我有一个尺寸为1000x1000x1000的3D numpy数组。我正在寻找整个数组中值1的索引。 np.nonzero（数组）对于较大的数据集来说非常慢。我想知道是否有办法通过pycuda来做到这一点。还是有其他更有效的方法。
答案 0 :(得分：1)
之前我没有使用过PyCuda，但是自从我找到good example on how to use thrust in PyCuda后，我提出了以下解决方案。
在内部，它使用thrust::counting_iterator和thrust::copy_if来查找等于1的元素的索引。
虽然这可能会更快，但整个问题存在严重缺陷： 您有一个包含10亿（1000000000）个元素的数组，使用32位整数时需要4 GB内存。 您还需要另一个4 GB的输出数组。 即使您的GPU有那么多RAM，输入数据也需要复制到GPU，这需要一些时间。
如果您的数组主要由零条目组成，那么使用sparse matrix format可能会更好，并且只存储非零条目。 这将节省内存，您根本不必搜索非零条目。
<强> find_indices_thrust.py
import pycuda
import pycuda.autoinit
import pycuda.gpuarray as gpuarray
import numpy as np
from codepy.cgen import *
from codepy.bpl import BoostPythonModule
from codepy.cuda import CudaModule
#Make a host_module, compiled for CPU
host_mod = BoostPythonModule()
#Make a device module, compiled with NVCC
nvcc_mod = CudaModule(host_mod)
#Describe device module code
#NVCC includes
nvcc_includes = [
'thrust/copy.h',
'thrust/device_vector.h',
'thrust/iterator/counting_iterator.h',
'thrust/functional.h',
'cuda.h',
'stdint.h'
]
#Add includes to module
nvcc_mod.add_to_preamble([Include(x) for x in nvcc_includes])
#NVCC function
nvcc_function = FunctionBody(
FunctionDeclaration(Value('void', 'find_indices'),
[Value('CUdeviceptr', 'input_ptr'),
Value('uint32_t', 'input_length'),
Value('CUdeviceptr', 'output_ptr'),
Value('uint32_t*', 'output_length')]),
Block([Statement('thrust::device_ptr<uint32_t> thrust_input_ptr((uint32_t*)input_ptr)'),
Statement('thrust::device_ptr<uint32_t> thrust_output_ptr((uint32_t*)output_ptr)'),
Statement('using namespace thrust::placeholders'),
Statement('*output_length = thrust::copy_if(thrust::counting_iterator<uint32_t>(0), thrust::counting_iterator<uint32_t>(input_length), thrust_input_ptr, thrust_output_ptr, _1==1)-thrust_output_ptr')]))
#Add declaration to nvcc_mod
#Adds declaration to host_mod as well
nvcc_mod.add_function(nvcc_function)
host_includes = [
'boost/python/extract.hpp',
]
#Add host includes to module
host_mod.add_to_preamble([Include(x) for x in host_includes])
host_namespaces = [
'using namespace boost::python',
]
#Add BPL using statement
host_mod.add_to_preamble([Statement(x) for x in host_namespaces])
host_statements = [
#Extract information from PyCUDA GPUArray
#Get length
'tuple shape = extract<tuple>(gpu_input_array.attr("shape"))',
'int input_length = extract<int>(shape[0])',
#Get input data pointer
'CUdeviceptr input_ptr = extract<CUdeviceptr>(gpu_input_array.attr("ptr"))',
#Get output data pointer
'CUdeviceptr output_ptr = extract<CUdeviceptr>(gpu_output_array.attr("ptr"))',
#Call Thrust routine, compiled into the CudaModule
'uint32_t output_size',
'find_indices(input_ptr, input_length, output_ptr, &output_size)',
#Return result
'return output_size',
]
host_mod.add_function(
FunctionBody(
FunctionDeclaration(Value('int', 'host_entry'),
[Value('object', 'gpu_input_array'),Value('object', 'gpu_output_array')]),
Block([Statement(x) for x in host_statements])))
#Print out generated code, to see what we're actually compiling
print("---------------------- Host code ----------------------")
