在我的代码的第一个版本中，我使用了python的Multiprocessing库，该库适用于16个线程的主函数MAIN_LOOP，如下所示：
def MAIN_LOOP(lll, seed=None):
global aa
global bb
aa, bb = 0,0
if paramo == 0:
C_ij_GG, C_ij_LL, C_ij_GL = np.zeros((len(zrange), len(zrange))), np.zeros((len(zrange), len(zrange))), np.zeros((len(zrange), len(zrange)))
C_ij_GG_up, C_ij_LL_up, C_ij_GL_up = np.zeros((len(zrange), len(zrange))), np.zeros((len(zrange), len(zrange))), np.zeros((len(zrange), len(zrange)))
C_ij_GG_dw, C_ij_LL_dw, C_ij_GL_dw = np.zeros((len(zrange), len(zrange))), np.zeros((len(zrange), len(zrange))), np.zeros((len(zrange), len(zrange)))
while aa < len(zrange):
while bb < len(zrange):
if paramo == 0:
C_ij_GG[aa][bb], C_ij_LL[aa][bb], C_ij_GL[aa][bb] = Pobs_C(zpm, zrange[aa], zrange[bb], h[2], Omega_m[2], Omega_DE[2], w0[2], wa[2], C_IA, A_IA[2], n_IA[2], B_IA[2], E_tab, R_tab, DG_tab, DG_tab, WG_tab, W_tab, WIA_tab, l[lll], P_dd_C, R_tab(z_pk))
C_ij_GG_up[aa][bb], C_ij_LL_up[aa][bb], C_ij_GL_up[aa][bb] = Pobs_C(zpm, zrange[aa], zrange[bb], h[0], Omega_m[0], Omega_DE[0], w0[0], wa[0], C_IA, A_IA[0], n_IA[0], B_IA[0], E_tab_up, R_tab_up, DG_tab, DG_tab_up, WG_tab_up, W_tab_up, WIA_tab_up, l[lll], P_dd_C_up, R_tab_up(z_pk))
C_ij_GG_dw[aa][bb], C_ij_LL_dw[aa][bb], C_ij_GL_dw[aa][bb] = Pobs_C(zpm, zrange[aa], zrange[bb], h[3], Omega_m[3], Omega_DE[3], w0[3], wa[3], C_IA, A_IA[3], n_IA[3], B_IA[3], E_tab_dw, R_tab_dw, DG_tab, DG_tab_dw, WG_tab_dw, W_tab_dw, WIA_tab_dw, l[lll], P_dd_C_dw, R_tab_dw(z_pk))
bb=bb+1
bb=0
aa=aa+1
if paramo == 0:
aa, bb = 0,0
outGG=open(pre_CC_path[0]+CC_path[2]+"/COVAR_fid_"+str(l[lll]),'w')
outLL=open(pre_CC_path[1]+CC_path[2]+"/COVAR_fid_"+str(l[lll]),'w')
outGL=open(pre_CC_path[2]+CC_path[2]+"/COVAR_fid_"+str(l[lll]),'w')
while aa < len(C_ij_GG):
while bb < len(C_ij_GG):
outGG.write(str("%.16e" % C_ij_GG[aa][bb]))
outGG.write(str(' '))
outLL.write(str("%.16e" % C_ij_LL[aa][bb]))
outLL.write(str(' '))
outGL.write(str("%.16e" % C_ij_GL[aa][bb]))
outGL.write(str(' '))
bb=bb+1
outGG.write(str('\n'))
outLL.write(str('\n'))
outGL.write(str('\n'))
bb=0
aa=aa+1
outGG.close()
outLL.close()
outGL.close()
aa, bb = 0,0
outGGU=open(pre_CC_path[0]+CC_path[0]+"/COVAR_up_"+str(l[lll]),'w')
outGGD=open(pre_CC_path[0]+CC_path[3]+"/COVAR_dw_"+str(l[lll]),'w')
outLLU=open(pre_CC_path[1]+CC_path[0]+"/COVAR_up_"+str(l[lll]),'w')
outLLD=open(pre_CC_path[1]+CC_path[3]+"/COVAR_dw_"+str(l[lll]),'w')
outGLU=open(pre_CC_path[2]+CC_path[0]+"/COVAR_up_"+str(l[lll]),'w')
outGLD=open(pre_CC_path[2]+CC_path[3]+"/COVAR_dw_"+str(l[lll]),'w')
while aa < len(C_ij_GG_up):
while bb < len(C_ij_GG_up):
outGGU.write(str("%.16e" % C_ij_GG_up[aa][bb]))
outGGU.write(str(' '))
outGGD.write(str("%.16e" % C_ij_GG_dw[aa][bb]))
outGGD.write(str(' '))
outLLU.write(str("%.16e" % C_ij_LL_up[aa][bb]))
outLLU.write(str(' '))
outLLD.write(str("%.16e" % C_ij_LL_dw[aa][bb]))
