我正在尝试使用cuda \ numba加快python代码的速度。该代码适用于大型的复数，浮点数和整数数组。我在这里同时包括了python版本和numba-cuda版本。 numba-cuda版本无法编译。
我尝试将复数计算作为单独的实数和虚数进行，尽管我可能会遇到复杂格式。
Calling python function...
---- 0.000344038009644 seconds for python ----:
Traceback (most recent call last):
File "GPU_Fake_PA_partial.py", line 82, in <module>
gpu_data_sum = calculate_cuda (data, data_index, coef)
File "GPU_Fake_PA_partial.py", line 44, in calculate_cuda
cuda_kernel[blockspergrid, threadsperblock](d_npart, d_npts, d_data, d_data_index, d_coef, d_datasum, d_tmp)
File "/disk/home/ajooya/software/venv/lib/python2.7/site-packages/numba/cuda/compiler.py", line 765, in __call__
kernel = self.specialize(*args)
File "/disk/home/ajooya/software/venv/lib/python2.7/site-packages/numba/cuda/compiler.py", line 776, in specialize
kernel = self.compile(argtypes)
File "/disk/home/ajooya/software/venv/lib/python2.7/site-packages/numba/cuda/compiler.py", line 792, in compile
**self.targetoptions)
File "/disk/home/ajooya/software/venv/lib/python2.7/site-packages/numba/compiler_lock.py", line 32, in _acquire_compile_lock
return func(*args, **kwargs)
File "/disk/home/ajooya/software/venv/lib/python2.7/site-packages/numba/cuda/compiler.py", line 62, in compile_kernel
cres = compile_cuda(pyfunc, types.void, args, debug=debug, inline=inline)
File "/disk/home/ajooya/software/venv/lib/python2.7/site-packages/numba/compiler_lock.py", line 32, in _acquire_compile_lock
return func(*args, **kwargs)
File "/disk/home/ajooya/software/venv/lib/python2.7/site-packages/numba/cuda/compiler.py", line 51, in compile_cuda
locals={})
File "/disk/home/ajooya/software/venv/lib/python2.7/site-packages/numba/compiler.py", line 926, in compile_extra
return pipeline.compile_extra(func)
File "/disk/home/ajooya/software/venv/lib/python2.7/site-packages/numba/compiler.py", line 374, in compile_extra
return self._compile_bytecode()
File "/disk/home/ajooya/software/venv/lib/python2.7/site-packages/numba/compiler.py", line 857, in _compile_bytecode
return self._compile_core()
File "/disk/home/ajooya/software/venv/lib/python2.7/site-packages/numba/compiler.py", line 844, in _compile_core
res = pm.run(self.status)
File "/disk/home/ajooya/software/venv/lib/python2.7/site-packages/numba/compiler_lock.py", line 32, in _acquire_compile_lock
return func(*args, **kwargs)
File "/disk/home/ajooya/software/venv/lib/python2.7/site-packages/numba/compiler.py", line 255, in run
raise patched_exception
numba.errors.TypingError: Failed in nopython mode pipeline (step: nopython frontend)
Invalid use of Function(<built-in function lt>) with argument(s) of type(s): (int32, array(int64, 1d, A))
Known signatures:
* (bool, bool) -> bool
* (int8, int8) -> bool
* (int16, int16) -> bool
* (int32, int32) -> bool
* (int64, int64) -> bool
* (uint8, uint8) -> bool
* (uint16, uint16) -> bool
* (uint32, uint32) -> bool
* (uint64, uint64) -> bool
* (float32, float32) -> bool
* (float64, float64) -> bool
* parameterized
In definition 0:
All templates rejected with literals.
In definition 1:
All templates rejected without literals.
In definition 2:
All templates rejected with literals.
In definition 3:
All templates rejected without literals.
In definition 4:
All templates rejected with literals.
In definition 5:
All templates rejected without literals.
In definition 6:
All templates rejected with literals.
In definition 7:
All templates rejected without literals.
This error is usually caused by passing an argument of a type that is unsupported by the named function.
