llvm-project/llvm/lib/MCA/HardwareUnits/Scheduler.cpp

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//===--------------------- Scheduler.cpp ------------------------*- C++ -*-===//
//
// 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
//
//===----------------------------------------------------------------------===//
//
// A scheduler for processor resource units and processor resource groups.
//
//===----------------------------------------------------------------------===//
#include "llvm/MCA/HardwareUnits/Scheduler.h"
#include "llvm/Support/Debug.h"
#include "llvm/Support/raw_ostream.h"
namespace llvm {
namespace mca {
#define DEBUG_TYPE "llvm-mca"
void Scheduler::initializeStrategy(std::unique_ptr<SchedulerStrategy> S) {
// Ensure we have a valid (non-null) strategy object.
Strategy = S ? std::move(S) : std::make_unique<DefaultSchedulerStrategy>();
}
// Anchor the vtable of SchedulerStrategy and DefaultSchedulerStrategy.
SchedulerStrategy::~SchedulerStrategy() = default;
DefaultSchedulerStrategy::~DefaultSchedulerStrategy() = default;
#ifndef NDEBUG
void Scheduler::dump() const {
dbgs() << "[SCHEDULER]: WaitSet size is: " << WaitSet.size() << '\n';
dbgs() << "[SCHEDULER]: ReadySet size is: " << ReadySet.size() << '\n';
dbgs() << "[SCHEDULER]: IssuedSet size is: " << IssuedSet.size() << '\n';
Resources->dump();
}
#endif
Scheduler::Status Scheduler::isAvailable(const InstRef &IR) {
ResourceStateEvent RSE =
Resources->canBeDispatched(IR.getInstruction()->getUsedBuffers());
HadTokenStall = RSE != RS_BUFFER_AVAILABLE;
switch (RSE) {
case ResourceStateEvent::RS_BUFFER_UNAVAILABLE:
return Scheduler::SC_BUFFERS_FULL;
case ResourceStateEvent::RS_RESERVED:
return Scheduler::SC_DISPATCH_GROUP_STALL;
case ResourceStateEvent::RS_BUFFER_AVAILABLE:
break;
}
// Give lower priority to LSUnit stall events.
LSUnit::Status LSS = LSU.isAvailable(IR);
HadTokenStall = LSS != LSUnit::LSU_AVAILABLE;
switch (LSS) {
case LSUnit::LSU_LQUEUE_FULL:
return Scheduler::SC_LOAD_QUEUE_FULL;
case LSUnit::LSU_SQUEUE_FULL:
return Scheduler::SC_STORE_QUEUE_FULL;
case LSUnit::LSU_AVAILABLE:
return Scheduler::SC_AVAILABLE;
}
llvm_unreachable("Don't know how to process this LSU state result!");
}
void Scheduler::issueInstructionImpl(
InstRef &IR,
SmallVectorImpl<std::pair<ResourceRef, ResourceCycles>> &UsedResources) {
Instruction *IS = IR.getInstruction();
const InstrDesc &D = IS->getDesc();
// Issue the instruction and collect all the consumed resources
// into a vector. That vector is then used to notify the listener.
Resources->issueInstruction(D, UsedResources);
// Notify the instruction that it started executing.
// This updates the internal state of each write.
IS->execute(IR.getSourceIndex());
IS->computeCriticalRegDep();
if (IS->isMemOp()) {
LSU.onInstructionIssued(IR);
const MemoryGroup &Group = LSU.getGroup(IS->getLSUTokenID());
IS->setCriticalMemDep(Group.getCriticalPredecessor());
}
if (IS->isExecuting())
IssuedSet.emplace_back(IR);
else if (IS->isExecuted())
LSU.onInstructionExecuted(IR);
}
// Release the buffered resources and issue the instruction.