outLLD.write(str(' '))
outGLU.write(str("%.16e" % C_ij_GL_up[aa][bb]))
outGLU.write(str(' '))
outGLD.write(str("%.16e" % C_ij_GL_dw[aa][bb]))
outGLD.write(str(' '))
bb=bb+1
outGGU.write(str('\n'))
outGGD.write(str('\n'))
outLLU.write(str('\n'))
outLLD.write(str('\n'))
outGLU.write(str('\n'))
outGLD.write(str('\n'))
bb=0
aa=aa+1
outGGU.close()
outGGD.close()
outLLU.close()
outLLD.close()
outGLU.close()
outGLD.close()
lll=lll+1
lll = range(len(l))
if __name__ == '__main__':
pool = mp.Pool(16)
pool.map(MAIN_LOOP, lll)
并行化版本位于末尾，即带有：
if __name__ == '__main__':
pool = mp.Pool(16)
pool.map(MAIN_LOOP, lll)
现在，我正在尝试使用另一种优化方法，并且尝试通过GPU / OpenCL进行优化：
所以，我没有用这个平行的multiprocessing pool代码部分，而是用：]代替了。
# NEW VERSION : with OpenCL if __name__ == '__main__': # GPU/OPenCL VERSION # Select a device ctx = cl.create_some_context() queue = cl.CommandQueue(ctx) # Kernel prg = cl.Program(ctx, """ typedef int T; // Extern MAIN_LOOP function void MAIN_LOOP(__global T* in); __kernel void gpu_map(__global T* in, const size_t n) { unsigned gid = get_global_id(0); // Call MAIN_LOOP with global_id MAIN_LOOP(in[gid]); } """).build() # Output compiler os.environ['PYOPENCL_COMPILER_OUTPUT'] = '1' # Allocate memory on the device and copy the content of our numpy array mf = cl.mem_flags # Get kernel function lll_np = np.array(lll, dtype=np.uint32) # Create input numpy lll_g = cl.Buffer(ctx, mf.READ_ONLY | mf.COPY_HOST_PTR, hostbuf=lll_np) #Get kernel function my_knl= prg.gpu_map my_knl.set_scalar_arg_dtypes([None, np.int32]) my_knl(queue, lll_np.shape, None, lll_g, len(lll_np))不幸的是，无法构建内核程序，出现以下错误：
Traceback (most recent call last):X2_non_flat_GPU_dev.py File "X2_non_flat_GPU_OpenCL_dev.py", line 671, in <module> """).build() File "/Users/fab/Library/Python/2.7/lib/python/site-packages/pyopencl/__init__.py", line 510, in build options_bytes=options_bytes, source=self._source) File "/Users/fab/Library/Python/2.7/lib/python/site-packages/pyopencl/__init__.py", line 554, in _build_and_catch_errors raise err pyopencl._cl.RuntimeError: clBuildProgram failed: BUILD_PROGRAM_FAILURE - clBuildProgram failed: BUILD_PROGRAM_FAILURE - clBuildProgram failed: BUILD_PROGRAM_FAILURE Build on <pyopencl.Device 'AMD Radeon Pro Vega 20 Compute Engine' on 'Apple' at 0x1021d00>: Error returned by cvms_element_build_from_source (options: -I /Users/fab/Library/Python/2.7/lib/python/site-packages/pyopencl/cl) (source saved as /var/folders/y7/5dtgdjld5fxd3c1qm9hknlm40000gn/T/tmpg3pfTx.cl)关于这些错误的任何线索？谢谢
UPDATE 1：
类似的悬赏开始于我的代码基准测试：Different ways to optimize with GPU PyOpenCL a python code : extern function inside kernel GPU/PyOpenCL从运行时的角度来看，有关代码部分的更多信息是贪婪的。但是，这种赏金更多地是关于可以找到一种优化方法的一般想法。
在我的代码的第一个版本中，我使用了python的Multiprocessing库，该库应用于16个线程的主函数MAIN_LOOP上，如下所示：def MAIN_LOOP（lll，seed = None）：global aa global ...
在OpenCL中，您有一个主机（在您的情况下，您的CPU执行Python代码）和几个设备（通常是单个GPU）。
主机使设备执行的入队操作：