![SAXPY
void
simpleSaxpy(double*
x,
double*
y,
const
double
A,
const
size_t
num)
{
for(size_t
i
=
0;
i
<
num;
++i)
{
y[i]
=
a
*
x[i]
+
y[i];
}
}](https://image.slidesharecdn.com/gpgpu2-130606102328-phpapp02/85/GPU-100-ver-2013-20-320.jpg)
![SAXPY OpenMP +
AVX
void
avxSaxpy(const
double*
x,
double*
y,
const
double
a,
const
size_t
num)
{
__m256d
v_a
=
_mm256_set1_pd(a);
#pragma
omp
parallel
for
for(int
i
=
0
;
i
<
num
/
4;
++i)
{
__m256d
v_x0
=
_mm256_loadu_pd(&x[i
*
4]);
__m256d
v_y0
=
_mm256_loadu_pd(&y[i
*
4]);
__m256d
v01
=
_mm256_mul_pd(v_a,
v_x0);
__m256d
v02
=
_mm256_add_pd(v01,
v_y0);
_mm256_storeu_pd(&y[i
*
4],
v02);
}
}](https://image.slidesharecdn.com/gpgpu2-130606102328-phpapp02/85/GPU-100-ver-2013-21-320.jpg)
![SAXPY CUDA
__global__
void
cudaSaxpyKernel(const
double*
x,
double*
y,
const
double
a,
const
int
num_elements)
{
const
int
id
=
blockDim.x
*
blockIdx.x
+
threadIdx.x;
if(id
<
num_elements)
{
y[id]
=
a
*
x[id]
+
y[id];
}
}](https://image.slidesharecdn.com/gpgpu2-130606102328-phpapp02/85/GPU-100-ver-2013-22-320.jpg)
![SAXPY MIC
void
micSaxpy(const
double*
x,
double*
y,
const
double
a,
const
size_t
num)
{
__m512d
v_a
=
_mm512_set1_pd(a);
#pragma
omp
parallel
for
for(int
i
=
0;
i
<
num
/
8;
++i)
{
__m512d
v_x
=
_mm512_load_pd(&x[i
*
8]);
__m512d
v_y
=
_mm512_load_pd(&y[i
*
8]);
__m512d
res
=
_mm512_fmadd_pd(v_x,
v_a,
v_y);
_mm512_storenr_pd(&y[i
*
8],
res);
}
}](https://image.slidesharecdn.com/gpgpu2-130606102328-phpapp02/85/GPU-100-ver-2013-23-320.jpg)
![Histogram
void
simpleHistogram(const
unsigned
char*
src,
std::vector<int>&
dst,
const
size_t
width,
const
size_t
height)
{
//
grayscale
for(size_t
y
=
0;
y
<
height;
++y)
{
for(size_t
x
=
0;
x
<
width;
++x)
{
unsigned
char
val
=
src[y
*
width
+
x];
dst[val]++;
}
}
}](https://image.slidesharecdn.com/gpgpu2-130606102328-phpapp02/85/GPU-100-ver-2013-28-320.jpg)
![Histogram OpenMP
void
openMPHistogram(const
unsigned
char*
src,
std::vector<int>&
dst,
const
size_t
width,
const
size_t
height)
{
#pragma
omp
parallel
{
std::vector<int>
local_dst(256);
#pragma
omp
for
for(size_t
y
=
0;
y
<
height;
++y)
{
for(size_t
x
=
0;
x
<
width;
++x)
{
unsigned
char
val
=
src[y
*
width
+
x];
local_dst[val]++;
}
}
#pragma
omp
critical
{
for(size_t
i
=
0;
i
<
256;
++i)
{
dist[i]
+=
local_dist[i];
}
}
}
}](https://image.slidesharecdn.com/gpgpu2-130606102328-phpapp02/85/GPU-100-ver-2013-29-320.jpg)
![Histogram CUDA
__global__
void
histogram_cuda_kernel(const
unsigned
char*
src,
int*
dst,
const
unsigned
int
width,
const
unsigned
int
height,
const
unsigned
int
num_elements)
{
int
idx
=
blockDim.x
*
blockIdx.x
+
threadIdx.x;
int
x
=
idx
%
width;
int
y
=
idx
/
width;
if(idx
<
num_elements)
{
unsigned
char
val
=
src[y
*
width
+
x];
atomicAdd(&dst[val],
1);
}
}](https://image.slidesharecdn.com/gpgpu2-130606102328-phpapp02/85/GPU-100-ver-2013-30-320.jpg)
![Histogram MIC
void
micHistogram_240(const
unsigned
char*
src,
int*
dst,
const
size_t
width,
const
size_t
height)
{
#pragma
omp
parallel
num_threads(240)
{
const
size_t
thread_id
=
omp_get_thread_num();
const
size_t
num_threads
=
omp_get_num_threads();
size_t
local_height
=
height
/
num_threads;
local_height
+=
(thread_id
%
2)?