void Scheduler::issueInstruction(
InstRef &IR,
SmallVectorImpl<std::pair<ResourceRef, ResourceCycles>> &UsedResources,
SmallVectorImpl<InstRef> &PendingInstructions,
SmallVectorImpl<InstRef> &ReadyInstructions) {
const Instruction &Inst = *IR.getInstruction();
bool HasDependentUsers = Inst.hasDependentUsers();
HasDependentUsers |= Inst.isMemOp() && LSU.hasDependentUsers(IR);
Resources->releaseBuffers(Inst.getUsedBuffers());
issueInstructionImpl(IR, UsedResources);
// Instructions that have been issued during this cycle might have unblocked
// other dependent instructions. Dependent instructions may be issued during
// this same cycle if operands have ReadAdvance entries. Promote those
// instructions to the ReadySet and notify the caller that those are ready.
if (HasDependentUsers)
if (promoteToPendingSet(PendingInstructions))
promoteToReadySet(ReadyInstructions);
}
bool Scheduler::promoteToReadySet(SmallVectorImpl<InstRef> &Ready) {
// Scan the set of waiting instructions and promote them to the
// ready set if operands are all ready.
unsigned PromotedElements = 0;
for (auto I = PendingSet.begin(), E = PendingSet.end(); I != E;) {
InstRef &IR = *I;
if (!IR)
break;
// Check if there are unsolved register dependencies.
Instruction &IS = *IR.getInstruction();
if (!IS.isReady() && !IS.updatePending()) {
++I;
continue;
}
// Check if there are unsolved memory dependencies.
if (IS.isMemOp() && !LSU.isReady(IR)) {
++I;
continue;
}
LLVM_DEBUG(dbgs() << "[SCHEDULER]: Instruction #" << IR
<< " promoted to the READY set.\n");
Ready.emplace_back(IR);
ReadySet.emplace_back(IR);
IR.invalidate();
++PromotedElements;
std::iter_swap(I, E - PromotedElements);
}
PendingSet.resize(PendingSet.size() - PromotedElements);
return PromotedElements;
}
bool Scheduler::promoteToPendingSet(SmallVectorImpl<InstRef> &Pending) {
// Scan the set of waiting instructions and promote them to the
// pending set if operands are all ready.
unsigned RemovedElements = 0;
for (auto I = WaitSet.begin(), E = WaitSet.end(); I != E;) {
InstRef &IR = *I;
if (!IR)
break;
// Check if this instruction is now ready. In case, force
// a transition in state using method 'updateDispatched()'.
Instruction &IS = *IR.getInstruction();
if (IS.isDispatched() && !IS.updateDispatched()) {
++I;
continue;
}
if (IS.isMemOp() && LSU.isWaiting(IR)) {
++I;
continue;
}
LLVM_DEBUG(dbgs() << "[SCHEDULER]: Instruction #" << IR
<< " promoted to the PENDING set.\n");
Pending.emplace_back(IR);
PendingSet.emplace_back(IR);
IR.invalidate();
++RemovedElements;
std::iter_swap(I, E - RemovedElements);
}
WaitSet.resize(WaitSet.size() - RemovedElements);
return RemovedElements;
}
InstRef Scheduler::select() {
unsigned QueueIndex = ReadySet.size();
for (unsigned I = 0, E = ReadySet.size(); I != E; ++I) {
InstRef &IR = ReadySet[I];
if (QueueIndex == ReadySet.size() ||
Strategy->compare(IR, ReadySet[QueueIndex])) {
[MCA] Highlight kernel bottlenecks in the summary view. This patch adds a new flag named -bottleneck-analysis to print out information about throughput bottlenecks. MCA knows how to identify and classify dynamic dispatch stalls. However, it doesn't know how to analyze and highlight kernel bottlenecks. The goal of this patch is to teach MCA how to correlate increases in backend pressure to backend stalls (and therefore, the loss of throughput). From a Scheduler point of view, backend pressure is a function of the scheduler buffer usage (i.e. how the number of uOps in the scheduler buffers changes over time). Backend pressure increases (or decreases) when there is a mismatch between the number of opcodes dispatched, and the number of opcodes issued in the same cycle. Since buffer resources are limited, continuous increases in backend pressure would eventually leads to dispatch stalls. So, there