[1] During: typing of intrinsic-call at GPU_Fake_PA_partial.py (15)
File "GPU_Fake_PA_partial.py", line 15:
def cuda_kernel (d_npart, d_npts, d_data, d_data_index, d_coef, d_datasum, d_tmp):
<source elided>
row, col = cuda.grid(2)
if row < d_npart and col < d_npts :
运行代码时出现此错误：
require('./js/my-module/my-module-one')我们非常感谢您的帮助。
答案 0 :(得分：1)
您的代码存在多种问题。我可能不会涵盖所有内容，因此请与您的代码进行比较。
numpy数组方法（例如np.sum()）cannot be used in numba CUDA kernels。
npart，npts）的标量不需要，也不应像.to_device()那样进行数组处理。只需按原样使用它们即可。这是您显示python错误的原因：
Invalid use of Function(<built-in function lt>) with argument(s) of type(s): (int32, array(int64, 1d, A))
您的内核不必要地复杂。基本上，您正在执行已根据索引模式进行置换并乘以系数数组的矩阵的列总和。我们可以为每个线程执行一个循环。
对于上述实现，我们不需要二维线程网格。
您发布的代码中存在缩进问题。
出于演示目的，我将数据集的大小从1000列减少到15列。这是解决上述问题的示例：
$ cat t31.py
import numba as nb
import numpy as np
from numba import cuda
import time
import math
def random_choice_noreplace(m,n, axis=-1):
# m, n are the number of rows, cols of output
return np.random.rand(m,n).argsort(axis=axis)
@cuda.jit
def cuda_kernel (npart, npts, d_data, d_data_index, d_coef, d_datasum):
col = cuda.grid(1)
if col < npts:
temp = 0
for i in range(npart):
temp += d_data[d_data_index[i, col]] * d_coef[i, col]
d_datasum[col] = temp
def calculate_cuda (data, data_index, coef):
npart, npts = data_index.shape
# arrays to copy to GPU memory =====================================
d_data = cuda.to_device(data)
d_data_imag = cuda.to_device(data_imag)
d_data_index = cuda.to_device(data_index)
d_coef = cuda.to_device(coef)
d_datasum = cuda.device_array(npts, np.complex64)
threadsperblock = 64
blockspergrid = int(math.ceil(npts / threadsperblock))+1
cuda_kernel[blockspergrid, threadsperblock](npart, npts, d_data, d_data_index, d_coef, d_datasum)
# Copy data from GPU to CPU ========================================
final_data_sum = d_datasum.copy_to_host()
return final_data_sum
def calculate_python (data, data_index, coef):
npart, npts = data_index.shape
data_sum = np.zeros(npts, dtype=np.complex64)
tmp = np.zeros(npts, dtype=np.complex64)
print(" Calling python function...")
for i in range(npart):
tmp[:] = data[data_index[i]]
data_sum += tmp * coef[i]
return data_sum
if __name__ == '__main__':
rows = 31
cols = 15
data_size = rows * cols
data_real = np.random.randn(data_size).astype(np.float32)
data_imag = np.random.randn(data_size).astype(np.float32)
data = data_real + 1j * data_imag
coef = np.random.randn(rows, cols)
data_index = random_choice_noreplace(rows, cols)
start_time = time.time()
gpu_data_sum_python = calculate_python (data, data_index, coef)
python_time = time.time() - start_time #print("gpu c : ", c_gpu)
print("---- %s seconds for python ----:" % (python_time))
print(gpu_data_sum_python)
start_time = time.time()
gpu_data_sum = calculate_cuda (data, data_index, coef)
gpu_time = time.time() - start_time
print("---- %s seconds for gpu ----:" % (gpu_time))
print(gpu_data_sum)
$ python t31.py
Calling python function...
