != 0 || Kernel->n_grid != 0) {
BuildSuccessful = 0;
LLVM_DEBUG(
dbgs() << getUniqueScopName(&S)
<< " has a store to a scalar value that"
" would be undefined to run in parallel. Bailing out.\n";);
}
}
}
void GPUNodeBuilder::createKernelVariables(ppcg_kernel *Kernel, Function *FN) {
Module *M = Builder.GetInsertBlock()->getParent()->getParent();
for (int i = 0; i < Kernel->n_var; ++i) {
struct ppcg_kernel_var &Var = Kernel->var[i];
isl_id *Id = isl_space_get_tuple_id(Var.array->space, isl_dim_set);
Type *EleTy = ScopArrayInfo::getFromId(isl::manage(Id))->getElementType();
Type *ArrayTy = EleTy;
SmallVector<const SCEV *, 4> Sizes;
Sizes.push_back(nullptr);
for (unsigned int j = 1; j < Var.array->n_index; ++j) {
isl_val *Val = isl_vec_get_element_val(Var.size, j);
long Bound = isl_val_get_num_si(Val);
isl_val_free(Val);
Sizes.push_back(S.getSE()->getConstant(Builder.getInt64Ty(), Bound));
}
for (int j = Var.array->n_index - 1; j >= 0; --j) {
isl_val *Val = isl_vec_get_element_val(Var.size, j);
long Bound = isl_val_get_num_si(Val);
isl_val_free(Val);
ArrayTy = ArrayType::get(ArrayTy, Bound);
}
const ScopArrayInfo *SAI;
Value *Allocation;
if (Var.type == ppcg_access_shared) {
auto GlobalVar = new GlobalVariable(
*M, ArrayTy, false, GlobalValue::InternalLinkage, 0, Var.name,
nullptr, GlobalValue::ThreadLocalMode::NotThreadLocal, 3);
GlobalVar->setAlignment(llvm::Align(EleTy->getPrimitiveSizeInBits() / 8));
GlobalVar->setInitializer(Constant::getNullValue(ArrayTy));
Allocation = GlobalVar;
} else if (Var.type == ppcg_access_private) {
Allocation = Builder.CreateAlloca(ArrayTy, 0, "private_array");
} else {
llvm_unreachable("unknown variable type");
}
SAI =
S.getOrCreateScopArrayInfo(Allocation, EleTy, Sizes, MemoryKind::Array);
Id = isl_id_alloc(S.getIslCtx().get(), Var.name, nullptr);
IDToValue[Id] = Allocation;
LocalArrays.push_back(Allocation);
KernelIds.push_back(Id);
IDToSAI[Id] = SAI;
}
}
void GPUNodeBuilder::createKernelFunction(
ppcg_kernel *Kernel, SetVector<Value *> &SubtreeValues,
SetVector<Function *> &SubtreeFunctions) {
std::string Identifier = getKernelFuncName(Kernel->id);
GPUModule.reset(new Module(Identifier, Builder.getContext()));
switch (Arch) {
case GPUArch::NVPTX64:
if (Runtime == GPURuntime::CUDA)
GPUModule->setTargetTriple(Triple::normalize("nvptx64-nvidia-cuda"));
else if (Runtime == GPURuntime::OpenCL)
GPUModule->setTargetTriple(Triple::normalize("nvptx64-nvidia-nvcl"));
GPUModule->setDataLayout(computeNVPTXDataLayout(true /* is64Bit */));
break;
case GPUArch::SPIR32:
GPUModule->setTargetTriple(Triple::normalize("spir-unknown-unknown"));
GPUModule->setDataLayout(computeSPIRDataLayout(false /* is64Bit */));
break;
case GPUArch::SPIR64:
GPUModule->setTargetTriple(Triple::normalize("spir64-unknown-unknown"));
GPUModule->setDataLayout(computeSPIRDataLayout(true /* is64Bit */));
break;
}
Function *FN = createKernelFunctionDecl(Kernel, SubtreeValues);
BasicBlock *PrevBlock = Builder.GetInsertBlock();
auto EntryBlock = BasicBlock::Create(Builder.getContext(), "entry", FN);
DT.addNewBlock(EntryBlock, PrevBlock);
Builder.SetInsertPoint(EntryBlock);
Builder.CreateRetVoid();
Builder.SetInsertPoint(EntryBlock, EntryBlock->begin());
ScopDetection::markFunctionAsInvalid(FN);
prepareKernelArguments(Kernel, FN);
createKernelVariables(Kernel, FN);
switch (Arch) {
case GPUArch::NVPTX64:
insertKernelIntrinsics(Kernel);
break;
case GPUArch::SPIR32:
insertKernelCallsSPIR(Kernel, false);
break;
case GPUArch::SPIR64:
insertKernelCallsSPIR(Kernel, true);
break;
}
}
std::string GPUNodeBuilder::createKernelASM() {
llvm::Triple GPUTriple;
switch (Arch) {
case GPUArch::NVPTX64:
switch (Runtime) {
case GPURuntime::CUDA:
GPUTriple = llvm::Triple(Triple::normalize("nvptx64-nvidia-cuda"));
break;
case GPURuntime::OpenCL:
GPUTriple = llvm::Triple(Triple::normalize("nvptx64-nvidia-nvcl"));
break;
}
break;
case GPUArch::SPIR64:
case GPUArch::SPIR32:
std::string SPIRAssembly;
raw_string_ostream IROstream(SPIRAssembly);
IROstream << *GPUModule;
IROstream.flush();
return SPIRAssembly;
}
std::string ErrMsg;
auto GPUTarget = TargetRegistry::lookupTarget(GPUTriple.getTriple(), ErrMsg);
if (!GPUTarget) {
errs() << ErrMsg << "\n";
return "";
}
TargetOptions Options;
Options.UnsafeFPMath = FastMath;
std::string subtarget;
switch (Arch) {
case GPUArch::NVPTX64:
subtarget = CudaVersion;
break;
case GPUArch::SPIR32:
case GPUArch::SPIR64:
llvm_unreachable("No subtarget for SPIR architecture");
}
std::unique_ptr<TargetMachine> TargetM(GPUTarget->createTargetMachine(
GPUTriple.getTriple(), subtarget, "", Options, Optional<Reloc::Model>()));
SmallString<0> ASMString;
raw_svector_ostream ASMStream(ASMString);
llvm::legacy::PassManager PM;
PM.add(createTargetTransformInfoWrapperPass(TargetM->getTargetIRAnalysis()));
if (TargetM->addPassesToEmitFile(PM, ASMStream, nullptr, CGFT_AssemblyFile,
true /* verify */)) {
errs() << "The target does not support generation of this file type!\n";
return "";
}
PM.run(*GPUModule);
return ASMStream.str().str();
}
bool GPUNodeBuilder::requiresCUDALibDevice() {
bool RequiresLibDevice = false;
for (Function &F : GPUModule->functions()) {
if (!F.isDeclaration())
continue;
const std::string CUDALibDeviceFunc = getCUDALibDeviceFuntion(F.getName());
if (CUDALibDeviceFunc.length() != 0) {
// We need to handle the case where a module looks like this:
// @expf(..)