is a strong correlation between dispatch stalls, and how backpressure changed over time. This patch teaches how to identify situations where backend pressure increases due to: - unavailable pipeline resources. - data dependencies. Data dependencies may delay execution of instructions and therefore increase the time that uOps have to spend in the scheduler buffers. That often translates to an increase in backend pressure which may eventually lead to a bottleneck. Contention on pipeline resources may also delay execution of instructions, and lead to a temporary increase in backend pressure. Internally, the Scheduler classifies instructions based on whether register / memory operands are available or not. An instruction is marked as "ready to execute" only if data dependencies are fully resolved. Every cycle, the Scheduler attempts to execute all instructions that are ready to execute. If an instruction cannot execute because of unavailable pipeline resources, then the Scheduler internally updates a BusyResourceUnits mask with the ID of each unavailable resource. ExecuteStage is responsible for tracking changes in backend pressure. If backend pressure increases during a cycle because of contention on pipeline resources, then ExecuteStage sends a "backend pressure" event to the listeners. That event would contain information about instructions delayed by resource pressure, as well as the BusyResourceUnits mask. Note that ExecuteStage also knows how to identify situations where backpressure increased because of delays introduced by data dependencies. The SummaryView observes "backend pressure" events and prints out a "bottleneck report". Example of bottleneck report: ``` Cycles with backend pressure increase [ 99.89% ] Throughput Bottlenecks: Resource Pressure [ 0.00% ] Data Dependencies: [ 99.89% ] - Register Dependencies [ 0.00% ] - Memory Dependencies [ 99.89% ] ``` A bottleneck report is printed out only if increases in backend pressure eventually caused backend stalls. About the time complexity: Time complexity is linear in the number of instructions in the Scheduler::PendingSet. The average slowdown tends to be in the range of ~5-6%. For memory intensive kernels, the slowdown can be significant if flag -noalias=false is specified. In the worst case scenario I have observed a slowdown of ~30% when flag -noalias=false was specified. We can definitely recover part of that slowdown if we optimize class LSUnit (by doing extra bookkeeping to speedup queries). For now, this new analysis is disabled by default, and it can be enabled via flag -bottleneck-analysis. Users of MCA as a library can enable the generation of pressure events through the constructor of ExecuteStage. This patch partially addresses https://bugs.llvm.org/show_bug.cgi?id=37494 Differential Revision: https://reviews.llvm.org/D58728 llvm-svn: 355308
2019-03-04 19:52:34 +08:00
Instruction &IS = *IR.getInstruction();
uint64_t BusyResourceMask = Resources->checkAvailability(IS.getDesc());
if (BusyResourceMask)
IS.setCriticalResourceMask(BusyResourceMask);
BusyResourceUnits |= BusyResourceMask;
if (!BusyResourceMask)
QueueIndex = I;
}
}
if (QueueIndex == ReadySet.size())
return InstRef();
// We found an instruction to issue.
InstRef IR = ReadySet[QueueIndex];
std::swap(ReadySet[QueueIndex], ReadySet[ReadySet.size() - 1]);
ReadySet.pop_back();
return IR;
}
void Scheduler::updateIssuedSet(SmallVectorImpl<InstRef> &Executed) {
unsigned RemovedElements = 0;
for (auto I = IssuedSet.begin(), E = IssuedSet.end(); I != E;) {
InstRef &IR = *I;
if (!IR)
break;
Instruction &IS = *IR.getInstruction();
if (!IS.isExecuted()) {
LLVM_DEBUG(dbgs() << "[SCHEDULER]: Instruction #" << IR
<< " is still executing.\n");
++I;
continue;
}
// Instruction IR has completed execution.
