In some cases the compiler can deduce the length of an array section
as constants. With this information, VLAs can be avoided in place of
a constant sized array or even a scalar value if the length is 1.
Example:
int a[4], b[2];
pragma omp parallel reduction(+: a[1:2], b[1:1])
{ }
For chained array sections, this optimization is restricted to cases
where all array sections except the last have a constant length 1.
This trivially guarantees that there are no holes in the memory region
that needs to be privatized.
Example:
int c[3][4];
pragma omp parallel reduction(+: c[1:1][1:2])
{ }
This relands commit r316229 that I reverted in r316235 because it
failed on some bots. During investigation I found that this was because
Clang and GCC evaluate the two arguments to emplace_back() in
ReductionCodeGen::emitSharedLValue() in a different order, hence
leading to a different order of generated instructions in the final
LLVM IR. Fix this by passing in the arguments from temporary variables
that are evaluated in a defined order.
Differential Revision: https://reviews.llvm.org/D39136
llvm-svn: 316362
In some cases the compiler can deduce the length of an array section
as constants. With this information, VLAs can be avoided in place of
a constant sized array or even a scalar value if the length is 1.
Example:
int a[4], b[2];
pragma omp parallel reduction(+: a[1:2], b[1:1])
{ }
For chained array sections, this optimization is restricted to cases
where all array sections except the last have a constant length 1.
This trivially guarantees that there are no holes in the memory region
that needs to be privatized.
Example:
int c[3][4];
pragma omp parallel reduction(+: c[1:1][1:2])
{ }
Differential Revision: https://reviews.llvm.org/D39136
llvm-svn: 316229
If the variables is boolean and we generating inner function with real
types, the codegen may crash because of not loading boolean value from
memory.
llvm-svn: 316011
Besides obvious code simplification, avoiding explicit creation
of LValueBaseInfo objects makes it easier to make TBAA
information to be part of such objects.
This is part of D38126 reworked to be a separate patch to
simplify review.
Differential Revision: https://reviews.llvm.org/D38695
llvm-svn: 315289
https://reviews.llvm.org/D38371
This patch implements codegen for the combined 'teams distribute" OpenMP pragma and adds regression tests for all its clauses.
llvm-svn: 314905
directives.
The argument of the `device` clause in target-based executable
directives must be captured to support codegen for the `target`
directives with the `depend` clauses.
llvm-svn: 314686
directives.
If the variable is used in the target-based region but is not found in
any private|mapping clause, then generate implicit firstprivate|map
clauses for these implicitly mapped variables.
llvm-svn: 314205
__kmpc_for_static_fini().
Added special flags for calls of __kmpc_for_static_fini(), like previous
ly for __kmpc_for_static_init(). Added flag OMP_IDENT_WORK_DISTRIBUTE
for distribute cnstruct, OMP_IDENT_WORK_SECTIONS for sections-based
constructs and OMP_IDENT_WORK_LOOP for loop-based constructs in
location flags.
llvm-svn: 312642
of class fails to map class static variable.
If the global variable is captured and it has several redeclarations,
sometimes it may lead to a compiler crash. Patch fixes this by working
only with canonical declarations.
llvm-svn: 311479
If worksharing construct has at least one linear item, an implicit
synchronization point must be emitted to avoid possible conflict with
the loading/storing values to the original variables. Added implicit
barrier if the linear item is found before actual start of the
worksharing construct.
llvm-svn: 311013
__kmpc_for_static_init().
OpenMP 5.0 will include OpenMP Tools interface that requires distinguishing different worksharing constructs.
Since the same entry point (__kmp_for_static_init(ident_t *loc,
kmp_int32 global_tid,........)) is called in case static
loop/sections/distribute it is suggested using 'flags' field of the
ident_t structure to pass the type of the construct.
llvm-svn: 310865
name.
If the host code is compiled with the debug info, while the target
without, there is a problem that the compiler is unable to find the
debug wrapper. Patch fixes this problem by emitting special name for the
debug version of the code.
llvm-svn: 310511
Arguments, passed to the outlined function, must have correct address
space info for proper Debug info support. Patch sets global address
space for arguments that are mapped and passed by reference.
Also, cuda-gdb does not handle reference types correctly, so reference
arguments are represented as pointers.
llvm-svn: 310387
Arguments, passed to the outlined function, must have correct address
space info for proper Debug info support. Patch sets global address
space for arguments that are mapped and passed by reference.