// @llvm.exp.f64(..)
// Both of these functions would be renamed to `__nv_expf`.
//
// So, we must first check for the existence of the libdevice function.
// If this exists, we replace our current function with it.
//
// If it does not exist, we rename the current function to the
// libdevice functiono name.
if (Function *Replacement = F.getParent()->getFunction(CUDALibDeviceFunc))
F.replaceAllUsesWith(Replacement);
else
F.setName(CUDALibDeviceFunc);
RequiresLibDevice = true;
}
}
return RequiresLibDevice;
}
void GPUNodeBuilder::addCUDALibDevice() {
if (Arch != GPUArch::NVPTX64)
return;
if (requiresCUDALibDevice()) {
SMDiagnostic Error;
errs() << CUDALibDevice << "\n";
auto LibDeviceModule =
parseIRFile(CUDALibDevice, Error, GPUModule->getContext());
if (!LibDeviceModule) {
BuildSuccessful = false;
report_fatal_error("Could not find or load libdevice. Skipping GPU "
"kernel generation. Please set -polly-acc-libdevice "
"accordingly.\n");
return;
}
Linker L(*GPUModule);
// Set an nvptx64 target triple to avoid linker warnings. The original
// triple of the libdevice files are nvptx-unknown-unknown.
LibDeviceModule->setTargetTriple(Triple::normalize("nvptx64-nvidia-cuda"));
L.linkInModule(std::move(LibDeviceModule), Linker::LinkOnlyNeeded);
}
}
std::string GPUNodeBuilder::finalizeKernelFunction() {
if (verifyModule(*GPUModule)) {
LLVM_DEBUG(dbgs() << "verifyModule failed on module:\n";
GPUModule->print(dbgs(), nullptr); dbgs() << "\n";);
LLVM_DEBUG(dbgs() << "verifyModule Error:\n";
verifyModule(*GPUModule, &dbgs()););
if (FailOnVerifyModuleFailure)
llvm_unreachable("VerifyModule failed.");
BuildSuccessful = false;
return "";
}
addCUDALibDevice();
if (DumpKernelIR)
outs() << *GPUModule << "\n";
if (Arch != GPUArch::SPIR32 && Arch != GPUArch::SPIR64) {
// Optimize module.
llvm::legacy::PassManager OptPasses;
PassManagerBuilder PassBuilder;
PassBuilder.OptLevel = 3;
PassBuilder.SizeLevel = 0;
PassBuilder.populateModulePassManager(OptPasses);
OptPasses.run(*GPUModule);
}
std::string Assembly = createKernelASM();
if (DumpKernelASM)
outs() << Assembly << "\n";
GPUModule.release();
KernelIDs.clear();
return Assembly;
}
/// Construct an `isl_pw_aff_list` from a vector of `isl_pw_aff`
/// @param PwAffs The list of piecewise affine functions to create an
/// `isl_pw_aff_list` from. We expect an rvalue ref because
/// all the isl_pw_aff are used up by this function.
///
/// @returns The `isl_pw_aff_list`.
__isl_give isl_pw_aff_list *
createPwAffList(isl_ctx *Context,
const std::vector<__isl_take isl_pw_aff *> &&PwAffs) {
isl_pw_aff_list *List = isl_pw_aff_list_alloc(Context, PwAffs.size());
for (unsigned i = 0; i < PwAffs.size(); i++) {
List = isl_pw_aff_list_insert(List, i, PwAffs[i]);
}
return List;
}
/// Align all the `PwAffs` such that they have the same parameter dimensions.
///
/// We loop over all `pw_aff` and align all of their spaces together to
/// create a common space for all the `pw_aff`. This common space is the
/// `AlignSpace`. We then align all the `pw_aff` to this space. We start
/// with the given `SeedSpace`.
/// @param PwAffs The list of piecewise affine functions we want to align.
/// This is an rvalue reference because the entire vector is
/// used up by the end of the operation.
/// @param SeedSpace The space to start the alignment process with.
/// @returns A std::pair, whose first element is the aligned space,
/// whose second element is the vector of aligned piecewise
/// affines.
static std::pair<__isl_give isl_space *, std::vector<__isl_give isl_pw_aff *>>
alignPwAffs(const std::vector<__isl_take isl_pw_aff *> &&PwAffs,
__isl_take isl_space *SeedSpace) {
assert(SeedSpace && "Invalid seed space given.");
isl_space *AlignSpace = SeedSpace;
for (isl_pw_aff *PwAff : PwAffs) {
isl_space *PwAffSpace = isl_pw_aff_get_domain_space(PwAff);
AlignSpace = isl_space_align_params(AlignSpace, PwAffSpace);
}
std::vector<isl_pw_aff *> AdjustedPwAffs;
for (unsigned i = 0; i < PwAffs.size(); i++) {
isl_pw_aff *Adjusted = PwAffs[i];
assert(Adjusted && "Invalid pw_aff given.");
Adjusted = isl_pw_aff_align_params(Adjusted, isl_space_copy(AlignSpace));
AdjustedPwAffs.push_back(Adjusted);
}
return std::make_pair(AlignSpace, AdjustedPwAffs);
}
namespace {
class PPCGCodeGeneration : public ScopPass {
public:
static char ID;
GPURuntime Runtime = GPURuntime::CUDA;
GPUArch Architecture = GPUArch::NVPTX64;
/// The scop that is currently processed.
Scop *S;
LoopInfo *LI;
DominatorTree *DT;
ScalarEvolution *SE;
const DataLayout *DL;
RegionInfo *RI;
PPCGCodeGeneration() : ScopPass(ID) {
// Apply defaults.
Runtime = GPURuntimeChoice;
Architecture = GPUArchChoice;
}
/// Construct compilation options for PPCG.
///
/// @returns The compilation options.
ppcg_options *createPPCGOptions() {
auto DebugOptions =
(ppcg_debug_options *)malloc(sizeof(ppcg_debug_options));
auto Options = (ppcg_options *)malloc(sizeof(ppcg_options));
DebugOptions->dump_schedule_constraints = false;
DebugOptions->dump_schedule = false;
DebugOptions->dump_final_schedule = false;
DebugOptions->dump_sizes = false;
DebugOptions->verbose = false;
Options->debug = DebugOptions;
Options->group_chains = false;
Options->reschedule = true;
Options->scale_tile_loops = false;
Options->wrap = false;
Options->non_negative_parameters = false;
Options->ctx = nullptr;
Options->sizes = nullptr;
Options->tile = true;
Options->tile_size = 32;
Options->isolate_full_tiles = false;
Options->use_private_memory = PrivateMemory;
Options->use_shared_memory = SharedMemory;
Options->max_shared_memory = 48 * 1024;
Options->target = PPCG_TARGET_CUDA;
Options->openmp = false;
Options->linearize_device_arrays = true;
Options->allow_gnu_extensions = false;
Options->unroll_copy_shared = false;
Options->unroll_gpu_tile = false;
Options->live_range_reordering = true;
Options->live_range_reordering = true;
Options->hybrid = false;
Options->opencl_compiler_options = nullptr;
Options->opencl_use_gpu = false;
Options->opencl_n_include_file = 0;
Options->opencl_include_files = nullptr;
Options->opencl_print_kernel_types = false;
Options->opencl_embed_kernel_code = false;
Options->save_schedule_file = nullptr;
Options->load_schedule_file = nullptr;
return Options;
}
/// Get a tagged access relation containing all accesses of type @p AccessTy.