LSU.onInstructionExecuted(IR);
Executed.emplace_back(IR);
++RemovedElements;
IR.invalidate();
std::iter_swap(I, E - RemovedElements);
}
IssuedSet.resize(IssuedSet.size() - RemovedElements);
}
[MCA] Highlight kernel bottlenecks in the summary view. This patch adds a new flag named -bottleneck-analysis to print out information about throughput bottlenecks. MCA knows how to identify and classify dynamic dispatch stalls. However, it doesn't know how to analyze and highlight kernel bottlenecks. The goal of this patch is to teach MCA how to correlate increases in backend pressure to backend stalls (and therefore, the loss of throughput). From a Scheduler point of view, backend pressure is a function of the scheduler buffer usage (i.e. how the number of uOps in the scheduler buffers changes over time). Backend pressure increases (or decreases) when there is a mismatch between the number of opcodes dispatched, and the number of opcodes issued in the same cycle. Since buffer resources are limited, continuous increases in backend pressure would eventually leads to dispatch stalls. So, there is a strong correlation between dispatch stalls, and how backpressure changed over time. This patch teaches how to identify situations where backend pressure increases due to: - unavailable pipeline resources. - data dependencies. Data dependencies may delay execution of instructions and therefore increase the time that uOps have to spend in the scheduler buffers. That often translates to an increase in backend pressure which may eventually lead to a bottleneck. Contention on pipeline resources may also delay execution of instructions, and lead to a temporary increase in backend pressure. Internally, the Scheduler classifies instructions based on whether register / memory operands are available or not. An instruction is marked as "ready to execute" only if data dependencies are fully resolved. Every cycle, the Scheduler attempts to execute all instructions that are ready to execute. If an instruction cannot execute because of unavailable pipeline resources, then the Scheduler internally updates a BusyResourceUnits mask with the ID of each unavailable resource. ExecuteStage is responsible for tracking changes in backend pressure. If backend pressure increases during a cycle because of contention on pipeline resources, then ExecuteStage sends a "backend pressure" event to the listeners. That event would contain information about instructions delayed by resource pressure, as well as the BusyResourceUnits mask. Note that ExecuteStage also knows how to identify situations where backpressure increased because of delays introduced by data dependencies. The SummaryView observes "backend pressure" events and prints out a "bottleneck report". Example of bottleneck report: ``` Cycles with backend pressure increase [ 99.89% ] Throughput Bottlenecks: Resource Pressure [ 0.00% ] Data Dependencies: [ 99.89% ] - Register Dependencies [ 0.00% ] - Memory Dependencies [ 99.89% ] ``` A bottleneck report is printed out only if increases in backend pressure eventually caused backend stalls. About the time complexity: Time complexity is linear in the number of instructions in the Scheduler::PendingSet. The average slowdown tends to be in the range of ~5-6%. For memory intensive kernels, the slowdown can be significant if flag -noalias=false is specified. In the worst case scenario I have observed a slowdown of ~30% when flag -noalias=false was specified. We can definitely recover part of that slowdown if we optimize class LSUnit (by doing extra bookkeeping to speedup queries). For now, this new analysis is disabled by default, and it can be enabled via flag -bottleneck-analysis. Users of MCA as a library can enable the generation of pressure events through the constructor of ExecuteStage. This patch partially addresses https://bugs.llvm.org/show_bug.cgi?id=37494 Differential Revision: https://reviews.llvm.org/D58728 llvm-svn: 355308
2019-03-04 19:52:34 +08:00
uint64_t Scheduler::analyzeResourcePressure(SmallVectorImpl<InstRef> &Insts) {
2021-01-07 10:27:33 +08:00
llvm::append_range(Insts, ReadySet);
