On AVX512F targets we'll produce an emulated sequence using 3 pmuludqs with shifts and adds. On AVX512DQ we'll use vpmulld.
Fixes PR37140.
llvm-svn: 330923
The unmasked versions already didn't have this restrction. I don't think gcc or icc limit these to 64-bit mode so we shouldn't either.
llvm-svn: 330681
Summary:
kunpck intrinsics were removed in favor of native IR a few months ago. The implementation lowers them as by operation on the integer types passed to the intrinsic and then just shifting, masking, and oring them together. A special X86 DAG combine was added to recognize this patter and turn it into a concat_vector operation.
I think it makes more sense to keep the IR implementation closer to vector operations on vXi1. Given that we expect these builtins to be used around other builtins that operate on k-registers which we try to represent in IR with vXi1. InstCombine should be able to get rid of the bitcasts between integers and vXi1 leaving only the vector operations.
Reviewers: RKSimon, spatel, zvi, jina.nahias
Reviewed By: RKSimon
Subscribers: cfe-commits
Differential Revision: https://reviews.llvm.org/D42016
llvm-svn: 322461
This patch, together with a matching llvm patch (https://reviews.llvm.org/D39720), implements the lowering of X86 kunpack intrinsics to IR.
Differential Revision: https://reviews.llvm.org/D39719
Change-Id: Id5d3cb394ad33b98be79a6783d1d15569e2b798d
llvm-svn: 319777
Change Header files of the intrinsics for lowering test and testn intrinsics to IR code.
Removed test and testn builtins from clang
Differential Revision: https://reviews.llvm.org/D38737
llvm-svn: 318035
This patch, together with a matching llvm patch (https://reviews.llvm.org/D38671), implements the lowering of X86 shuffle i/f intrinsics to IR.
Differential Revision: https://reviews.llvm.org/D38672
Change-Id: I9b3c2f2b34323bd9ccb21d0c1832f848b88ec047
llvm-svn: 318025
The __builtin_ia32_pbroadcastq512_mem_mask we were previously trying to use in 32-bit mode is not implemented in the x86 backend and causes isel to fail in release builds. In debug builds it fails even earlier during legalization with an llvm_unreachable.
While there add the missing test case for this intrinsic for this for 64-bit mode.
This fixes PR34631. D37668 should be able to recover this for 32-bit mode soon. But I wanted to fix the crash ahead of that.
llvm-svn: 313392
Based off the Intel Intrinsics guide, we should expect a void const* argument.
Prevents 'passing 'const void *' to parameter of type 'void *' discards qualifiers' warnings.
Differential Revision: https://reviews.llvm.org/D37449
llvm-svn: 312523
Clang specifies a max type alignment of 16 bytes on darwin targets (annoyingly in the driver not via cc1), meaning that the builtin nontemporal stores don't correctly align the loads/stores to 32 or 64 bytes when required, resulting in lowering to temporal unaligned loads/stores.
This patch casts the vectors to explicitly aligned types prior to the load/store to ensure that the require alignment is respected.
Differential Revision: https://reviews.llvm.org/D35996
llvm-svn: 309488
MOVNTDQA non-temporal aligned vector loads can be correctly represented using generic builtin loads, allowing us to remove the existing x86 intrinsics.
LLVM companion patch: D31767.
Differential Revision: https://reviews.llvm.org/D31766
llvm-svn: 300326
This will allow the backend to constant fold these to generic shuffle vectors like 128-bit and 256-bit without having to working about handling masking.
llvm-svn: 289351
Both the (V)CVTDQ2PD (i32 to f64) and (V)CVTUDQ2PD (u32 to f64) conversion instructions are lossless and can be safely represented as generic __builtin_convertvector calls instead of x86 intrinsics without affecting final codegen.
This patch removes the clang builtins and their use in the headers - a future patch will deal with removing the llvm intrinsics.
This is an extension patch to D20528 which dealt with the equivalent sse/avx cases.
Differential Revision: https://reviews.llvm.org/D26686
llvm-svn: 287088
This is part of a set of changes to allow InstCombine in the backend to optimize variable shifts without having to know about masking.
llvm-svn: 286757
Summary: Inverting the mask argument does not reflect the intended semantics of the intrinsic.
Reviewers: igorb, delena
Subscribers: cfe-commits
Differential Revision: https://reviews.llvm.org/D26019
llvm-svn: 286733
Unfortunately, the backend currently doesn't fold masks into the instructions correctly when they come from these shufflevectors. I'll work on that in a future commit.
llvm-svn: 285667
Unfortunately, the backend currently doesn't fold masks into the instructions correctly when they come from these shufflevectors. I'll work on that in a future commit.
llvm-svn: 285540
After LGTM and Check-all
Vector-reduction arithmetic accepts vectors as inputs and produces
scalars as outputs.This class of vector operation forms the basis
of many scientific computations. In vector-reduction arithmetic,
the evaluation off is independent of the order of the input elements of V.
Reviewer: 1. craig.topper
2. igorb
Differential Revision: https://reviews.llvm.org/D25988
llvm-svn: 285493
Summary: The preserved input should be the first argument and the vector inputs should be in the same order as the intrinsics it is used to implement.
Reviewers: igorb, delena
Subscribers: cfe-commits
Differential Revision: https://reviews.llvm.org/D25902
llvm-svn: 285175
Committed after LGTM and check-all
Vector-reduction arithmetic accepts vectors as inputs and produces scalars as outputs.
This class of vector operation forms the basis of many scientific computations.
In vector-reduction arithmetic, the evaluation off is independent of the order of the input elements of V.
Used bisection method. At each step, we partition the vector with previous
step in half, and the operation is performed on its two halves.
This takes log2(n) steps where n is the number of elements in the vector.
Reviwer: 1. igorb
2. craig.topper
Differential Revision: https://reviews.llvm.org/D25527
llvm-svn: 285054
Committed after LGTM and check-all
Vector-reduction arithmetic accepts vectors as inputs and produces scalars as outputs.
This class of vector operation forms the basis of many scientific computations.
In vector-reduction arithmetic, the evaluation off is independent of the order of the input elements of V.
Used bisection method. At each step, we partition the vector with previous
step in half, and the operation is performed on its two halves.
This takes log2(n) steps where n is the number of elements in the vector.
Differential Revision: https://reviews.llvm.org/D25527
llvm-svn: 284963
Craig Topper