///
/// Instead of a normal access of the form:
///
/// Stmt[i,j,k] -> Array[f_0(i,j,k), f_1(i,j,k)]
///
/// a tagged access has the form
///
/// [Stmt[i,j,k] -> id[]] -> Array[f_0(i,j,k), f_1(i,j,k)]
///
/// where 'id' is an additional space that references the memory access that
/// triggered the access.
///
/// @param AccessTy The type of the memory accesses to collect.
///
/// @return The relation describing all tagged memory accesses.
isl_union_map *getTaggedAccesses(enum MemoryAccess::AccessType AccessTy) {
isl_union_map *Accesses = isl_union_map_empty(S->getParamSpace().release());
for (auto &Stmt : *S)
for (auto &Acc : Stmt)
if (Acc->getType() == AccessTy) {
isl_map *Relation = Acc->getAccessRelation().release();
Relation =
isl_map_intersect_domain(Relation, Stmt.getDomain().release());
isl_space *Space = isl_map_get_space(Relation);
Space = isl_space_range(Space);
Space = isl_space_from_range(Space);
Space =
isl_space_set_tuple_id(Space, isl_dim_in, Acc->getId().release());
isl_map *Universe = isl_map_universe(Space);
Relation = isl_map_domain_product(Relation, Universe);
Accesses = isl_union_map_add_map(Accesses, Relation);
}
return Accesses;
}
/// Get the set of all read accesses, tagged with the access id.
///
/// @see getTaggedAccesses
isl_union_map *getTaggedReads() {
return getTaggedAccesses(MemoryAccess::READ);
}
/// Get the set of all may (and must) accesses, tagged with the access id.
///
/// @see getTaggedAccesses
isl_union_map *getTaggedMayWrites() {
return isl_union_map_union(getTaggedAccesses(MemoryAccess::MAY_WRITE),
getTaggedAccesses(MemoryAccess::MUST_WRITE));
}
/// Get the set of all must accesses, tagged with the access id.
///
/// @see getTaggedAccesses
isl_union_map *getTaggedMustWrites() {
return getTaggedAccesses(MemoryAccess::MUST_WRITE);
}
/// Collect parameter and array names as isl_ids.
///
/// To reason about the different parameters and arrays used, ppcg requires
/// a list of all isl_ids in use. As PPCG traditionally performs
/// source-to-source compilation each of these isl_ids is mapped to the
/// expression that represents it. As we do not have a corresponding
/// expression in Polly, we just map each id to a 'zero' expression to match
/// the data format that ppcg expects.
///
/// @returns Retun a map from collected ids to 'zero' ast expressions.
__isl_give isl_id_to_ast_expr *getNames() {
auto *Names = isl_id_to_ast_expr_alloc(
S->getIslCtx().get(),
S->getNumParams() + std::distance(S->array_begin(), S->array_end()));
auto *Zero = isl_ast_expr_from_val(isl_val_zero(S->getIslCtx().get()));
for (const SCEV *P : S->parameters()) {
isl_id *Id = S->getIdForParam(P).release();
Names = isl_id_to_ast_expr_set(Names, Id, isl_ast_expr_copy(Zero));
}
for (auto &Array : S->arrays()) {
auto Id = Array->getBasePtrId().release();
Names = isl_id_to_ast_expr_set(Names, Id, isl_ast_expr_copy(Zero));
}
isl_ast_expr_free(Zero);
return Names;
}
/// Create a new PPCG scop from the current scop.
///
/// The PPCG scop is initialized with data from the current polly::Scop. From
/// this initial data, the data-dependences in the PPCG scop are initialized.
/// We do not use Polly's dependence analysis for now, to ensure we match
/// the PPCG default behaviour more closely.
///
/// @returns A new ppcg scop.
ppcg_scop *createPPCGScop() {
MustKillsInfo KillsInfo = computeMustKillsInfo(*S);
auto PPCGScop = (ppcg_scop *)malloc(sizeof(ppcg_scop));
PPCGScop->options = createPPCGOptions();
// enable live range reordering
PPCGScop->options->live_range_reordering = 1;
PPCGScop->start = 0;
PPCGScop->end = 0;
PPCGScop->context = S->getContext().release();
PPCGScop->domain = S->getDomains().release();
// TODO: investigate this further. PPCG calls collect_call_domains.
PPCGScop->call = isl_union_set_from_set(S->getContext().release());
PPCGScop->tagged_reads = getTaggedReads();
PPCGScop->reads = S->getReads().release();
PPCGScop->live_in = nullptr;
PPCGScop->tagged_may_writes = getTaggedMayWrites();
PPCGScop->may_writes = S->getWrites().release();
PPCGScop->tagged_must_writes = getTaggedMustWrites();
PPCGScop->must_writes = S->getMustWrites().release();
PPCGScop->live_out = nullptr;
PPCGScop->tagged_must_kills = KillsInfo.TaggedMustKills.release();
PPCGScop->must_kills = KillsInfo.MustKills.release();
PPCGScop->tagger = nullptr;
PPCGScop->independence =
isl_union_map_empty(isl_set_get_space(PPCGScop->context));
PPCGScop->dep_flow = nullptr;
PPCGScop->tagged_dep_flow = nullptr;
PPCGScop->dep_false = nullptr;
PPCGScop->dep_forced = nullptr;
PPCGScop->dep_order = nullptr;
PPCGScop->tagged_dep_order = nullptr;
PPCGScop->schedule = S->getScheduleTree().release();
// If we have something non-trivial to kill, add it to the schedule
if (KillsInfo.KillsSchedule.get())
PPCGScop->schedule = isl_schedule_sequence(
PPCGScop->schedule, KillsInfo.KillsSchedule.release());
PPCGScop->names = getNames();
PPCGScop->pet = nullptr;
compute_tagger(PPCGScop);
compute_dependences(PPCGScop);
eliminate_dead_code(PPCGScop);
return PPCGScop;
}
/// Collect the array accesses in a statement.
///
/// @param Stmt The statement for which to collect the accesses.
