155 lines
6.1 KiB
C
155 lines
6.1 KiB
C
#include "StgSharpNative.h"
|
|
#include "sn_intrinsic.h"
|
|
|
|
#if defined(_MSC_VER) && !defined(__clang__)
|
|
#include <intrin.h>
|
|
#include <isa_availability.h>
|
|
#else
|
|
#include <cpuid.h>
|
|
#endif // _MSC_VER
|
|
|
|
PRIVATE void load_intrinsic_sse(sn_intrinsic *context);
|
|
PRIVATE void load_intrinsic_avx(sn_intrinsic *context);
|
|
PRIVATE void load_intrinsic_512(sn_intrinsic *context);
|
|
|
|
PRIVATE int check_isa(most_advanced_instruction instruction_family)
|
|
{
|
|
if (instruction_family != NEON) {
|
|
#if defined(_MSC_VER) && !defined(__clang__)
|
|
return __check_isa_support(instruction_family, 0);
|
|
#else
|
|
switch (instruction_family) {
|
|
case SSE:
|
|
return __builtin_cpu_supports("sse4.2");
|
|
case AVX256:
|
|
return __builtin_cpu_supports("avx2");
|
|
case AVX512:
|
|
return __builtin_cpu_supports("avx512f");
|
|
default:
|
|
return 0;
|
|
}
|
|
#endif // _MSC_VER
|
|
}
|
|
|
|
#if defined(__ARM_NEON) || defined(__ARM_NEON__)
|
|
return 1;
|
|
#else
|
|
return 0;
|
|
#endif
|
|
}
|
|
|
|
SN_API int load_intrinsic_function(void *intrinsic_context)
|
|
{
|
|
sn_null_assert(intrinsic_context);
|
|
|
|
sn_intrinsic *context = (sn_intrinsic *)intrinsic_context;
|
|
|
|
if (check_isa(AVX512)) {
|
|
load_intrinsic_512(context);
|
|
return 3;
|
|
} else if (check_isa(AVX256)) {
|
|
load_intrinsic_avx(context);
|
|
return 2;
|
|
} else if (check_isa(SSE)) {
|
|
load_intrinsic_sse(context);
|
|
return 1;
|
|
} else if (check_isa(NEON)) {
|
|
return -1;
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
|
|
void load_intrinsic_sse(sn_intrinsic *context)
|
|
{
|
|
context->city_hash_simplify = city_hash_simplify_sse;
|
|
context->factorial_simd = factorial_simd_sse;
|
|
context->index_pair = index_pair_sse;
|
|
|
|
context->mat[F32].buffer_add = BUF_PROC(float, sse, add);
|
|
context->mat[F32].buffer_fill = BUF_PROC(float, sse, fill);
|
|
context->mat[F32].buffer_scalar_mul = BUF_PROC(float, sse, scalar_mul);
|
|
context->mat[F32].buffer_sub = BUF_PROC(float, sse, sub);
|
|
context->mat[F32].buffer_transpose = KER_PROC(float, sse, transpose);
|
|
context->mat[F32].build_panel = NULL;
|
|
context->mat[F32].clear_panel = PNL_PROC(float, sse, clear);
|
|
context->mat[F32].panel_fma = PNL_PROC(float, sse, fma);
|
|
context->mat[F32].pivot = pivot_float;
|
|
context->mat[F32].store_panel = NULL;
|
|
|
|
context->mat[F64].buffer_add = BUF_PROC(double, sse, add);
|
|
context->mat[F64].buffer_fill = BUF_PROC(double, sse, fill);
|
|
context->mat[F64].buffer_scalar_mul = BUF_PROC(double, sse, scalar_mul);
|
|
context->mat[F64].buffer_sub = BUF_PROC(double, sse, sub);
|
|
context->mat[F64].buffer_transpose = NULL;
|
|
context->mat[F64].build_panel = NULL;
|
|
context->mat[F64].clear_panel = PNL_PROC(double, sse, clear);
|
|
context->mat[F64].panel_fma = NULL;
|
|
context->mat[F64].pivot = NULL;
|
|
context->mat[F64].store_panel = NULL;
|
|
|
|