Also, cuda-gdb does not handle reference types correctly, so reference
arguments are represented as pointers.
llvm-svn: 310377
Arguments, passed to the outlined function, must have correct address
space info for proper Debug info support. Patch sets global address
space for arguments that are mapped and passed by reference.
Also, cuda-gdb does not handle reference types correctly, so reference
arguments are represented as pointers.
llvm-svn: 310360
Arguments, passed to the outlined function, must have correct address
space info for proper Debug info support. Patch sets global address
space for arguments that are mapped and passed by reference.
Also, cuda-gdb does not handle reference types correctly, so reference
arguments are represented as pointers.
llvm-svn: 310104
If taskloop directive has no associated nogroup clause, it must emitted
inside implicit taskgroup block. Runtime supports it, but we need to
generate implicit taskgroup block explicitly to support future
reductions codegen.
llvm-svn: 307822
Currently, if the some of the parameters are captured by value, this
argument is converted to uintptr_t type and thus we loosing the debug
info about real type of the argument (captured variable):
```
void @.outlined_function.(uintptr %par);
...
%a = alloca i32
%a.casted = alloca uintptr
%cast = bitcast uintptr* %a.casted to i32*
%a.val = load i32, i32 *%a
store i32 %a.val, i32 *%cast
%a.casted.val = load uintptr, uintptr* %a.casted
call void @.outlined_function.(uintptr %a.casted.val)
...
```
To resolve this problem, in debug mode a speciall external wrapper
function is generated, that calls the outlined function with the correct
parameters types:
```
void @.wrapper.(uintptr %par) {
%a = alloca i32
%cast = bitcast i32* %a to uintptr*
store uintptr %par, uintptr *%cast
%a.val = load i32, i32* %a
call void @.outlined_function.(i32 %a)
ret void
}
void @.outlined_function.(i32 %par);
...
%a = alloca i32
%a.casted = alloca uintptr
%cast = bitcast uintptr* %a.casted to i32*
%a.val = load i32, i32 *%a
store i32 %a.val, i32 *%cast
%a.casted.val = load uintptr, uintptr* %a.casted
call void @.wrapper.(uintptr %a.casted.val)
...
```
llvm-svn: 306697
Summary:
If the first parameter of the function is the ImplicitParamDecl, codegen
automatically marks it as an implicit argument with `this` or `self`
pointer. Added internal kind of the ImplicitParamDecl to separate
'this', 'self', 'vtt' and other implicit parameters from other kind of
parameters.
Reviewers: rjmccall, aaron.ballman
Subscribers: cfe-commits
Differential Revision: https://reviews.llvm.org/D33735
llvm-svn: 305075
The functions creating LValues propagated information about alignment
source. Extend the propagated data to also include information about
possible unrestricted aliasing. A new class LValueBaseInfo will
contain both AlignmentSource and MayAlias info.
This patch should not introduce any functional changes.
Differential Revision: https://reviews.llvm.org/D33284
llvm-svn: 303358
[OpenMP] Initial implementation of code generation for pragma 'distribute parallel for' on host
https://reviews.llvm.org/D29508
This patch makes the following additions:
It abstracts away loop bound generation code from procedures associated with pragma 'for' and loops in general, in such a way that the same procedures can be used for 'distribute parallel for' without the need for a full re-implementation.
It implements code generation for 'distribute parallel for' and adds regression tests. It includes tests for clauses.
It is important to notice that most of the clauses are implemented as part of existing procedures. For instance, firstprivate is already implemented for 'distribute' and 'for' as separate pragmas. As the implementation of 'distribute parallel for' is based on the same procedures, then we automatically obtain implementation for such clauses without the need to add new code. However, this requires regression tests that verify correctness of produced code.
llvm-svn: 301340
https://reviews.llvm.org/D29508
This patch makes the following additions:
1. It abstracts away loop bound generation code from procedures associated with pragma 'for' and loops in general, in such a way that the same procedures can be used for 'distribute parallel for' without the need for a full re-implementation.
