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Use the size of the widest type of the matrix multiplication operands The size of the operands type is the one of the parameters required to determine the BLIS micro-kernel. We get the size of the widest type of the matrix multiplication operands in case there are several different types. Reviewed-by: Michael Kruse <llvm@meinersbur.de> Differential Revision: https://reviews.llvm.org/D29269 llvm-svn: 294828
2017-02-11 15:00:05 +08:00
; RUN: opt %loadPolly -polly-opt-isl -polly-pattern-matching-based-opts=true \
; RUN: -polly-target-throughput-vector-fma=1 \
; RUN: -polly-target-latency-vector-fma=8 \
; RUN: -analyze -polly-ast -polly-target-1st-cache-level-associativity=8 \
; RUN: -polly-target-2nd-cache-level-associativity=8 \
; RUN: -polly-target-1st-cache-level-size=32768 \
; RUN: -polly-target-vector-register-bitwidth=256 \
; RUN: -polly-target-2nd-cache-level-size=262144 < %s \
; RUN: | FileCheck %s
;
; /* C := A * B + C */
; /* Elements of the matrices B, C have the double type. */
; /* Elements of the matrix A have the float type. */
; /* The type size of elements of the matrix multiplication operands is used
; to determine the parameters of the code produced by the optimization
; of the matrix multiplication (e.g. bounds of the loops of the loop
; nest, the innermost loop body). This test checks the form of
; the generated loop nest. See getMicroKernelParams and
; getMacroKernelParams from lib/Transform/ScheduleOptimizer.cpp
; for details. */
; for (i = 0; i < _PB_NI; i++)
; for (j = 0; j < _PB_NJ; j++)
; for (k = 0; k < _PB_NK; ++k)
; C[i][j] += A[i][k] * B[k][j];
;
; CHECK: // 1st level tiling - Tiles
; CHECK-NEXT: for (int c1 = 0; c1 <= 3; c1 += 1) {
; CHECK-NEXT: for (int c3 = 0; c3 <= 1023; c3 += 1)
; CHECK-NEXT: for (int c4 = 256 * c1; c4 <= 256 * c1 + 255; c4 += 1)
; CHECK-NEXT: CopyStmt_0(0, c3, c4);
; CHECK-NEXT: for (int c2 = 0; c2 <= 10; c2 += 1) {
; CHECK-NEXT: for (int c3 = 96 * c2; c3 <= min(1023, 96 * c2 + 95); c3 += 1)
; CHECK-NEXT: for (int c5 = 256 * c1; c5 <= 256 * c1 + 255; c5 += 1)
; CHECK-NEXT: CopyStmt_1(c3, 0, c5);
; CHECK-NEXT: // 1st level tiling - Points
; CHECK-NEXT: // Register tiling - Tiles
; CHECK-NEXT: for (int c3 = 0; c3 <= 127; c3 += 1)
; CHECK-NEXT: for (int c4 = 0; c4 <= min(23, -24 * c2 + 255); c4 += 1)
; CHECK-NEXT: for (int c5 = 0; c5 <= 255; c5 += 1) {
Disable the Loop Vectorizer in case of GEMM Currently, in case of GEMM and the pattern matching based optimizations, we use only the SLP Vectorizer out of two LLVM vectorizers. Since the Loop Vectorizer can get in the way of optimal code generation, we disable the Loop Vectorizer for the innermost loop using mark nodes and emitting the corresponding metadata. Reviewed-by: Tobias Grosser <tobias@grosser.es> Differential Revision: https://reviews.llvm.org/D36928 llvm-svn: 311473