///
/// @returns A list of array accesses.
gpu_stmt_access *getStmtAccesses(ScopStmt &Stmt) {
gpu_stmt_access *Accesses = nullptr;
for (MemoryAccess *Acc : Stmt) {
auto Access =
isl_alloc_type(S->getIslCtx().get(), struct gpu_stmt_access);
Access->read = Acc->isRead();
Access->write = Acc->isWrite();
Access->access = Acc->getAccessRelation().release();
isl_space *Space = isl_map_get_space(Access->access);
Space = isl_space_range(Space);
Space = isl_space_from_range(Space);
Space = isl_space_set_tuple_id(Space, isl_dim_in, Acc->getId().release());
isl_map *Universe = isl_map_universe(Space);
Access->tagged_access =
isl_map_domain_product(Acc->getAccessRelation().release(), Universe);
Access->exact_write = !Acc->isMayWrite();
Access->ref_id = Acc->getId().release();
Access->next = Accesses;
Access->n_index = Acc->getScopArrayInfo()->getNumberOfDimensions();
// TODO: Also mark one-element accesses to arrays as fixed-element.
Access->fixed_element =
Acc->isLatestScalarKind() ? isl_bool_true : isl_bool_false;
Accesses = Access;
}
return Accesses;
}
/// Collect the list of GPU statements.
///
/// Each statement has an id, a pointer to the underlying data structure,
/// as well as a list with all memory accesses.
///
/// TODO: Initialize the list of memory accesses.
///
/// @returns A linked-list of statements.
gpu_stmt *getStatements() {
gpu_stmt *Stmts = isl_calloc_array(S->getIslCtx().get(), struct gpu_stmt,
std::distance(S->begin(), S->end()));
int i = 0;
for (auto &Stmt : *S) {
gpu_stmt *GPUStmt = &Stmts[i];
GPUStmt->id = Stmt.getDomainId().release();
// We use the pet stmt pointer to keep track of the Polly statements.
GPUStmt->stmt = (pet_stmt *)&Stmt;
GPUStmt->accesses = getStmtAccesses(Stmt);
i++;
}
return Stmts;
}
/// Derive the extent of an array.
///
/// The extent of an array is the set of elements that are within the
/// accessed array. For the inner dimensions, the extent constraints are
/// 0 and the size of the corresponding array dimension. For the first
/// (outermost) dimension, the extent constraints are the minimal and maximal
/// subscript value for the first dimension.
///
/// @param Array The array to derive the extent for.
///
/// @returns An isl_set describing the extent of the array.
isl::set getExtent(ScopArrayInfo *Array) {
unsigned NumDims = Array->getNumberOfDimensions();
if (Array->getNumberOfDimensions() == 0)
return isl::set::universe(Array->getSpace());
isl::union_map Accesses = S->getAccesses(Array);
isl::union_set AccessUSet = Accesses.range();
AccessUSet = AccessUSet.coalesce();
AccessUSet = AccessUSet.detect_equalities();
AccessUSet = AccessUSet.coalesce();
if (AccessUSet.is_empty())
return isl::set::empty(Array->getSpace());
isl::set AccessSet = AccessUSet.extract_set(Array->getSpace());
isl::local_space LS = isl::local_space(Array->getSpace());
isl::pw_aff Val = isl::aff::var_on_domain(LS, isl::dim::set, 0);
isl::pw_aff OuterMin = AccessSet.dim_min(0);
isl::pw_aff OuterMax = AccessSet.dim_max(0);
OuterMin = OuterMin.add_dims(isl::dim::in, Val.dim(isl::dim::in).release());
OuterMax = OuterMax.add_dims(isl::dim::in, Val.dim(isl::dim::in).release());
OuterMin = OuterMin.set_tuple_id(isl::dim::in, Array->getBasePtrId());
OuterMax = OuterMax.set_tuple_id(isl::dim::in, Array->getBasePtrId());
isl::set Extent = isl::set::universe(Array->getSpace());
Extent = Extent.intersect(OuterMin.le_set(Val));
Extent = Extent.intersect(OuterMax.ge_set(Val));
for (unsigned i = 1; i < NumDims; ++i)
Extent = Extent.lower_bound_si(isl::dim::set, i, 0);
for (unsigned i = 0; i < NumDims; ++i) {
isl::pw_aff PwAff = Array->getDimensionSizePw(i);
// isl_pw_aff can be NULL for zero dimension. Only in the case of a
// Fortran array will we have a legitimate dimension.
if (PwAff.is_null()) {
assert(i == 0 && "invalid dimension isl_pw_aff for nonzero dimension");
continue;
}
isl::pw_aff Val = isl::aff::var_on_domain(
isl::local_space(Array->getSpace()), isl::dim::set, i);
PwAff = PwAff.add_dims(isl::dim::in, Val.dim(isl::dim::in).release());
PwAff = PwAff.set_tuple_id(isl::dim::in, Val.get_tuple_id(isl::dim::in));
isl::set Set = PwAff.gt_set(Val);
Extent = Set.intersect(Extent);
}
return Extent;
}
/// Derive the bounds of an array.
///
/// For the first dimension we derive the bound of the array from the extent
/// of this dimension. For inner dimensions we obtain their size directly from
/// ScopArrayInfo.
///
/// @param PPCGArray The array to compute bounds for.
/// @param Array The polly array from which to take the information.
void setArrayBounds(gpu_array_info &PPCGArray, ScopArrayInfo *Array) {
std::vector<isl_pw_aff *> Bounds;
if (PPCGArray.n_index > 0) {
if (isl_set_is_empty(PPCGArray.extent)) {
isl_set *Dom = isl_set_copy(PPCGArray.extent);
isl_local_space *LS = isl_local_space_from_space(
isl_space_params(isl_set_get_space(Dom)));
isl_set_free(Dom);
isl_pw_aff *Zero = isl_pw_aff_from_aff(isl_aff_zero_on_domain(LS));
Bounds.push_back(Zero);
} else {
isl_set *Dom = isl_set_copy(PPCGArray.extent);
Dom = isl_set_project_out(Dom, isl_dim_set, 1, PPCGArray.n_index - 1);
isl_pw_aff *Bound = isl_set_dim_max(isl_set_copy(Dom), 0);
isl_set_free(Dom);
Dom = isl_pw_aff_domain(isl_pw_aff_copy(Bound));
isl_local_space *LS =
isl_local_space_from_space(isl_set_get_space(Dom));
isl_aff *One = isl_aff_zero_on_domain(LS);
One = isl_aff_add_constant_si(One, 1);
Bound = isl_pw_aff_add(Bound, isl_pw_aff_alloc(Dom, One));
Bound = isl_pw_aff_gist(Bound, S->getContext().release());
Bounds.push_back(Bound);
}
}
for (unsigned i = 1; i < PPCGArray.n_index; ++i) {
isl_pw_aff *Bound = Array->getDimensionSizePw(i).release();
auto LS = isl_pw_aff_get_domain_space(Bound);
auto Aff = isl_multi_aff_zero(LS);
// We need types to work out, which is why we perform this weird dance
// with `Aff` and `Bound`. Consider this example:
// LS: [p] -> { [] }
// Zero: [p] -> { [] } | Implicitly, is [p] -> { ~ -> [] }.