print(host_mod.generate())
print("--------------------- Device code ---------------------")
print(nvcc_mod.generate())
print("-------------------------------------------------------")
#Compile modules
import codepy.jit, codepy.toolchain
gcc_toolchain = codepy.toolchain.guess_toolchain()
nvcc_toolchain = codepy.toolchain.guess_nvcc_toolchain()
module = nvcc_mod.compile(gcc_toolchain, nvcc_toolchain, debug=True)
length = 100
input_array = np.array(np.random.rand(length)*5, dtype=np.uint32)
output_array = np.zeros(length, dtype=np.uint32)
print("---------------------- INPUT -----------------------")
print(input_array)
gpu_input_array = gpuarray.to_gpu(input_array)
gpu_output_array = gpuarray.to_gpu(output_array)
# call GPU function
output_size = module.host_entry(gpu_input_array, gpu_output_array)
print("----------------------- OUTPUT ------------------------")
print gpu_output_array[:output_size]
print("-------------------------------------------------------")
生成的代码
---------------------- Host code ----------------------
#include <boost/python.hpp>
#include <cuda.h>
void find_indices(CUdeviceptr input_ptr, uint32_t input_length, CUdeviceptr output_ptr, uint32_t* output_length);
#include <boost/python/extract.hpp>
using namespace boost::python;
namespace private_namespace_6f5e74fc4bebe20d5478de66e2226656
{
int host_entry(object gpu_input_array, object gpu_output_array)
{
tuple shape = extract<tuple>(gpu_input_array.attr("shape"));
int input_length = extract<int>(shape[0]);
CUdeviceptr input_ptr = extract<CUdeviceptr>(gpu_input_array.attr("ptr"));
CUdeviceptr output_ptr = extract<CUdeviceptr>(gpu_output_array.attr("ptr"));
uint32_t output_size;
find_indices(input_ptr, input_length, output_ptr, &output_size);
return output_size;
}
}
using namespace private_namespace_6f5e74fc4bebe20d5478de66e2226656;
BOOST_PYTHON_MODULE(module)
{
boost::python::def("host_entry", &host_entry);
}
--------------------- Device code ---------------------
#include <thrust/copy.h>
#include <thrust/device_vector.h>
#include <thrust/iterator/counting_iterator.h>
#include <thrust/functional.h>
#include <cuda.h>
#include <stdint.h>
void find_indices(CUdeviceptr input_ptr, uint32_t input_length, CUdeviceptr output_ptr, uint32_t* output_length)
{
thrust::device_ptr<uint32_t> thrust_input_ptr((uint32_t*)input_ptr);
thrust::device_ptr<uint32_t> thrust_output_ptr((uint32_t*)output_ptr);
using namespace thrust::placeholders;
*output_length = thrust::copy_if(thrust::counting_iterator<uint32_t>(0), thrust::counting_iterator<uint32_t>(input_length), thrust_input_ptr, thrust_output_ptr, _1==1)-thrust_output_ptr;
}
-------------------------------------------------------
演示输出
---------------------- INPUT -----------------------
[1 2 3 0 3 3 1 2 1 2 0 4 4 3 2 0 4 2 3 0 2 3 1 4 3 4 3 4 3 2 4 3 2 4 2 0 3
0 3 4 3 0 0 4 4 2 0 3 3 1 3 4 2 0 0 4 0 4 3 2 3 2 1 1 4 3 0 4 3 1 1 1 3 2
0 0 3 4 3 3 4 2 2 3 4 1 1 3 2 2 2 2 3 2 0 2 4 3 2 0]
----------------------- OUTPUT ------------------------
[ 0 6 8 22 49 62 63 69 70 71 85 86]
-------------------------------------------------------