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1 change: 1 addition & 0 deletions MicroBenchmarks/LoopVectorization/CMakeLists.txt
Original file line number Diff line number Diff line change
Expand Up @@ -10,6 +10,7 @@ endif()
llvm_test_run()

llvm_test_executable(LoopVectorizationBenchmarks
ControlFlowVectorization.cpp
ConditionalScalarAssignment.cpp
main.cpp
MathFunctions.cpp
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177 changes: 177 additions & 0 deletions MicroBenchmarks/LoopVectorization/ControlFlowVectorization.cpp
Original file line number Diff line number Diff line change
@@ -0,0 +1,177 @@
#include <iostream>
#include <memory>
#include <random>

#include "benchmark/benchmark.h"

#define ITERATIONS 100000

template <typename T> using CFVFunc = void (*)(T *, unsigned);
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might be good to use a more descriptive name or add a comment

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Update to ControlFlowLoopFunc. Thanks!


// Define conditional increment loop with given stride.
#define DEF_COND_INC_LOOP(name, stride) \
template <typename T> \
__attribute__((noinline)) static void run_##name##_autovec(T *A, \
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What is the meaning of this benchmark ? Just track current state of cf vectorization of novec and autovec or help to identify better LMUL to vectorize the loop ? If latter, it does make sense to add similar functions with forced vectorization for default LMUL and specified LMULs

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IIUC, this benchmark serves as a test suite for other targets to measure the performance impact of enabling control-flow vectorization.

I've updated the PR description to make it more clear.

unsigned N) { \
for (unsigned i = 0; i < N; i++) { \
if (i % stride == 0) { \
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Stride may be a bit mis-leading here, maybe to be confused with the stride by which pointers increment? stride here effectively controls how frequently the condition executes, right?

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Yes. Update the naming to the branch_every_N. Thanks!

A[i] = A[i] + 1; \
} \
} \
} \
template <typename T> \
__attribute__((noinline)) static void run_##name##_novec(T *A, unsigned N) { \
_Pragma("clang loop vectorize(disable) interleave(disable)") \
for (unsigned i = 0; i < N; i++) { \
if (i % stride == 0) { \
A[i] = A[i] + 1; \
} \
} \
}

// Define conditional increment by value loop.
#define DEF_COND_INC_VALUE_LOOP(name, marker) \
template <typename T> \
__attribute__((noinline)) static void run_##name##_autovec(T *A, \
unsigned N) { \
for (unsigned i = 0; i < N; i++) { \
if (A[i] == marker) { \
A[i] = A[i] + 1; \
} \
} \
} \
template <typename T> \
__attribute__((noinline)) static void run_##name##_novec(T *A, unsigned N) { \
_Pragma("clang loop vectorize(disable) interleave(disable)") \
for (unsigned i = 0; i < N; i++) { \
if (A[i] == marker) { \
A[i] = A[i] + 1; \
} \
} \
}

// Define unconditional increment loop.
template <typename T>
__attribute__((noinline)) static void run_uncond_inc_autovec(T *A, unsigned N) {
for (unsigned i = 0; i < N; i++) {
A[i] = A[i] + 1;
}
}

template <typename T>
__attribute__((noinline)) static void run_uncond_inc_novec(T *A, unsigned N) {
_Pragma("clang loop vectorize(disable) interleave(disable)")
for (unsigned i = 0; i < N; i++) {
A[i] = A[i] + 1;
}
}

// Define loops with different strides.
// Stride 16 usually big enough to accross single vector which can test if
// control-flow-vectorization is profitable on these loops.
DEF_COND_INC_LOOP(cond_inc_stride_16, 16)
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This variant roughly executes the conditional code every 16 iterations, right?

Would be good to also add variations where conditional code executes more frequently, including extreme case (every iteration, every other iteration)?

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Yes.

Add the cases that branch will taken for every (1, 2, 4, 8) iters. Thanks!