[MCA] Highlight kernel bottlenecks in the summary view. This patch adds a new flag named -bottleneck-analysis to print out information about throughput bottlenecks. MCA knows how to identify and classify dynamic dispatch stalls. However, it doesn't know how to analyze and highlight kernel bottlenecks. The goal of this patch is to teach MCA how to correlate increases in backend pressure to backend stalls (and therefore, the loss of throughput). From a Scheduler point of view, backend pressure is a function of the scheduler buffer usage (i.e. how the number of uOps in the scheduler buffers changes over time). Backend pressure increases (or decreases) when there is a mismatch between the number of opcodes dispatched, and the number of opcodes issued in the same cycle. Since buffer resources are limited, continuous increases in backend pressure would eventually leads to dispatch stalls. So, there is a strong correlation between dispatch stalls, and how backpressure changed over time. This patch teaches how to identify situations where backend pressure increases due to: - unavailable pipeline resources. - data dependencies. Data dependencies may delay execution of instructions and therefore increase the time that uOps have to spend in the scheduler buffers. That often translates to an increase in backend pressure which may eventually lead to a bottleneck. Contention on pipeline resources may also delay execution of instructions, and lead to a temporary increase in backend pressure. Internally, the Scheduler classifies instructions based on whether register / memory operands are available or not. An instruction is marked as "ready to execute" only if data dependencies are fully resolved. Every cycle, the Scheduler attempts to execute all instructions that are ready to execute. If an instruction cannot execute because of unavailable pipeline resources, then the Scheduler internally updates a BusyResourceUnits mask with the ID of each unavailable resource. ExecuteStage is responsible for tracking changes in backend pressure. If backend pressure increases during a cycle because of contention on pipeline resources, then ExecuteStage sends a "backend pressure" event to the listeners. That event would contain information about instructions delayed by resource pressure, as well as the BusyResourceUnits mask. Note that ExecuteStage also knows how to identify situations where backpressure increased because of delays introduced by data dependencies. The SummaryView observes "backend pressure" events and prints out a "bottleneck report". Example of bottleneck report: ``` Cycles with backend pressure increase [ 99.89% ] Throughput Bottlenecks: Resource Pressure [ 0.00% ] Data Dependencies: [ 99.89% ] - Register Dependencies [ 0.00% ] - Memory Dependencies [ 99.89% ] ``` A bottleneck report is printed out only if increases in backend pressure eventually caused backend stalls. About the time complexity: Time complexity is linear in the number of instructions in the Scheduler::PendingSet. The average slowdown tends to be in the range of ~5-6%. For memory intensive kernels, the slowdown can be significant if flag -noalias=false is specified. In the worst case scenario I have observed a slowdown of ~30% when flag -noalias=false was specified. We can definitely recover part of that slowdown if we optimize class LSUnit (by doing extra bookkeeping to speedup queries). For now, this new analysis is disabled by default, and it can be enabled via flag -bottleneck-analysis. Users of MCA as a library can enable the generation of pressure events through the constructor of ExecuteStage. This patch partially addresses https://bugs.llvm.org/show_bug.cgi?id=37494 Differential Revision: https://reviews.llvm.org/D58728 llvm-svn: 355308
2019-03-04 19:52:34 +08:00
return BusyResourceUnits;
}
void Scheduler::analyzeDataDependencies(SmallVectorImpl<InstRef> &RegDeps,
SmallVectorImpl<InstRef> &MemDeps) {
const auto EndIt = PendingSet.end() - NumDispatchedToThePendingSet;
for (const InstRef &IR : make_range(PendingSet.begin(), EndIt)) {
const Instruction &IS = *IR.getInstruction();
[MCA] Highlight kernel bottlenecks in the summary view. This patch adds a new flag named -bottleneck-analysis to print out information about throughput bottlenecks. MCA knows how to identify and classify dynamic dispatch stalls. However, it doesn't know how to analyze and highlight kernel bottlenecks. The goal of this patch is to teach MCA how to correlate increases in backend pressure to backend stalls (and therefore, the loss of throughput). From a Scheduler point of view, backend pressure is a function of the scheduler buffer usage (i.e. how the number of uOps in the scheduler buffers changes over time). Backend pressure increases (or decreases) when there is a mismatch between the number of opcodes dispatched, and the number of opcodes issued in the same cycle. Since buffer resources are limited, continuous increases in backend pressure would eventually leads to dispatch stalls. So, there is a strong correlation between dispatch stalls, and how backpressure changed