0
:
1;
const
size_t
offset
=
5
*
thread_id
-‐
(thread_id
/
2);
std::vector<int>
local_dst(256);
std::vector<unsigned
char>
local_src(local_height
*
width);
memcpy(&local_src[0],
&src[offset
*
width],
sizeof(unsigned
char)
*
local_height
*
width);
for(size_t
y
=
0;
y
<
local_height;
++y)
{
for(size_t
x
=
0;
x
<
width;
++x)
{
size_t
val
=
local_src[y
*
width
+
x];
local_dst[val]++;
}
}
#pragma
omp
critical
{
for(size_t
i
=
0;
i
<
256;
++i)
{
dst[i]
+=
local_dst[i];
}
}
}
}](https://image.slidesharecdn.com/gpgpu2-130606102328-phpapp02/85/GPU-100-ver-2013-32-320.jpg)
Uploaded byRyo Sakamoto
GPUが100倍速いという神話をぶち殺せたらいいな ver.2013
関東GPGPU勉強会#2 山田てるみ 「GPUが100倍速いという神話をぶち殺せたらいいな ver.2013 + Haswell and MIC fix」
GPUが100倍速いという神話をぶち殺せたらいいな ver.2013
- 1.
- 2. 自己紹介 • 山田てるみ • @telmin_orca •なんちゃってGPUプログラマ
- 3.
- 4. 2012∼2013 • Fermi ->Kepler http://en.wikipedia.org/wiki/File:Nvidia_logo.svg http://www.nvidia.co.jp/page/home.html
- 5.
- 6.
- 7.
- 8. Debunking the 100X GPUvs. CPU Myth GPUが100倍速いという神話をぶち殺す
- 9.
- 10. NVIDIA • Kepler Architecture •GK110 • SM -> SMX • 32(48) -> 192!! • 1.03TFLOPS -> 3.5TFLOPS!!
- 12. の造りし も の
- 14.
- 15. Xeon Phi • MICArchitecture • 60 Core / 4 Threads • 32KB / L1 cache • 512KB / L2 cache • 512-bit vector unit!
- 16. Debunking the 100X GPUvs. CPU Myth? ver.2013
- 17. 大事なこと • 以下の測定結果にはHost <->device間の データ転送時間は含まれていません •元論文に準拠しました • Haswellのデータも追加しました
- 18. SAXPY • y =Ax+y • かけてたす • 演算量少なすぎてメモリ律速
- 19.
- 20. SAXPY void simpleSaxpy(double* x, double* y, const double A, const size_t num) { for(size_t i = 0; i < num; ++i) { y[i] = a * x[i] + y[i]; } }
- 21. SAXPY OpenMP + AVX void avxSaxpy(const double* x, double* y, const double a, const size_t num) { __m256d v_a = _mm256_set1_pd(a); #pragma omp parallel for for(int i = 0 ; i < num / 4; ++i) { __m256d v_x0 = _mm256_loadu_pd(&x[i * 4]); __m256d v_y0 = _mm256_loadu_pd(&y[i * 4]); __m256d v01 = _mm256_mul_pd(v_a, v_x0); __m256d v02 = _mm256_add_pd(v01, v_y0); _mm256_storeu_pd(&y[i * 4], v02); } }
- 22. SAXPY CUDA __global__ void cudaSaxpyKernel(const double* x, double* y, const double a, const int num_elements) { const int id = blockDim.x * blockIdx.x + threadIdx.x; if(id < num_elements) { y[id] = a * x[id] + y[id]; } }
- 23. SAXPY MIC void micSaxpy(const double* x, double* y, const double a, const size_t num) { __m512d v_a = _mm512_set1_pd(a); #pragma omp parallel for for(int i = 0; i < num / 8; ++i) { __m512d v_x = _mm512_load_pd(&x[i * 8]); __m512d v_y = _mm512_load_pd(&y[i * 8]); __m512d res = _mm512_fmadd_pd(v_x, v_a, v_y); _mm512_storenr_pd(&y[i * 8], res); } }
- 24. SAXPY msec GFlops GB/s Corei7 2600K 14.0771.42071 17.0486 Corei7 4770K 12.448 1.606 19.279 Titan 0.134 141.461 848.763 XeonPhi 1.98 10.095 121.15
- 25.
