我正在尝试使用分配设置 3D 循环
C(i,j,k) = A(i,j,k) + B(i,j,k)
在我的 GPU 上使用 Python。这是我的 GPU:
http://www.geforce.com/hardware/desktop-gpus/geforce-gt-520/specifications
我正在查看/比较的来源是:
http://nbviewer.ipython.org/gist/harrism/f5707335f40af9463c43
我可能导入了不必要的模块。这是我的代码:
import numpy as np
import numbapro
import numba
import math
from timeit import default_timer as timer
from numbapro import cuda
from numba import *
@autojit
def myAdd(a, b):
return a+b
myAdd_gpu = cuda.jit(restype=f8, argtypes=[f8, f8], device=True)(myAdd)
@cuda.jit(argtypes=[float32[:,:,:], float32[:,:,:], float32[:,:,:]])
def myAdd_kernel(a, b, c):
tx = cuda.threadIdx.x
ty = cuda.threadIdx.y
tz = cuda.threadIdx.z
bx = cuda.blockIdx.x
by = cuda.blockIdx.y
bz = cuda.blockIdx.z
bw = cuda.blockDim.x
bh = cuda.blockDim.y
bd = cuda.blockDim.z
i = tx + bx * bw
j = ty + by * bh
k = tz + bz * bd
if i >= c.shape[0]:
return
if j >= c.shape[1]:
return
if k >= c.shape[2]:
return
for i in xrange(0,c.shape[0]):
for j in xrange(0,c.shape[1]):
for k in xrange(0,c.shape[2]):
# c[i,j,k] = a[i,j,k] + b[i,j,k]
c[i,j,k] = myAdd_gpu(a[i,j,k],b[i,j,k])
def main():
my_gpu = numba.cuda.get_current_device()
print "Running on GPU:", my_gpu.name
cores_per_capability = {1: 8,2: 32,3: 192,}
cc = my_gpu.compute_capability
print "Compute capability: ", "%d.%d" % cc, "(Numba requires >= 2.0)"
majorcc = cc[0]
print "Number of streaming multiprocessor:", my_gpu.MULTIPROCESSOR_COUNT
cores_per_multiprocessor = cores_per_capability[majorcc]
print "Number of cores per mutliprocessor:", cores_per_multiprocessor
total_cores = cores_per_multiprocessor * my_gpu.MULTIPROCESSOR_COUNT
print "Number of cores on GPU:", total_cores
N = 100
thread_ct = my_gpu.WARP_SIZE
block_ct = int(math.ceil(float(N) / thread_ct))
print "Threads per block:", thread_ct
print "Block per grid:", block_ct
a = np.ones((N,N,N), dtype = np.float32)
b = np.ones((N,N,N), dtype = np.float32)
c = np.zeros((N,N,N), dtype = np.float32)
start = timer()
cg = cuda.to_device(c)
myAdd_kernel[block_ct, thread_ct](a,b,cg)
cg.to_host()
dt = timer() - start
print "Wall clock time with GPU in %f s" % dt
print 'c[:3,:,:] = ' + str(c[:3,1,1])
print 'c[-3:,:,:] = ' + str(c[-3:,1,1])
if __name__ == '__main__':
main()
我运行它的结果如下:
Running on GPU: GeForce GT 520
Compute capability: 2.1 (Numba requires >= 2.0)
Number of streaming multiprocessor: 1
Number of cores per mutliprocessor: 32
Number of cores on GPU: 32
Threads per block: 32
Block per grid: 4
Wall clock time with GPU in 1.104860 s
c[:3,:,:] = [ 2. 2. 2.]
c[-3:,:,:] = [ 2. 2. 2.]
当我运行源代码中的示例时,我看到了显着的加速。我认为我的示例运行不正常,因为挂钟时间比我预期的要长得多。我主要从第一个示例链接中的“使用 cuda python 实现更大的加速”部分对此进行了建模。
我相信我已经正确且安全地建立了索引。也许问题出在我的blockdim上?还是格子?或者我的 GPU 使用了错误的类型。我想我读到它们一定是某种类型。我对此很陌生,所以这个问题很可能是微不足道的!
非常感谢任何和所有帮助!