over time. This patch teaches how to identify situations where backend pressure increases due to: - unavailable pipeline resources. - data dependencies. Data dependencies may delay execution of instructions and therefore increase the time that uOps have to spend in the scheduler buffers. That often translates to an increase in backend pressure which may eventually lead to a bottleneck. Contention on pipeline resources may also delay execution of instructions, and lead to a temporary increase in backend pressure. Internally, the Scheduler classifies instructions based on whether register / memory operands are available or not. An instruction is marked as "ready to execute" only if data dependencies are fully resolved. Every cycle, the Scheduler attempts to execute all instructions that are ready to execute. If an instruction cannot execute because of unavailable pipeline resources, then the Scheduler internally updates a BusyResourceUnits mask with the ID of each unavailable resource. ExecuteStage is responsible for tracking changes in backend pressure. If backend pressure increases during a cycle because of contention on pipeline resources, then ExecuteStage sends a "backend pressure" event to the listeners. That event would contain information about instructions delayed by resource pressure, as well as the BusyResourceUnits mask. Note that ExecuteStage also knows how to identify situations where backpressure increased because of delays introduced by data dependencies. The SummaryView observes "backend pressure" events and prints out a "bottleneck report". Example of bottleneck report: ``` Cycles with backend pressure increase [ 99.89% ] Throughput Bottlenecks: Resource Pressure [ 0.00% ] Data Dependencies: [ 99.89% ] - Register Dependencies [ 0.00% ] - Memory Dependencies [ 99.89% ] ``` A bottleneck report is printed out only if increases in backend pressure eventually caused backend stalls. About the time complexity: Time complexity is linear in the number of instructions in the Scheduler::PendingSet. The average slowdown tends to be in the range of ~5-6%. For memory intensive kernels, the slowdown can be significant if flag -noalias=false is specified. In the worst case scenario I have observed a slowdown of ~30% when flag -noalias=false was specified. We can definitely recover part of that slowdown if we optimize class LSUnit (by doing extra bookkeeping to speedup queries). For now, this new analysis is disabled by default, and it can be enabled via flag -bottleneck-analysis. Users of MCA as a library can enable the generation of pressure events through the constructor of ExecuteStage. This patch partially addresses https://bugs.llvm.org/show_bug.cgi?id=37494 Differential Revision: https://reviews.llvm.org/D58728 llvm-svn: 355308
2019-03-04 19:52:34 +08:00
if (Resources->checkAvailability(IS.getDesc()))
continue;
if (IS.isMemOp() && LSU.isPending(IR))
MemDeps.emplace_back(IR);
if (IS.isPending())
[MCA] Highlight kernel bottlenecks in the summary view. This patch adds a new flag named -bottleneck-analysis to print out information about throughput bottlenecks. MCA knows how to identify and classify dynamic dispatch stalls. However, it doesn't know how to analyze and highlight kernel bottlenecks. The goal of this patch is to teach MCA how to correlate increases in backend pressure to backend stalls (and therefore, the loss of throughput). From a Scheduler point of view, backend pressure is a function of the scheduler buffer usage (i.e. how the number of uOps in the scheduler buffers changes over time). Backend pressure increases (or decreases) when there is a mismatch between the number of opcodes dispatched, and the number of opcodes issued in the same cycle. Since buffer resources are limited, continuous increases in backend pressure would eventually leads to dispatch stalls. So, there is a strong correlation between dispatch stalls, and how backpressure changed over time. This patch teaches how to identify situations where backend pressure increases due to: - unavailable pipeline resources. - data dependencies. Data dependencies may delay execution of instructions and therefore increase the time that uOps have to spend in the scheduler buffers. That often translates to an increase in backend pressure which may eventually lead to a bottleneck. Contention on pipeline resources may also delay execution of instructions, and lead to a temporary increase in backend pressure. Internally, the Scheduler classifies instructions based on whether register / memory operands are available or not. An instruction is marked as "ready to