- 26.
- 27. Histogram • 実験条件 • 1920x1080画像 •bin: 256
- 28. Histogram void simpleHistogram(const unsigned char* src, std::vector<int>& dst, const size_t width, const size_t height) { // grayscale for(size_t y = 0; y < height; ++y) { for(size_t x = 0; x < width; ++x) { unsigned char val = src[y * width + x]; dst[val]++; } } }
- 29. Histogram OpenMP void openMPHistogram(const unsigned char* src, std::vector<int>& dst, const size_t width, const size_t height) { #pragma omp parallel { std::vector<int> local_dst(256); #pragma omp for for(size_t y = 0; y < height; ++y) { for(size_t x = 0; x < width; ++x) { unsigned char val = src[y * width + x]; local_dst[val]++; } } #pragma omp critical { for(size_t i = 0; i < 256; ++i) { dist[i] += local_dist[i]; } } } }
- 30. Histogram CUDA __global__ void histogram_cuda_kernel(const unsigned char* src, int* dst, const unsigned int width, const unsigned int height, const unsigned int num_elements) { int idx = blockDim.x * blockIdx.x + threadIdx.x; int x = idx % width; int y = idx / width; if(idx < num_elements) { unsigned char val = src[y * width + x]; atomicAdd(&dst[val], 1); } }
- 31. Histogram msec MPixel /s Corei7 2600K 0.734 2823.8 Corei7 4770K 0.273 3370.66 Titan 0.0816 25381.3 XeonPhi 117.5 17.6463
- 32. Histogram MIC void micHistogram_240(const unsigned char* src, int* dst, const size_t width, const size_t height) { #pragma omp parallel num_threads(240) { const size_t thread_id = omp_get_thread_num(); const size_t num_threads = omp_get_num_threads(); size_t local_height = height / num_threads; local_height += (thread_id % 2)? 0 : 1; const size_t offset = 5 * thread_id -‐ (thread_id / 2); std::vector<int> local_dst(256); std::vector<unsigned char> local_src(local_height * width); memcpy(&local_src[0], &src[offset * width], sizeof(unsigned char) * local_height * width); for(size_t y = 0; y < local_height; ++y) { for(size_t x = 0; x < width; ++x) { size_t val = local_src[y * width + x]; local_dst[val]++; } } #pragma omp critical { for(size_t i = 0; i < 256; ++i) { dst[i] += local_dst[i]; } } } }
- 33. Histogram msec MPixel /s Corei7 2600K 0.734 2823.8 Corei7 4770K 0.273 3370.66 Titan 0.0816 25381.3 XeonPhi 2.074 999.806
- 34. NL-means • Non-local Algorithm •A non-local algorithm for image denoising • http://bengal.missouri.edu/~kes25c/nl2.pdf • バイラテラルフィルタの親戚
- 35. NL-means • エッジキープ型のフィルタ • ノイズを除去しつつもボケにくい！ •Aviutilとかにプラグインがある
- 36.
- 37.
- 38. NL-means • 実験条件 • 1920x1080画像 •Window size : 7x7 • Template size : 3x3
- 39. NL-means sec FPS Corei7 2600K2.086 0.479 Corei7 4770K 2.29 0.436 Titan 0.05826 17.16 XeonPhi 1.217 0.822
- 40. Aobench • Ambient Occlution •前回もやった • Intelもサンプルに 使用 http://software.intel.com/en-us/articles/data-and-thread-parallelism/
- 41. Aobench • 実験条件 • 512x512画像 •NSUBSAMPLE: 2 • NTHETA: 16 • NPHI: 16
- 42. Aobench sec FPS Corei7 2600K1.556 0.642 Corei7 4770K 1.448 0.6905 Titan 0.0162 61.71 XeonPhi 0.9 1.11
- 43. 0.00%$ 2000.00%$ 4000.00%$ 6000.00%$ 8000.00%$ 10000.00%$ 12000.00%$ SAXPY$ Histogram$ NL:means$Aobench$ Core$i7$2600K$ Core$i7$4770K$ Titan$ XeonPhi$
- 44.