我正在尝试用numba cuda编写一段代码。我看到了很多分别处理设备内存和共享内存的例子。我陷入了困境和困惑。代码或函数可以同时处理这两种情况吗?例如,代码可以在某个比例中使用共享内存进行乘法运算,而在另一个比例中则使用设备。
另一件需要问的事情是,当我试图一步一步地使代码复杂化以计算适应度函数时,我使用了一个共享内存的空间作为中间阶段sD,根据mark harris的演示,减少了一半的线程,并添加了2作为sSdata[tid]+=s数据[tid+s]
当我写下面的代码时,我出现了一个错误,我不知道为什么。
import numpy as np
import math
from numba import cuda, float32
@cuda.jit
def fast_matmul(A, C):
sA = cuda.shared.array(shape=(1, TPB), dtype=float32)
sD = cuda.shared.array(shape=(1, TPB), dtype=float32)
thread_idx_x = cuda.threadIdx.x
thread_idx_y = cuda.threadIdx.y
totla_No_of_threads_x = cuda.blockDim.x
totla_No_of_threads_y = cuda.blockDim.y
block_idx_x = cuda.blockIdx.x
block_idx_y = cuda.blockIdx.y
x, y = cuda.grid(2)
if x >= A.shape[1]: #and y >= C.shape[1]:
return
s = 0
index_1 = 1
for i in range(int(A.shape[1] / TPB)):
sA[thread_idx_x, thread_idx_y] = A[x, thread_idx_y + i * TPB]
cuda.syncthreads()
if thread_idx_y <= (totla_No_of_threads_y-index_1):
sD[thread_idx_x, thread_idx_y] = sA[thread_idx_x, (thread_idx_y +index_1)] - sA[thread_idx_x, thread_idx_y]
cuda.syncthreads()
for s in range(totla_No_of_threads_y//2):
if thread_idx_y < s:
sD[thread_idx_x, thread_idx_y] += sD[thread_idx_x, thread_idx_y+s]
cuda.syncthreads()
C[x, y] = sD[x,y]
A = np.full((1, 16), 3, dtype=np.float32)
C = np.zeros((1, 16))
print('A:', A, 'C:', C)
TPB = 32
dA = cuda.to_device(A)
dC= cuda.to_device(C)
fast_matmul[(1, 1), (32, 32)](dA, dC)
res= dC.copy_to_host()
print(res)
错误显示为
CudaAPIError Traceback (most recent call last)
<ipython-input-214-780fde9bbab5> in <module>
5 TPB = 32
6
----> 7 dA = cuda.to_device(A)
8 dC= cuda.to_device(C)
9 fast_matmul[(8, 8), (32, 32)](dA, dC)
~Anaconda3libsite-packagesnumbacudacudadrvdevices.py in _require_cuda_context(*args, **kws)
222 def _require_cuda_context(*args, **kws):
223 with _runtime.ensure_context():
--> 224 return fn(*args, **kws)
225
226 return _require_cuda_context
~Anaconda3libsite-packagesnumbacudaapi.py in to_device(obj, stream, copy, to)
108 """
109 if to is None:
--> 110 to, new = devicearray.auto_device(obj, stream=stream, copy=copy)
111 return to
112 if copy:
~Anaconda3libsite-packagesnumbacudacudadrvdevicearray.py in auto_device(obj, stream, copy)
764 subok=True)
765 sentry_contiguous(obj)
--> 766 devobj = from_array_like(obj, stream=stream)
767 if copy:
768 devobj.copy_to_device(obj, stream=stream)
~Anaconda3libsite-packagesnumbacudacudadrvdevicearray.py in from_array_like(ary, stream, gpu_data)
686 "Create a DeviceNDArray object that is like ary."
687 return DeviceNDArray(ary.shape, ary.strides, ary.dtype,
--> 688 writeback=ary, stream=stream, gpu_data=gpu_data)
689
690
~Anaconda3libsite-packagesnumbacudacudadrvdevicearray.py in __init__(self, shape, strides, dtype, stream, writeback, gpu_data)
102 self.strides,
103 self.dtype.itemsize)
--> 104 gpu_data = devices.get_context().memalloc(self.alloc_size)
105 else:
106 self.alloc_size = _driver.device_memory_size(gpu_data)
~Anaconda3libsite-packagesnumbacudacudadrvdriver.py in memalloc(self, bytesize)
1099
1100 def memalloc(self, bytesize):
-> 1101 return self.memory_manager.memalloc(bytesize)
1102
1103 def memhostalloc(self, bytesize, mapped=False, portable=False, wc=False):
~Anaconda3libsite-packagesnumbacudacudadrvdriver.py in memalloc(self, size)
849 driver.cuMemAlloc(byref(ptr), size)
850
--> 851 self._attempt_allocation(allocator)
852
853 finalizer = _alloc_finalizer(self, ptr, size)
~Anaconda3libsite-packagesnumbacudacudadrvdriver.py in _attempt_allocation(self, allocator)
709 """
710 try:
--> 711 allocator()
712 except CudaAPIError as e:
713 # is out-of-memory?
~Anaconda3libsite-packagesnumbacudacudadrvdriver.py in allocator()
847
848 def allocator():
--> 849 driver.cuMemAlloc(byref(ptr), size)
850
851 self._attempt_allocation(allocator)
~Anaconda3libsite-packagesnumbacudacudadrvdriver.py in safe_cuda_api_call(*args)
300 _logger.debug('call driver api: %s', libfn.__name__)
301 retcode = libfn(*args)
--> 302 self._check_error(fname, retcode)
303 return safe_cuda_api_call
304
~Anaconda3libsite-packagesnumbacudacudadrvdriver.py in _check_error(self, fname, retcode)
335 _logger.critical(msg, _getpid(), self.pid)
336 raise CudaDriverError("CUDA initialized before forking")
--> 337 raise CudaAPIError(retcode, msg)
338
339 def get_device(self, devnum=0):
CudaAPIError: [700] Call to cuMemAlloc results in UNKNOWN_CUDA_ERROR
是的,两者都可以使用。当您将数据从主机复制到设备时,它将以";设备存储器";。此后,如果您想使用共享内存,则必须从内核代码中明确地将数据复制到其中。同样,当您想将结果返回到主机代码(将数据从设备复制到主机(时,数据必须是";设备存储器";。
共享内存是一种较小的、草稿式的资源。
这提供了一个很好的例子/比较。
我不知道这是否能解决您的错误,因为看起来您没有使用多处理。但我犯了完全相同的错误";引发CudaDriverError("在分叉之前初始化的CUDA"(;问题是python多处理正在使用";叉子";而不是";产卵";。
multiprocessing.set_start_method('spawn')
为我修复了这个问题,它可能对你没有帮助,但可能会帮助其他基于这个麻木错误进行搜索的人。