2. It implements code generation for 'distribute parallel for' and adds regression tests. It includes tests for clauses.
It is important to notice that most of the clauses are implemented as part of existing procedures. For instance, firstprivate is already implemented for 'distribute' and 'for' as separate pragmas. As the implementation of 'distribute parallel for' is based on the same procedures, then we automatically obtain implementation for such clauses without the need to add new code. However, this requires regression tests that verify correctness of produced code.
Looking forward to comments.
llvm-svn: 301223
If the type of the captured variable is a pointer(s) to variably
modified type, this type was not processed correctly. Need to drill into
the type, find the innermost variably modified array type and convert it
to canonical parameter type.
llvm-svn: 299868
Sema holds the current FPOptions which is adjusted by 'pragma STDC
FP_CONTRACT'. This then gets propagated into expression nodes as they are
built.
This encapsulates FPOptions so that this propagation happens opaquely rather
than directly with the fp_contractable on/off bit. This allows controlled
transitioning of fp_contractable to a ternary value (off, on, fast). It will
also allow adding more fast-math flags later.
This is toward moving fp-contraction=fast from an LLVM TargetOption to a
FastMathFlag in order to fix PR25721.
Differential Revision: https://reviews.llvm.org/D31166
llvm-svn: 298877
This patch implements codegen for the reduction clause on
any teams construct for elementary data types. It builds
on parallel reductions on the GPU. Subsequently,
the team master writes to a unique location in a global
memory scratchpad. The last team to do so loads and
reduces this array to calculate the final result.
This patch emits two helper functions that are used by
the OpenMP runtime on the GPU to perform reductions across
teams.
Patch by Tian Jin in collaboration with Arpith Jacob
Reviewers: ABataev
Differential Revision: https://reviews.llvm.org/D29879
llvm-svn: 295335
This patch implements codegen for the reduction clause on
any parallel construct for elementary data types. An efficient
implementation requires hierarchical reduction within a
warp and a threadblock. It is complicated by the fact that
variables declared in the stack of a CUDA thread cannot be
shared with other threads.
The patch creates a struct to hold reduction variables and
a number of helper functions. The OpenMP runtime on the GPU
implements reduction algorithms that uses these helper
functions to perform reductions within a team. Variables are
shared between CUDA threads using shuffle intrinsics.
An implementation of reductions on the NVPTX device is
substantially different to that of CPUs. However, this patch
is written so that there are minimal changes to the rest of
OpenMP codegen.
The implemented design allows the compiler and runtime to be
decoupled, i.e., the runtime does not need to know of the
reduction operation(s), the type of the reduction variable(s),
or the number of reductions. The design also allows reuse of
host codegen, with appropriate specialization for the NVPTX
device.
While the patch does introduce a number of abstractions, the
expected use case calls for inlining of the GPU OpenMP runtime.
After inlining and optimizations in LLVM, these abstractions
are unwound and performance of OpenMP reductions is comparable
to CUDA-canonical code.
Patch by Tian Jin in collaboration with Arpith Jacob
Reviewers: ABataev
Differential Revision: https://reviews.llvm.org/D29758
llvm-svn: 295333
This patch implements codegen for the reduction clause on
any parallel construct for elementary data types. An efficient
implementation requires hierarchical reduction within a
warp and a threadblock. It is complicated by the fact that
variables declared in the stack of a CUDA thread cannot be
shared with other threads.
The patch creates a struct to hold reduction variables and
a number of helper functions. The OpenMP runtime on the GPU
implements reduction algorithms that uses these helper
functions to perform reductions within a team. Variables are
shared between CUDA threads using shuffle intrinsics.
An implementation of reductions on the NVPTX device is
substantially different to that of CPUs. However, this patch
is written so that there are minimal changes to the rest of
OpenMP codegen.
The implemented design allows the compiler and runtime to be
decoupled, i.e., the runtime does not need to know of the
reduction operation(s), the type of the reduction variable(s),
or the number of reductions. The design also allows reuse of
host codegen, with appropriate specialization for the NVPTX
device.
While the patch does introduce a number of abstractions, the
expected use case calls for inlining of the GPU OpenMP runtime.
After inlining and optimizations in LLVM, these abstractions
are unwound and performance of OpenMP reductions is comparable
to CUDA-canonical code.
Patch by Tian Jin in collaboration with Arpith Jacob
Reviewers: ABataev
Differential Revision: https://reviews.llvm.org/D29758
llvm-svn: 295319
Ivan A. Kosarev