context->vec_f32_normalize_3 = f32_normalize;
|
|
}
|
|
|
|
void load_intrinsic_avx(sn_intrinsic *context)
|
|
{
|
|
context->city_hash_simplify = city_hash_simplify_sse;
|
|
context->factorial_simd = factorial_simd_sse;
|
|
context->index_pair = index_pair_sse;
|
|
|
|
context->mat[F32].buffer_add = BUF_PROC(float, avx, add);
|
|
context->mat[F32].buffer_fill = BUF_PROC(float, avx, fill);
|
|
context->mat[F32].buffer_scalar_mul = BUF_PROC(float, avx, scalar_mul);
|
|
context->mat[F32].buffer_sub = BUF_PROC(float, avx, sub);
|
|
context->mat[F32].buffer_transpose = KER_PROC(float, avx, transpose);
|
|
context->mat[F32].build_panel = BUILD_PANEL(avx, float);
|
|
context->mat[F32].clear_panel = PNL_PROC(float, avx, clear);
|
|
context->mat[F32].panel_fma = PNL_PROC(float, avx, fma);
|
|
context->mat[F32].pivot = pivot_float;
|
|
context->mat[F32].store_panel = STORE_PANEL(avx, float);
|
|
|
|
context->mat[F64].buffer_add = BUF_PROC(double, avx, add);
|
|
context->mat[F64].buffer_fill = BUF_PROC(double, avx, fill);
|
|
context->mat[F64].buffer_scalar_mul = BUF_PROC(double, avx, scalar_mul);
|
|
context->mat[F64].buffer_sub = BUF_PROC(double, avx, sub);
|
|
context->mat[F64].buffer_transpose = NULL;
|
|
context->mat[F64].build_panel = NULL;
|
|
context->mat[F64].clear_panel = PNL_PROC(double, avx, clear);
|
|
context->mat[F64].panel_fma = NULL;
|
|
context->mat[F64].pivot = NULL;
|
|
context->mat[F64].store_panel = NULL;
|
|
|
|
context->vec_f32_normalize_3 = f32_normalize;
|
|
}
|
|
|
|
void load_intrinsic_512(sn_intrinsic *context)
|
|
{
|
|
context->city_hash_simplify = city_hash_simplify_sse;
|
|
context->factorial_simd = factorial_simd_sse;
|
|
context->index_pair = index_pair_sse;
|
|
|
|
context->mat[F32].buffer_add = BUF_PROC(float, 512, add);
|
|
context->mat[F32].buffer_fill = BUF_PROC(float, 512, fill);
|
|
context->mat[F32].buffer_scalar_mul = BUF_PROC(float, 512, scalar_mul);
|
|
context->mat[F32].buffer_sub = BUF_PROC(float, 512, sub);
|
|
context->mat[F32].buffer_transpose = KER_PROC(float, 512, transpose);
|
|
context->mat[F32].build_panel = BUILD_PANEL(512, float);
|
|
context->mat[F32].clear_panel = PNL_PROC(float, 512, clear);
|
|
context->mat[F32].panel_fma = PNL_PROC(float, 512, fma);
|
|
context->mat[F32].pivot = pivot_float;
|
|
context->mat[F32].store_panel = STORE_PANEL(512, float);
|
|
|
|
context->mat[F64].buffer_add = BUF_PROC(double, 512, add);
|
|
context->mat[F64].buffer_fill = BUF_PROC(double, 512, fill);
|
|
context->mat[F64].buffer_scalar_mul = BUF_PROC(double, 512, scalar_mul);
|
|
context->mat[F64].buffer_sub = BUF_PROC(double, 512, sub);
|
|
context->mat[F64].buffer_transpose = NULL;
|
|
context->mat[F64].build_panel = BUILD_PANEL(512, double);
|
|
context->mat[F64].clear_panel = PNL_PROC(double, 512, clear);
|
|
context->mat[F64].panel_fma = NULL;
|
|
context->mat[F64].pivot = NULL;
|
|
context->mat[F64].store_panel = STORE_PANEL(512, double);
|
|
|
|
context->vec_f32_normalize_3 = f32_normalize;
|
|
}
|