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