2017-08-23 01:38:46 +08:00
; CHECK-NEXT: // Loop Vectorizer Disabled
Use the size of the widest type of the matrix multiplication operands The size of the operands type is the one of the parameters required to determine the BLIS micro-kernel. We get the size of the widest type of the matrix multiplication operands in case there are several different types. Reviewed-by: Michael Kruse <llvm@meinersbur.de> Differential Revision: https://reviews.llvm.org/D29269 llvm-svn: 294828
2017-02-11 15:00:05 +08:00
; CHECK-NEXT: // Register tiling - Points
; CHECK-NEXT: {
; CHECK-NEXT: Stmt_for_body6(96 * c2 + 4 * c4, 8 * c3, 256 * c1 + c5);
; CHECK-NEXT: Stmt_for_body6(96 * c2 + 4 * c4, 8 * c3 + 1, 256 * c1 + c5);
; CHECK-NEXT: Stmt_for_body6(96 * c2 + 4 * c4, 8 * c3 + 2, 256 * c1 + c5);
; CHECK-NEXT: Stmt_for_body6(96 * c2 + 4 * c4, 8 * c3 + 3, 256 * c1 + c5);
; CHECK-NEXT: Stmt_for_body6(96 * c2 + 4 * c4, 8 * c3 + 4, 256 * c1 + c5);
; CHECK-NEXT: Stmt_for_body6(96 * c2 + 4 * c4, 8 * c3 + 5, 256 * c1 + c5);
; CHECK-NEXT: Stmt_for_body6(96 * c2 + 4 * c4, 8 * c3 + 6, 256 * c1 + c5);
; CHECK-NEXT: Stmt_for_body6(96 * c2 + 4 * c4, 8 * c3 + 7, 256 * c1 + c5);
; CHECK-NEXT: Stmt_for_body6(96 * c2 + 4 * c4 + 1, 8 * c3, 256 * c1 + c5);
; CHECK-NEXT: Stmt_for_body6(96 * c2 + 4 * c4 + 1, 8 * c3 + 1, 256 * c1 + c5);
; CHECK-NEXT: Stmt_for_body6(96 * c2 + 4 * c4 + 1, 8 * c3 + 2, 256 * c1 + c5);
; CHECK-NEXT: Stmt_for_body6(96 * c2 + 4 * c4 + 1, 8 * c3 + 3, 256 * c1 + c5);
; CHECK-NEXT: Stmt_for_body6(96 * c2 + 4 * c4 + 1, 8 * c3 + 4, 256 * c1 + c5);
; CHECK-NEXT: Stmt_for_body6(96 * c2 + 4 * c4 + 1, 8 * c3 + 5, 256 * c1 + c5);
; CHECK-NEXT: Stmt_for_body6(96 * c2 + 4 * c4 + 1, 8 * c3 + 6, 256 * c1 + c5);
; CHECK-NEXT: Stmt_for_body6(96 * c2 + 4 * c4 + 1, 8 * c3 + 7, 256 * c1 + c5);
; CHECK-NEXT: Stmt_for_body6(96 * c2 + 4 * c4 + 2, 8 * c3, 256 * c1 + c5);
; CHECK-NEXT: Stmt_for_body6(96 * c2 + 4 * c4 + 2, 8 * c3 + 1, 256 * c1 + c5);
; CHECK-NEXT: Stmt_for_body6(96 * c2 + 4 * c4 + 2, 8 * c3 + 2, 256 * c1 + c5);
; CHECK-NEXT: Stmt_for_body6(96 * c2 + 4 * c4 + 2, 8 * c3 + 3, 256 * c1 + c5);
; CHECK-NEXT: Stmt_for_body6(96 * c2 + 4 * c4 + 2, 8 * c3 + 4, 256 * c1 + c5);
; CHECK-NEXT: Stmt_for_body6(96 * c2 + 4 * c4 + 2, 8 * c3 + 5, 256 * c1 + c5);
; CHECK-NEXT: Stmt_for_body6(96 * c2 + 4 * c4 + 2, 8 * c3 + 6, 256 * c1 + c5);
; CHECK-NEXT: Stmt_for_body6(96 * c2 + 4 * c4 + 2, 8 * c3 + 7, 256 * c1 + c5);
; CHECK-NEXT: Stmt_for_body6(96 * c2 + 4 * c4 + 3, 8 * c3, 256 * c1 + c5);