// This `~` is used to denote a "null space" (which is different from
// a *zero dimensional* space), which is something that ISL does not
// show you when pretty printing.
// Bound: [p] -> { [] -> [(10p)] } | Here, the [] is a *zero dimensional*
// space, not a "null space" which does not exist at all.
// When we pullback (precompose) `Bound` with `Zero`, we get:
// Bound . Zero =
// ([p] -> { [] -> [(10p)] }) . ([p] -> {~ -> [] }) =
// [p] -> { ~ -> [(10p)] } =
// [p] -> [(10p)] (as ISL pretty prints it)
// Bound Pullback: [p] -> { [(10p)] }
// We want this kind of an expression for Bound, without a
// zero dimensional input, but with a "null space" input for the types
// to work out later on, as far as I (Siddharth Bhat) understand.
// I was unable to find a reference to this in the ISL manual.
// References: Tobias Grosser.
Bound = isl_pw_aff_pullback_multi_aff(Bound, Aff);
Bounds.push_back(Bound);
}
/// To construct a `isl_multi_pw_aff`, we need all the indivisual `pw_aff`
/// to have the same parameter dimensions. So, we need to align them to an
/// appropriate space.
/// Scop::Context is _not_ an appropriate space, because when we have
/// `-polly-ignore-parameter-bounds` enabled, the Scop::Context does not
/// contain all parameter dimensions.
/// So, use the helper `alignPwAffs` to align all the `isl_pw_aff` together.
isl_space *SeedAlignSpace = S->getParamSpace().release();
SeedAlignSpace = isl_space_add_dims(SeedAlignSpace, isl_dim_set, 1);
isl_space *AlignSpace = nullptr;
std::vector<isl_pw_aff *> AlignedBounds;
std::tie(AlignSpace, AlignedBounds) =
alignPwAffs(std::move(Bounds), SeedAlignSpace);
assert(AlignSpace && "alignPwAffs did not initialise AlignSpace");
isl_pw_aff_list *BoundsList =
createPwAffList(S->getIslCtx().get(), std::move(AlignedBounds));
isl_space *BoundsSpace = isl_set_get_space(PPCGArray.extent);
BoundsSpace = isl_space_align_params(BoundsSpace, AlignSpace);
assert(BoundsSpace && "Unable to access space of array.");
assert(BoundsList && "Unable to access list of bounds.");
PPCGArray.bound =
isl_multi_pw_aff_from_pw_aff_list(BoundsSpace, BoundsList);
assert(PPCGArray.bound && "PPCGArray.bound was not constructed correctly.");
}
/// Create the arrays for @p PPCGProg.
///
/// @param PPCGProg The program to compute the arrays for.
void createArrays(gpu_prog *PPCGProg,
const SmallVector<ScopArrayInfo *, 4> &ValidSAIs) {
int i = 0;
for (auto &Array : ValidSAIs) {
std::string TypeName;
raw_string_ostream OS(TypeName);
OS << *Array->getElementType();
TypeName = OS.str();
gpu_array_info &PPCGArray = PPCGProg->array[i];
PPCGArray.space = Array->getSpace().release();
PPCGArray.type = strdup(TypeName.c_str());
PPCGArray.size = DL->getTypeAllocSize(Array->getElementType());
PPCGArray.name = strdup(Array->getName().c_str());
PPCGArray.extent = nullptr;
PPCGArray.n_index = Array->getNumberOfDimensions();
PPCGArray.extent = getExtent(Array).release();
PPCGArray.n_ref = 0;
PPCGArray.refs = nullptr;
PPCGArray.accessed = true;
PPCGArray.read_only_scalar =
Array->isReadOnly() && Array->getNumberOfDimensions() == 0;
PPCGArray.has_compound_element = false;
PPCGArray.local = false;
PPCGArray.declare_local = false;
PPCGArray.global = false;
PPCGArray.linearize = false;
PPCGArray.dep_order = nullptr;
PPCGArray.user = Array;
PPCGArray.bound = nullptr;
setArrayBounds(PPCGArray, Array);
i++;
collect_references(PPCGProg, &PPCGArray);
PPCGArray.only_fixed_element = only_fixed_element_accessed(&PPCGArray);
}
}
/// Create an identity map between the arrays in the scop.
///
/// @returns An identity map between the arrays in the scop.
isl_union_map *getArrayIdentity() {
isl_union_map *Maps = isl_union_map_empty(S->getParamSpace().release());
for (auto &Array : S->arrays()) {
isl_space *Space = Array->getSpace().release();
Space = isl_space_map_from_set(Space);
isl_map *Identity = isl_map_identity(Space);
Maps = isl_union_map_add_map(Maps, Identity);
}
return Maps;
}
/// Create a default-initialized PPCG GPU program.
///
/// @returns A new gpu program description.
gpu_prog *createPPCGProg(ppcg_scop *PPCGScop) {
if (!PPCGScop)
return nullptr;
auto PPCGProg = isl_calloc_type(S->getIslCtx().get(), struct gpu_prog);
PPCGProg->ctx = S->getIslCtx().get();
PPCGProg->scop = PPCGScop;
PPCGProg->context = isl_set_copy(PPCGScop->context);
PPCGProg->read = isl_union_map_copy(PPCGScop->reads);
PPCGProg->may_write = isl_union_map_copy(PPCGScop->may_writes);
PPCGProg->must_write = isl_union_map_copy(PPCGScop->must_writes);
PPCGProg->tagged_must_kill =
isl_union_map_copy(PPCGScop->tagged_must_kills);
PPCGProg->to_inner = getArrayIdentity();
PPCGProg->to_outer = getArrayIdentity();
// TODO: verify that this assignment is correct.
PPCGProg->any_to_outer = nullptr;
PPCGProg->n_stmts = std::distance(S->begin(), S->end());
PPCGProg->stmts = getStatements();
// Only consider arrays that have a non-empty extent.
// Otherwise, this will cause us to consider the following kinds of
// empty arrays:
// 1. Invariant loads that are represented by SAI objects.
// 2. Arrays with statically known zero size.
auto ValidSAIsRange =
make_filter_range(S->arrays(), [this](ScopArrayInfo *SAI) -> bool {
return !getExtent(SAI).is_empty();
});
SmallVector<ScopArrayInfo *, 4> ValidSAIs(ValidSAIsRange.begin(),
ValidSAIsRange.end());
PPCGProg->n_array =
ValidSAIs.size(); // std::distance(S->array_begin(), S->array_end());
PPCGProg->array = isl_calloc_array(
S->getIslCtx().get(), struct gpu_array_info, PPCGProg->n_array);
createArrays(PPCGProg, ValidSAIs);
PPCGProg->array_order = nullptr;
collect_order_dependences(PPCGProg);
PPCGProg->may_persist = compute_may_persist(PPCGProg);
return PPCGProg;
}
struct PrintGPUUserData {
struct cuda_info *CudaInfo;
struct gpu_prog *PPCGProg;
std::vector<ppcg_kernel *> Kernels;
};
/// Print a user statement node in the host code.
