我正在尝试运行 oneAPI 的 hello-world DPC++ 示例，该示例在 CPU 和 GPU 上添加两个一维数组，并验证结果。代码如下所示：
/*
DataParallel Addition of two Vectors
*/
#include <CL/sycl.hpp>
#include <array>
#include <iostream>
using namespace sycl;
constexpr size_t array_size = 100000;
typedef std::array<int, array_size> IntArray;
// Initialize array with the same value as its index
void InitializeArray(IntArray& a) { for (size_t i = 0; i < a.size(); i++) a[i] = i; }
/*
Create an asynchronous Exception Handler for sycl
*/
static auto exception_handler = [](cl::sycl::exception_list eList) {
for (std::exception_ptr const& e : eList) {
try {
std::rethrow_exception(e);
}
catch (std::exception const& e) {
std::cout << "Failure" << std::endl;
std::terminate();
}
}
};
void VectorAddParallel(queue &q, const IntArray& x, const IntArray& y, IntArray& parallel_sum) {
range<1> num_items{ x.size() };
buffer x_buf(x);
buffer y_buf(y);
buffer sum_buf(parallel_sum.data(), num_items);
/*
Submit a command group to the queue by a lambda
which contains data access permissions and device computation
*/
q.submit([&](handler& h) {
auto xa = x_buf.get_access<access::mode::read>(h);
auto ya = y_buf.get_access<access::mode::read>(h);
auto sa = sum_buf.get_access<access::mode::write>(h);
std::cout << "Adding on GPU (Parallel)\n";
h.parallel_for(num_items, [=](id<1> i) { sa[i] = xa[i] + ya[i]; });
std::cout << "Done on GPU (Parallel)\n";
});
/*
queue runs the kernel asynchronously. Once beyond the scope,
buffers' data is copied back to the host.
*/
}
int main() {
default_selector d_selector;
IntArray a, b, sequential, parallel;
InitializeArray(a);
InitializeArray(b);
try {
// Queue needs: Device and Exception handler
queue q(d_selector, exception_handler);
std::cout << "Accelerator: "
<< q.get_device().get_info<info::device::name>() << "\n";
std::cout << "Vector size: " << a.size() << "\n";
VectorAddParallel(q, a, b, parallel);
}
catch (std::exception const& e) {
std::cout << "Exception while creating Queue. Terminating...\n";
std::terminate();
}
/*
Do the sequential, which is supposed to be slow
*/
std::cout << "Adding on CPU (Scalar)\n";
for (size_t i = 0; i < sequential.size(); i++) {
sequential[i] = a[i] + b[i];
}
std::cout << "Done on CPU (Scalar)\n";
/*
Verify results, the old-school way
*/
for (size_t i = 0; i < parallel.size(); i++) {
if (parallel[i] != sequential[i]) {
std::cout << "Fail: " << parallel[i] << " != " << sequential[i] << std::endl;
std::cout << "Failed. Results do not match.\n";
return -1;
}
}
std::cout << "Success!\n";
return 0;
}
使用相对较小的array_size,（我测试了 100-50k 个元素）计算结果很好。样本输出：
Accelerator: Intel(R) Gen9
Vector size: 50000
Adding on GPU (Parallel)
Done on GPU (Parallel)
Adding on CPU (Scalar)
Done on CPU (Scalar)
Success!
可以注意到，在 CPU 和 GPU 上完成计算只需要一秒钟。但是当我增加array_size, 说,100000我得到这个看似毫无头绪的错误：
C:\Users\myuser\source\repos\dpcpp-iotas\x64\Debug\dpcpp-iotas.exe (process 24472) exited with code -1073741571.
虽然我不确定错误开始发生的精确值是多少，但我似乎确信它会在70000. 我似乎不知道为什么会发生这种情况，对可能出现什么问题有任何见解吗？