execute" only if data dependencies are fully resolved. Every cycle, the Scheduler attempts to execute all instructions that are ready to execute. If an instruction cannot execute because of unavailable pipeline resources, then the Scheduler internally updates a BusyResourceUnits mask with the ID of each unavailable resource. ExecuteStage is responsible for tracking changes in backend pressure. If backend pressure increases during a cycle because of contention on pipeline resources, then ExecuteStage sends a "backend pressure" event to the listeners. That event would contain information about instructions delayed by resource pressure, as well as the BusyResourceUnits mask. Note that ExecuteStage also knows how to identify situations where backpressure increased because of delays introduced by data dependencies. The SummaryView observes "backend pressure" events and prints out a "bottleneck report". Example of bottleneck report: ``` Cycles with backend pressure increase [ 99.89% ] Throughput Bottlenecks: Resource Pressure [ 0.00% ] Data Dependencies: [ 99.89% ] - Register Dependencies [ 0.00% ] - Memory Dependencies [ 99.89% ] ``` A bottleneck report is printed out only if increases in backend pressure eventually caused backend stalls. About the time complexity: Time complexity is linear in the number of instructions in the Scheduler::PendingSet. The average slowdown tends to be in the range of ~5-6%. For memory intensive kernels, the slowdown can be significant if flag -noalias=false is specified. In the worst case scenario I have observed a slowdown of ~30% when flag -noalias=false was specified. We can definitely recover part of that slowdown if we optimize class LSUnit (by doing extra bookkeeping to speedup queries). For now, this new analysis is disabled by default, and it can be enabled via flag -bottleneck-analysis. Users of MCA as a library can enable the generation of pressure events through the constructor of ExecuteStage. This patch partially addresses https://bugs.llvm.org/show_bug.cgi?id=37494 Differential Revision: https://reviews.llvm.org/D58728 llvm-svn: 355308
2019-03-04 19:52:34 +08:00
RegDeps.emplace_back(IR);
}
}
void Scheduler::cycleEvent(SmallVectorImpl<ResourceRef> &Freed,
SmallVectorImpl<InstRef> &Executed,
SmallVectorImpl<InstRef> &Pending,
SmallVectorImpl<InstRef> &Ready) {
LSU.cycleEvent();
// Release consumed resources.
Resources->cycleEvent(Freed);
for (InstRef &IR : IssuedSet)
IR.getInstruction()->cycleEvent();
updateIssuedSet(Executed);
for (InstRef &IR : PendingSet)
IR.getInstruction()->cycleEvent();
for (InstRef &IR : WaitSet)
IR.getInstruction()->cycleEvent();
promoteToPendingSet(Pending);
promoteToReadySet(Ready);
NumDispatchedToThePendingSet = 0;
BusyResourceUnits = 0;
}
bool Scheduler::mustIssueImmediately(const InstRef &IR) const {
const InstrDesc &Desc = IR.getInstruction()->getDesc();
if (Desc.isZeroLatency())
return true;
// Instructions that use an in-order dispatch/issue processor resource must be
// issued immediately to the pipeline(s). Any other in-order buffered
// resources (i.e. BufferSize=1) is consumed.
return Desc.MustIssueImmediately;
}
bool Scheduler::dispatch(InstRef &IR) {
Instruction &IS = *IR.getInstruction();
Resources->reserveBuffers(IS.getUsedBuffers());
// If necessary, reserve queue entries in the load-store unit (LSU).
if (IS.isMemOp())
IS.setLSUTokenID(LSU.dispatch(IR));
if (IS.isDispatched() || (IS.isMemOp() && LSU.isWaiting(IR))) {
LLVM_DEBUG(dbgs() << "[SCHEDULER] Adding #" << IR << " to the WaitSet\n");
WaitSet.push_back(IR);
return false;
}
if (IS.isPending() || (IS.isMemOp() && LSU.isPending(IR))) {
LLVM_DEBUG(dbgs() << "[SCHEDULER] Adding #" << IR
<< " to the PendingSet\n");
PendingSet.push_back(IR);
++NumDispatchedToThePendingSet;
return false;
}
assert(IS.isReady() && (!IS.isMemOp() || LSU.isReady(IR)) &&
"Unexpected internal state found!");
// Don't add a zero-latency instruction to the Ready queue.
// A zero-latency instruction doesn't consume any scheduler resources. That is
// because it doesn't need to be executed, and it is often removed at register
// renaming stage. For example, register-register moves are often optimized at
// register renaming stage by simply updating register aliases. On some
// targets, zero-idiom instructions (for example: a xor that clears the value
// of a register) are treated specially, and are often eliminated at register
// renaming stage.
if (!mustIssueImmediately(IR)) {
LLVM_DEBUG(dbgs() << "[SCHEDULER] Adding #" << IR << " to the ReadySet\n");
ReadySet.push_back(IR);
}
return true;
}
} // namespace mca
} // namespace llvm