///
/// We use ppcg's printing facilities to print the actual statement and
/// additionally build up a list of all kernels that are encountered in the
/// host ast.
///
/// @param P The printer to print to
/// @param Options The printing options to use
/// @param Node The node to print
/// @param User A user pointer to carry additional data. This pointer is
/// expected to be of type PrintGPUUserData.
///
/// @returns A printer to which the output has been printed.
static __isl_give isl_printer *
printHostUser(__isl_take isl_printer *P,
__isl_take isl_ast_print_options *Options,
__isl_take isl_ast_node *Node, void *User) {
auto Data = (struct PrintGPUUserData *)User;
auto Id = isl_ast_node_get_annotation(Node);
if (Id) {
bool IsUser = !strcmp(isl_id_get_name(Id), "user");
// If this is a user statement, format it ourselves as ppcg would
// otherwise try to call pet functionality that is not available in
// Polly.
if (IsUser) {
P = isl_printer_start_line(P);
P = isl_printer_print_ast_node(P, Node);
P = isl_printer_end_line(P);
isl_id_free(Id);
isl_ast_print_options_free(Options);
return P;
}
auto Kernel = (struct ppcg_kernel *)isl_id_get_user(Id);
isl_id_free(Id);
Data->Kernels.push_back(Kernel);
}
return print_host_user(P, Options, Node, User);
}
/// Print C code corresponding to the control flow in @p Kernel.
///
/// @param Kernel The kernel to print
void printKernel(ppcg_kernel *Kernel) {
auto *P = isl_printer_to_str(S->getIslCtx().get());
P = isl_printer_set_output_format(P, ISL_FORMAT_C);
auto *Options = isl_ast_print_options_alloc(S->getIslCtx().get());
P = isl_ast_node_print(Kernel->tree, P, Options);
char *String = isl_printer_get_str(P);
outs() << String << "\n";
free(String);
isl_printer_free(P);
}
/// Print C code corresponding to the GPU code described by @p Tree.
///
/// @param Tree An AST describing GPU code
/// @param PPCGProg The PPCG program from which @Tree has been constructed.
void printGPUTree(isl_ast_node *Tree, gpu_prog *PPCGProg) {
auto *P = isl_printer_to_str(S->getIslCtx().get());
P = isl_printer_set_output_format(P, ISL_FORMAT_C);
PrintGPUUserData Data;
Data.PPCGProg = PPCGProg;
auto *Options = isl_ast_print_options_alloc(S->getIslCtx().get());
Options =
isl_ast_print_options_set_print_user(Options, printHostUser, &Data);
P = isl_ast_node_print(Tree, P, Options);
char *String = isl_printer_get_str(P);
outs() << "# host\n";
outs() << String << "\n";
free(String);
isl_printer_free(P);
for (auto Kernel : Data.Kernels) {
outs() << "# kernel" << Kernel->id << "\n";
printKernel(Kernel);
}
}
// Generate a GPU program using PPCG.
//
// GPU mapping consists of multiple steps:
//
// 1) Compute new schedule for the program.
// 2) Map schedule to GPU (TODO)
// 3) Generate code for new schedule (TODO)
//
// We do not use here the Polly ScheduleOptimizer, as the schedule optimizer
// is mostly CPU specific. Instead, we use PPCG's GPU code generation
// strategy directly from this pass.
gpu_gen *generateGPU(ppcg_scop *PPCGScop, gpu_prog *PPCGProg) {
auto PPCGGen = isl_calloc_type(S->getIslCtx().get(), struct gpu_gen);
PPCGGen->ctx = S->getIslCtx().get();
PPCGGen->options = PPCGScop->options;
PPCGGen->print = nullptr;
PPCGGen->print_user = nullptr;
PPCGGen->build_ast_expr = &pollyBuildAstExprForStmt;
PPCGGen->prog = PPCGProg;
PPCGGen->tree = nullptr;
PPCGGen->types.n = 0;
PPCGGen->types.name = nullptr;
PPCGGen->sizes = nullptr;
PPCGGen->used_sizes = nullptr;
PPCGGen->kernel_id = 0;
// Set scheduling strategy to same strategy PPCG is using.
isl_options_set_schedule_outer_coincidence(PPCGGen->ctx, true);
isl_options_set_schedule_maximize_band_depth(PPCGGen->ctx, true);
isl_options_set_schedule_whole_component(PPCGGen->ctx, false);
isl_schedule *Schedule = get_schedule(PPCGGen);
int has_permutable = has_any_permutable_node(Schedule);
Schedule =
isl_schedule_align_params(Schedule, S->getFullParamSpace().release());
if (!has_permutable || has_permutable < 0) {
Schedule = isl_schedule_free(Schedule);
LLVM_DEBUG(dbgs() << getUniqueScopName(S)
<< " does not have permutable bands. Bailing out\n";);
} else {
const bool CreateTransferToFromDevice = !PollyManagedMemory;
Schedule = map_to_device(PPCGGen, Schedule, CreateTransferToFromDevice);
PPCGGen->tree = generate_code(PPCGGen, isl_schedule_copy(Schedule));
}
if (DumpSchedule) {
isl_printer *P = isl_printer_to_str(S->getIslCtx().get());
P = isl_printer_set_yaml_style(P, ISL_YAML_STYLE_BLOCK);
P = isl_printer_print_str(P, "Schedule\n");
P = isl_printer_print_str(P, "========\n");
if (Schedule)
P = isl_printer_print_schedule(P, Schedule);
else
P = isl_printer_print_str(P, "No schedule found\n");
outs() << isl_printer_get_str(P) << "\n";
isl_printer_free(P);
}
if (DumpCode) {
outs() << "Code\n";
outs() << "====\n";
if (PPCGGen->tree)
printGPUTree(PPCGGen->tree, PPCGProg);
else
outs() << "No code generated\n";
}
isl_schedule_free(Schedule);
return PPCGGen;
}
/// Free gpu_gen structure.
///
/// @param PPCGGen The ppcg_gen object to free.
void freePPCGGen(gpu_gen *PPCGGen) {
isl_ast_node_free(PPCGGen->tree);
isl_union_map_free(PPCGGen->sizes);
isl_union_map_free(PPCGGen->used_sizes);
free(PPCGGen);
}
/// Free the options in the ppcg scop structure.
///
/// ppcg is not freeing these options for us. To avoid leaks we do this
/// ourselves.
///
/// @param PPCGScop The scop referencing the options to free.
void freeOptions(ppcg_scop *PPCGScop) {
free(PPCGScop->options->debug);
PPCGScop->options->debug = nullptr;
free(PPCGScop->options);
PPCGScop->options = nullptr;
}
/// Approximate the number of points in the set.
///
/// This function returns an ast expression that overapproximates the number
/// of points in an isl set through the rectangular hull surrounding this set.
///
/// @param Set The set to count.
/// @param Build The isl ast build object to use for creating the ast
/// expression.