DEF_COND_INC_LOOP(cond_inc_stride_32, 32)
DEF_COND_INC_LOOP(cond_inc_stride_64, 64)
DEF_COND_INC_LOOP(cond_inc_stride_128, 128)

// Conditional increment by value.
DEF_COND_INC_VALUE_LOOP(cond_inc_by_value, 40)

// Initialize array with random numbers.
template <typename T> static void init_data(T *A) {
std::uniform_int_distribution<T> dist(0, 100);
std::mt19937 rng(12345);
for (unsigned i = 0; i < ITERATIONS; i++) {
A[i] = dist(rng);
}
}

// Benchmark vectorized version.
template <typename T>
static void __attribute__((always_inline))
benchmark_cfv_autovec(benchmark::State &state, CFVFunc<T> VecFn,
CFVFunc<T> NoVecFn) {
std::unique_ptr<T[]> A(new T[ITERATIONS]);
std::unique_ptr<T[]> A_vec(new T[ITERATIONS]);
std::unique_ptr<T[]> A_novec(new T[ITERATIONS]);
init_data(&A[0]);

#ifdef BENCH_AND_VERIFY
// Verify the vectorized and scalar versions produce the same results.
{
std::copy(&A[0], &A[0] + ITERATIONS, &A_vec[0]);
std::copy(&A[0], &A[0] + ITERATIONS, &A_novec[0]);
VecFn(&A_vec[0], ITERATIONS);
NoVecFn(&A_novec[0], ITERATIONS);
for (unsigned i = 0; i < ITERATIONS; i++) {
if (A_vec[i] != A_novec[i]) {
std::cerr << "ERROR: vectorization result different at index " << i
<< "; " << A_vec[i] << " != " << A_novec[i] << "\n";
exit(1);
}
}
}
#endif

for (auto _ : state) {
std::copy(&A[0], &A[0] + ITERATIONS, &A_vec[0]);
VecFn(&A_vec[0], ITERATIONS);
benchmark::DoNotOptimize(A_vec);
benchmark::ClobberMemory();
}
}

// Benchmark version with vectorization disabled.
template <typename T>
static void __attribute__((always_inline))
benchmark_cfv_novec(benchmark::State &state, CFVFunc<T> NoVecFn) {
std::unique_ptr<T[]> A(new T[ITERATIONS]);
std::unique_ptr<T[]> A_work(new T[ITERATIONS]);
init_data(&A[0]);

for (auto _ : state) {
std::copy(&A[0], &A[0] + ITERATIONS, &A_work[0]);
NoVecFn(&A_work[0], ITERATIONS);
benchmark::DoNotOptimize(A_work);
benchmark::ClobberMemory();
}
}

#define BENCHMARK_CFV_CASE(name, ty) \
void BENCHMARK_##name##_autovec_##ty##_(benchmark::State &state) { \
benchmark_cfv_autovec<ty>(state, run_##name##_autovec, run_##name##_novec);\
} \
BENCHMARK(BENCHMARK_##name##_autovec_##ty##_)->Unit(benchmark::kNanosecond); \
\
void BENCHMARK_##name##_novec_##ty##_(benchmark::State &state) { \
benchmark_cfv_novec<ty>(state, run_##name##_novec); \
} \
BENCHMARK(BENCHMARK_##name##_novec_##ty##_)->Unit(benchmark::kNanosecond);

// Unconditional increment benchmark.
#define BENCHMARK_UNCOND_CASE(ty) \
void BENCHMARK_uncond_inc_autovec_##ty##_(benchmark::State &state) { \
benchmark_cfv_autovec<ty>(state, run_uncond_inc_autovec, \
run_uncond_inc_novec); \
} \
BENCHMARK(BENCHMARK_uncond_inc_autovec_##ty##_) \
->Unit(benchmark::kNanosecond); \
\
void BENCHMARK_uncond_inc_novec_##ty##_(benchmark::State &state) { \
benchmark_cfv_novec<ty>(state, run_uncond_inc_novec); \
} \
BENCHMARK(BENCHMARK_uncond_inc_novec_##ty##_)->Unit(benchmark::kNanosecond);

// Add benchmarks for all variants.
#define ADD_CFV_BENCHMARKS(ty) \
BENCHMARK_UNCOND_CASE(ty) \
BENCHMARK_CFV_CASE(cond_inc_stride_16, ty) \
BENCHMARK_CFV_CASE(cond_inc_stride_32, ty) \
BENCHMARK_CFV_CASE(cond_inc_stride_64, ty) \
BENCHMARK_CFV_CASE(cond_inc_stride_128, ty) \
BENCHMARK_CFV_CASE(cond_inc_by_value, ty)

ADD_CFV_BENCHMARKS(int64_t)
ADD_CFV_BENCHMARKS(int32_t)
ADD_CFV_BENCHMARKS(int16_t)