---- 0.000281095504761 seconds for python ----:
[-1.10292518+0.90700358j 2.67771578+2.47935939j -5.22553015-2.22675705j
-3.55810285+2.39755774j 4.11441088-3.89396238j -2.70894790-0.75690132j
3.24859619+0.65993834j 1.05531025+2.3959775j -4.27368307+1.6297332j
0.17896785-7.0437355j -6.31506491+6.22674656j -1.85534143-6.08459902j
0.40037563+6.33309507j -1.71916604-0.55055946j 0.49263301+1.08690035j]
---- 0.593510866165 seconds for gpu ----:
[-1.10292506+0.9070037j 2.67771506+2.47935939j -5.22553062-2.22675681j
-3.55810285+2.39755821j 4.11441135-3.89396238j -2.70894790-0.75690138j
3.24859619+0.65993822j 1.05531013+2.39597774j -4.27368307+1.62973344j
0.17896791-7.0437355j -6.31506491+6.22674656j -1.85534155-6.08459902j
0.40037528+6.33309603j -1.71916604-0.55055946j 0.49263287+1.08690035j]
$
请注意，在某些情况下，主机和设备的计算结果在数字上会有细微的差异，从小数点后第六位开始。我将此归因于主机和设备代码之间可能的计算顺序差异，并结合了float32（或complex64）numpy数据类型的限制。
由于您的代码内置了计时功能，因此您可能会对性能感兴趣。对于numba python，我推荐一种典型的基准测试实践，它不是衡量第一次运行，而是衡量第二次运行。这样避免了一次性开销进入测量。此外，我们希望选择比15列大得多的数据集大小，以使GPU有足够的工作量来摊销各种成本。经过这些修改，下面的基准测试表明此代码中的GPU版本可以比此代码中的CPU版本更快：
$ cat t31.py
import numba as nb
import numpy as np
from numba import cuda
import time
import math
def random_choice_noreplace(m,n, axis=-1):
# m, n are the number of rows, cols of output
return np.random.rand(m,n).argsort(axis=axis)
@cuda.jit
def cuda_kernel (npart, npts, d_data, d_data_index, d_coef, d_datasum):
col = cuda.grid(1)
if col < npts:
temp = 0
for i in range(npart):
temp += d_data[d_data_index[i, col]] * d_coef[i, col]
d_datasum[col] = temp
def calculate_cuda (data, data_index, coef):
npart, npts = data_index.shape
# arrays to copy to GPU memory =====================================
d_data = cuda.to_device(data)
d_data_imag = cuda.to_device(data_imag)
d_data_index = cuda.to_device(data_index)
d_coef = cuda.to_device(coef)
d_datasum = cuda.device_array(npts, np.complex64)
threadsperblock = 64
blockspergrid = int(math.ceil(npts / threadsperblock))+1
cuda_kernel[blockspergrid, threadsperblock](npart, npts, d_data, d_data_index, d_coef, d_datasum)
# Copy data from GPU to CPU ========================================
final_data_sum = d_datasum.copy_to_host()
return final_data_sum
def calculate_python (data, data_index, coef):
npart, npts = data_index.shape
data_sum = np.zeros(npts, dtype=np.complex64)
tmp = np.zeros(npts, dtype=np.complex64)
print(" Calling python function...")
for i in range(npart):
tmp[:] = data[data_index[i]]
data_sum += tmp * coef[i]
return data_sum
if __name__ == '__main__':
rows = 31
cols = 1000000
data_size = rows * cols
data_real = np.random.randn(data_size).astype(np.float32)
data_imag = np.random.randn(data_size).astype(np.float32)
data = data_real + 1j * data_imag
coef = np.random.randn(rows, cols)
data_index = random_choice_noreplace(rows, cols)
gpu_data_sum_python = calculate_python (data, data_index, coef)
start_time = time.time()
gpu_data_sum_python = calculate_python (data, data_index, coef)
python_time = time.time() - start_time #print("gpu c : ", c_gpu)
print("---- %s seconds for python ----:" % (python_time))
print(gpu_data_sum_python)
gpu_data_sum = calculate_cuda (data, data_index, coef)
start_time = time.time()
gpu_data_sum = calculate_cuda (data, data_index, coef)
gpu_time = time.time() - start_time
print("---- %s seconds for gpu ----:" % (gpu_time))
print(gpu_data_sum)
$ python t31.py
Calling python function...
Calling python function...
---- 0.806931018829 seconds for python ----:
[ 6.56164026-7.95271683j -7.42586899+3.68758106j 3.48999476+3.10376763j
..., 13.12746525+4.61855698j 0.08796659+0.9710322j
-6.54224586+4.89485168j]
---- 0.417661905289 seconds for gpu ----:
[ 6.56164074-7.95271683j -7.42586851+3.68758035j 3.48999500+3.10376763j
..., 13.12746525+4.61855745j 0.08796643+0.97103256j
-6.54224634+4.89485121j]
$
经过这些修改，GPU代码似乎比CPU代码快大约2倍。
这是在CUDA 9.2，Fedora 27，Quadro K2000（相对较小，速度较慢的GPU）上测得的。在比较中，我不会读太多，因为几乎可以肯定，CPU代码也不是最优的，而且对于GPU加速而言，每个输出数据点的工作量仍然相对较少。