; CHECK-NEXT: Stmt_for_body6(96 * c2 + 4 * c4 + 3, 8 * c3 + 1, 256 * c1 + c5);
; CHECK-NEXT: Stmt_for_body6(96 * c2 + 4 * c4 + 3, 8 * c3 + 2, 256 * c1 + c5);
; CHECK-NEXT: Stmt_for_body6(96 * c2 + 4 * c4 + 3, 8 * c3 + 3, 256 * c1 + c5);
; CHECK-NEXT: Stmt_for_body6(96 * c2 + 4 * c4 + 3, 8 * c3 + 4, 256 * c1 + c5);
; CHECK-NEXT: Stmt_for_body6(96 * c2 + 4 * c4 + 3, 8 * c3 + 5, 256 * c1 + c5);
; CHECK-NEXT: Stmt_for_body6(96 * c2 + 4 * c4 + 3, 8 * c3 + 6, 256 * c1 + c5);
; CHECK-NEXT: Stmt_for_body6(96 * c2 + 4 * c4 + 3, 8 * c3 + 7, 256 * c1 + c5);
; CHECK-NEXT: }
; CHECK-NEXT: }
; CHECK-NEXT: }
; CHECK-NEXT: }
;
target datalayout = "e-m:e-i64:64-f80:128-n8:16:32:64-S128"
target triple = "x86_64-unknown-unknown"
; Function Attrs: noinline nounwind uwtable
define internal void @kernel_gemm(i32 %ni, i32 %nj, i32 %nk, double %alpha, double %beta, [1024 x double]* %C, [1024 x float]* %A, [1024 x double]* %B) #0 {
entry:
br label %entry.split
entry.split: ; preds = %entry
br label %for.cond1.preheader
for.cond1.preheader: ; preds = %for.inc20, %entry.split
%indvars.iv41 = phi i64 [ 0, %entry.split ], [ %indvars.iv.next42, %for.inc20 ]
br label %for.cond4.preheader
for.cond4.preheader: ; preds = %for.inc17, %for.cond1.preheader
%indvars.iv38 = phi i64 [ 0, %for.cond1.preheader ], [ %indvars.iv.next39, %for.inc17 ]
br label %for.body6
for.body6: ; preds = %for.body6, %for.cond4.preheader
%indvars.iv = phi i64 [ 0, %for.cond4.preheader ], [ %indvars.iv.next, %for.body6 ]
%arrayidx8 = getelementptr inbounds [1024 x float], [1024 x float]* %A, i64 %indvars.iv41, i64 %indvars.iv
%tmp = load float, float* %arrayidx8, align 4
%conv = fpext float %tmp to double
%arrayidx12 = getelementptr inbounds [1024 x double], [1024 x double]* %B, i64 %indvars.iv, i64 %indvars.iv38
%tmp1 = load double, double* %arrayidx12, align 8
%mul = fmul double %conv, %tmp1
%arrayidx16 = getelementptr inbounds [1024 x double], [1024 x double]* %C, i64 %indvars.iv41, i64 %indvars.iv38
%tmp2 = load double, double* %arrayidx16, align 8
%add = fadd double %tmp2, %mul
store double %add, double* %arrayidx16, align 8
%indvars.iv.next = add nuw nsw i64 %indvars.iv, 1
%exitcond = icmp ne i64 %indvars.iv.next, 1024
br i1 %exitcond, label %for.body6, label %for.inc17
for.inc17: ; preds = %for.body6
%indvars.iv.next39 = add nuw nsw i64 %indvars.iv38, 1
%exitcond40 = icmp ne i64 %indvars.iv.next39, 1024
br i1 %exitcond40, label %for.cond4.preheader, label %for.inc20
for.inc20: ; preds = %for.inc17
%indvars.iv.next42 = add nuw nsw i64 %indvars.iv41, 1
%exitcond43 = icmp ne i64 %indvars.iv.next42, 1024
br i1 %exitcond43, label %for.cond1.preheader, label %for.end22
for.end22: ; preds = %for.inc20
ret void
}