Ben Sander
49 linhas
2.0 KiB
C++
49 linhas
2.0 KiB
C++
// Simple test showing how to use C++AMP syntax with array_view.
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// The code uses AMP's array_view class, which provides automatic data synchronization
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// of data between the host and the accelerator. As noted below, the HCC runtime
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// will automatically copy data to and from the host, without the user needing
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// to manually perform such copies. This is an excellent mode for developers
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// new to GPU programming and matches the memory models provided by recent systems where
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// CPU and GPU share the same memory pool. Advanced programmers may prefer
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// more explicit control over the data movement - shown in the other vadd_hc_array and
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// vadd_hc_am examples.
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// This example shows the similarity between C++AMP and and HC for simple cases where
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// implicit data transfer is used - really the only difference is the namespace.
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// Other examples show some of the more advanced controls.
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#include <amp.h>
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int main(int argc, char *argv[])
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{
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int sizeElements = 1000000;
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// Allocate auto-managed host/device views of data:
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concurrency::array_view<float> A(sizeElements);
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concurrency::array_view<float> B(sizeElements);
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concurrency::array_view<float> C(sizeElements);
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// Initialize host data
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for (int i=0; i<sizeElements; i++) {
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A[i] = 1.618f * i;
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B[i] = 3.142f * i;
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}
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C.discard_data(); // tell runtime not to copy CPU host data.
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// Launch kernel onto default accelerator
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// The HCC runtime will ensure that A and B are available on the accelerator before launching the kernel.
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concurrency::parallel_for_each(concurrency::extent<1> (sizeElements),
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[=] (concurrency::index<1> idx) restrict(amp) {
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int i = idx[0];
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C[i] = A[i] + B[i];
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});
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for (int i=0; i<sizeElements; i++) {
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float ref= 1.618f * i + 3.142f * i;
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// Because C is an array_view, the HCC runtime will copy C back to host at first access here:
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if (C[i] != ref) {
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printf ("error:%d computed=%6.2f, reference=%6.2f\n", i, C[i], ref);
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}
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};
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}
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