///
/// @returns An approximation of the number of points in the set.
__isl_give isl_ast_expr *approxPointsInSet(__isl_take isl_set *Set,
__isl_keep isl_ast_build *Build) {
isl_val *One = isl_val_int_from_si(isl_set_get_ctx(Set), 1);
auto *Expr = isl_ast_expr_from_val(isl_val_copy(One));
isl_space *Space = isl_set_get_space(Set);
Space = isl_space_params(Space);
auto *Univ = isl_set_universe(Space);
isl_pw_aff *OneAff = isl_pw_aff_val_on_domain(Univ, One);
for (long i = 0, n = isl_set_dim(Set, isl_dim_set); i < n; i++) {
isl_pw_aff *Max = isl_set_dim_max(isl_set_copy(Set), i);
isl_pw_aff *Min = isl_set_dim_min(isl_set_copy(Set), i);
isl_pw_aff *DimSize = isl_pw_aff_sub(Max, Min);
DimSize = isl_pw_aff_add(DimSize, isl_pw_aff_copy(OneAff));
auto DimSizeExpr = isl_ast_build_expr_from_pw_aff(Build, DimSize);
Expr = isl_ast_expr_mul(Expr, DimSizeExpr);
}
isl_set_free(Set);
isl_pw_aff_free(OneAff);
return Expr;
}
/// Approximate a number of dynamic instructions executed by a given
/// statement.
///
/// @param Stmt The statement for which to compute the number of dynamic
/// instructions.
/// @param Build The isl ast build object to use for creating the ast
/// expression.
/// @returns An approximation of the number of dynamic instructions executed
/// by @p Stmt.
__isl_give isl_ast_expr *approxDynamicInst(ScopStmt &Stmt,
__isl_keep isl_ast_build *Build) {
auto Iterations = approxPointsInSet(Stmt.getDomain().release(), Build);
long InstCount = 0;
if (Stmt.isBlockStmt()) {
auto *BB = Stmt.getBasicBlock();
InstCount = std::distance(BB->begin(), BB->end());
} else {
auto *R = Stmt.getRegion();
for (auto *BB : R->blocks()) {
InstCount += std::distance(BB->begin(), BB->end());
}
}
isl_val *InstVal = isl_val_int_from_si(S->getIslCtx().get(), InstCount);
auto *InstExpr = isl_ast_expr_from_val(InstVal);
return isl_ast_expr_mul(InstExpr, Iterations);
}
/// Approximate dynamic instructions executed in scop.
///
/// @param S The scop for which to approximate dynamic instructions.
/// @param Build The isl ast build object to use for creating the ast
/// expression.
/// @returns An approximation of the number of dynamic instructions executed
/// in @p S.
__isl_give isl_ast_expr *
getNumberOfIterations(Scop &S, __isl_keep isl_ast_build *Build) {
isl_ast_expr *Instructions;
isl_val *Zero = isl_val_int_from_si(S.getIslCtx().get(), 0);
Instructions = isl_ast_expr_from_val(Zero);
for (ScopStmt &Stmt : S) {
isl_ast_expr *StmtInstructions = approxDynamicInst(Stmt, Build);
Instructions = isl_ast_expr_add(Instructions, StmtInstructions);
}
return Instructions;
}
/// Create a check that ensures sufficient compute in scop.
///
/// @param S The scop for which to ensure sufficient compute.
/// @param Build The isl ast build object to use for creating the ast
/// expression.
/// @returns An expression that evaluates to TRUE in case of sufficient
/// compute and to FALSE, otherwise.
__isl_give isl_ast_expr *
createSufficientComputeCheck(Scop &S, __isl_keep isl_ast_build *Build) {
auto Iterations = getNumberOfIterations(S, Build);
auto *MinComputeVal = isl_val_int_from_si(S.getIslCtx().get(), MinCompute);
auto *MinComputeExpr = isl_ast_expr_from_val(MinComputeVal);
return isl_ast_expr_ge(Iterations, MinComputeExpr);
}
/// Check if the basic block contains a function we cannot codegen for GPU
/// kernels.
///
/// If this basic block does something with a `Function` other than calling
/// a function that we support in a kernel, return true.
bool containsInvalidKernelFunctionInBlock(const BasicBlock *BB,
bool AllowCUDALibDevice) {
for (const Instruction &Inst : *BB) {
const CallInst *Call = dyn_cast<CallInst>(&Inst);
if (Call && isValidFunctionInKernel(Call->getCalledFunction(),
AllowCUDALibDevice))
continue;
for (Value *Op : Inst.operands())
// Look for (<func-type>*) among operands of Inst
if (auto PtrTy = dyn_cast<PointerType>(Op->getType())) {
if (isa<FunctionType>(PtrTy->getElementType())) {
LLVM_DEBUG(dbgs()
<< Inst << " has illegal use of function in kernel.\n");
return true;
}
}
}
return false;
}
/// Return whether the Scop S uses functions in a way that we do not support.
bool containsInvalidKernelFunction(const Scop &S, bool AllowCUDALibDevice) {
for (auto &Stmt : S) {
if (Stmt.isBlockStmt()) {
if (containsInvalidKernelFunctionInBlock(Stmt.getBasicBlock(),
AllowCUDALibDevice))
return true;
} else {
assert(Stmt.isRegionStmt() &&
"Stmt was neither block nor region statement");
for (const BasicBlock *BB : Stmt.getRegion()->blocks())
if (containsInvalidKernelFunctionInBlock(BB, AllowCUDALibDevice))
return true;
}
}
return false;
}
/// Generate code for a given GPU AST described by @p Root.
///
/// @param Root An isl_ast_node pointing to the root of the GPU AST.
/// @param Prog The GPU Program to generate code for.
void generateCode(__isl_take isl_ast_node *Root, gpu_prog *Prog) {
ScopAnnotator Annotator;
Annotator.buildAliasScopes(*S);
Region *R = &S->getRegion();
simplifyRegion(R, DT, LI, RI);
BasicBlock *EnteringBB = R->getEnteringBlock();
PollyIRBuilder Builder(EnteringBB->getContext(), ConstantFolder(),
IRInserter(Annotator));
Builder.SetInsertPoint(EnteringBB->getTerminator());
// Only build the run-time condition and parameters _after_ having
// introduced the conditional branch. This is important as the conditional
// branch will guard the original scop from new induction variables that
// the SCEVExpander may introduce while code generating the parameters and
// which may introduce scalar dependences that prevent us from correctly
// code generating this scop.
BBPair StartExitBlocks;
BranchInst *CondBr = nullptr;
std::tie(StartExitBlocks, CondBr) =
executeScopConditionally(*S, Builder.getTrue(), *DT, *RI, *LI);
BasicBlock *StartBlock = std::get<0>(StartExitBlocks);
assert(CondBr && "CondBr not initialized by executeScopConditionally");
GPUNodeBuilder NodeBuilder(Builder, Annotator, *DL, *LI, *SE, *DT, *S,
StartBlock, Prog, Runtime, Architecture);
// TODO: Handle LICM
auto SplitBlock = StartBlock->getSinglePredecessor();
Builder.SetInsertPoint(SplitBlock->getTerminator());
isl_ast_build *Build = isl_ast_build_alloc(S->getIslCtx().get());
isl::ast_expr Condition =
IslAst::buildRunCondition(*S, isl::manage_copy(Build));
isl_ast_expr *SufficientCompute = createSufficientComputeCheck(*S, Build);
Condition =
isl::manage(isl_ast_expr_and(Condition.release(), SufficientCompute));
isl_ast_build_free(Build);
// preload invariant loads. Note: This should happen before the RTC
// because the RTC may depend on values that are invariant load hoisted.
if (!NodeBuilder.preloadInvariantLoads()) {
// Patch the introduced branch condition to ensure that we always execute
// the original SCoP.
auto *FalseI1 = Builder.getFalse();
auto *SplitBBTerm = Builder.GetInsertBlock()->getTerminator();
SplitBBTerm->setOperand(0, FalseI1);
LLVM_DEBUG(dbgs() << "preloading invariant loads failed in function: " +
S->getFunction().getName() +
" | Scop Region: " + S->getNameStr());
// adjust the dominator tree accordingly.
auto *ExitingBlock = StartBlock->getUniqueSuccessor();
assert(ExitingBlock);
auto *MergeBlock = ExitingBlock->getUniqueSuccessor();
assert(MergeBlock);
polly::markBlockUnreachable(*StartBlock, Builder);
polly::markBlockUnreachable(*ExitingBlock, Builder);
auto *ExitingBB = S->getExitingBlock();
assert(ExitingBB);
DT->changeImmediateDominator(MergeBlock, ExitingBB);
DT->eraseNode(ExitingBlock);
isl_ast_node_free(Root);
} else {
if (polly::PerfMonitoring) {
PerfMonitor P(*S, EnteringBB->getParent()->getParent());
P.initialize();
P.insertRegionStart(SplitBlock->getTerminator());
// TODO: actually think if this is the correct exiting block to place
// the `end` performance marker. Invariant load hoisting changes
// the CFG in a way that I do not precisely understand, so I
// (Siddharth<siddu.druid@gmail.com>) should come back to this and
// think about which exiting block to use.
auto *ExitingBlock = StartBlock->getUniqueSuccessor();
assert(ExitingBlock);
BasicBlock *MergeBlock = ExitingBlock->getUniqueSuccessor();
P.insertRegionEnd(MergeBlock->getTerminator());
}
NodeBuilder.addParameters(S->getContext().release());
Value *RTC = NodeBuilder.createRTC(Condition.release());
Builder.GetInsertBlock()->getTerminator()->setOperand(0, RTC);
Builder.SetInsertPoint(&*StartBlock->begin());
NodeBuilder.create(Root);
}
/// In case a sequential kernel has more surrounding loops as any parallel
/// kernel, the SCoP is probably mostly sequential. Hence, there is no
/// point in running it on a GPU.
if (NodeBuilder.DeepestSequential > NodeBuilder.DeepestParallel)
CondBr->setOperand(0, Builder.getFalse());
if (!NodeBuilder.BuildSuccessful)
CondBr->setOperand(0, Builder.getFalse());
}
bool runOnScop(Scop &CurrentScop) override {
S = &CurrentScop;
LI = &getAnalysis<LoopInfoWrapperPass>().getLoopInfo();
DT = &getAnalysis<DominatorTreeWrapperPass>().getDomTree();
SE = &getAnalysis<ScalarEvolutionWrapperPass>().getSE();
DL = &S->getRegion().getEntry()->getModule()->getDataLayout();
RI = &getAnalysis<RegionInfoPass>().getRegionInfo();
LLVM_DEBUG(dbgs() << "PPCGCodeGen running on : " << getUniqueScopName(S)
<< " | loop depth: " << S->getMaxLoopDepth() << "\n");
// We currently do not support functions other than intrinsics inside
// kernels, as code generation will need to offload function calls to the
// kernel. This may lead to a kernel trying to call a function on the host.
// This also allows us to prevent codegen from trying to take the
// address of an intrinsic function to send to the kernel.
if (containsInvalidKernelFunction(CurrentScop,
Architecture == GPUArch::NVPTX64)) {
LLVM_DEBUG(
dbgs() << getUniqueScopName(S)
<< " contains function which cannot be materialised in a GPU "
"kernel. Bailing out.\n";);
return false;
}
auto PPCGScop = createPPCGScop();
auto PPCGProg = createPPCGProg(PPCGScop);
auto PPCGGen = generateGPU(PPCGScop, PPCGProg);
if (PPCGGen->tree) {
generateCode(isl_ast_node_copy(PPCGGen->tree), PPCGProg);
CurrentScop.markAsToBeSkipped();
} else {
LLVM_DEBUG(dbgs() << getUniqueScopName(S)
<< " has empty PPCGGen->tree. Bailing out.\n");
}
freeOptions(PPCGScop);
freePPCGGen(PPCGGen);
gpu_prog_free(PPCGProg);
ppcg_scop_free(PPCGScop);
return true;
}
void printScop(raw_ostream &, Scop &) const override {}
void getAnalysisUsage(AnalysisUsage &AU) const override {
ScopPass::getAnalysisUsage(AU);
AU.addRequired<DominatorTreeWrapperPass>();
AU.addRequired<RegionInfoPass>();
AU.addRequired<ScalarEvolutionWrapperPass>();
AU.addRequired<ScopDetectionWrapperPass>();
AU.addRequired<ScopInfoRegionPass>();
AU.addRequired<LoopInfoWrapperPass>();
// FIXME: We do not yet add regions for the newly generated code to the
// region tree.
}
};
} // namespace
char PPCGCodeGeneration::ID = 1;
Pass *polly::createPPCGCodeGenerationPass(GPUArch Arch, GPURuntime Runtime) {
PPCGCodeGeneration *generator = new PPCGCodeGeneration();
generator->Runtime = Runtime;
generator->Architecture = Arch;
return generator;
}
INITIALIZE_PASS_BEGIN(PPCGCodeGeneration, "polly-codegen-ppcg",
"Polly - Apply PPCG translation to SCOP", false, false)
INITIALIZE_PASS_DEPENDENCY(DependenceInfo);
INITIALIZE_PASS_DEPENDENCY(DominatorTreeWrapperPass);
INITIALIZE_PASS_DEPENDENCY(LoopInfoWrapperPass);
INITIALIZE_PASS_DEPENDENCY(RegionInfoPass);
INITIALIZE_PASS_DEPENDENCY(ScalarEvolutionWrapperPass);
INITIALIZE_PASS_DEPENDENCY(ScopDetectionWrapperPass);
INITIALIZE_PASS_END(PPCGCodeGeneration, "polly-codegen-ppcg",
"Polly - Apply PPCG translation to SCOP", false, false)
Reference in New Issue View Git Blame Copy Permalink