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added printf style error messages to nail down memory access problems

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This commit is contained in:
Peter Steinbach 2017-02-27 17:01:38 +01:00
parent 0fc6722684
commit f7af8ebc91
1 changed files with 16 additions and 7 deletions
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23
HCStream.cpp
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@ -78,6 +78,7 @@ HCStream<T>::~HCStream()
template <class T>
void HCStream<T>::init_arrays(T _a, T _b, T _c)
{
std::cout << __FILE__ << ":" << __LINE__ << "\t" << "initializing arrays\n";
std::vector<T> temp(array_size,_a);
hc::copy(temp.begin(), temp.end(),this->d_a);
@ -93,6 +94,7 @@ template <class T>
void HCStream<T>::read_arrays(std::vector<T>& a, std::vector<T>& b, std::vector<T>& c)
{
// Copy device memory to host
std::cout << __FILE__ << ":" << __LINE__ << "\t" << "read arrays\n";
hc::copy(d_a,a.begin());
hc::copy(d_b,b.begin());
hc::copy(d_c,c.begin());
@ -103,6 +105,7 @@ template <class T>
void HCStream<T>::copy()
{
std::cout << __FILE__ << ":" << __LINE__ << "\t" << "copy\n";
hc::array<T,1>& device_a = this->d_a;
hc::array<T,1>& device_c = this->d_c;
@ -123,6 +126,7 @@ void HCStream<T>::copy()
template <class T>
void HCStream<T>::mul()
{
std::cout << __FILE__ << ":" << __LINE__ << "\t" << "mul\n";
const T scalar = 0.3;
hc::array<T,1>& device_b = this->d_b;
hc::array<T,1>& device_c = this->d_c;
@ -144,7 +148,7 @@ void HCStream<T>::mul()
template <class T>
void HCStream<T>::add()
{
std::cout << __FILE__ << ":" << __LINE__ << "\t" << "add\n";
hc::array<T,1>& device_a = this->d_a;
hc::array<T,1>& device_b = this->d_b;
hc::array<T,1>& device_c = this->d_c;
@ -166,6 +170,7 @@ void HCStream<T>::add()
template <class T>
void HCStream<T>::triad()
{
std::cout << __FILE__ << ":" << __LINE__ << "\t" << "triad\n";
const T scalar = 0.3;
hc::array<T,1>& device_a = this->d_a;
hc::array<T,1>& device_b = this->d_b;
@ -188,16 +193,17 @@ void HCStream<T>::triad()
template <class T>
T HCStream<T>::dot()
{
hc::array<T,1>& device_a = this->d_a;
hc::array<T,1> product = this->d_b;
std::cout << __FILE__ << ":" << __LINE__ << "\t" << "dot\n";
hc::array_view<T,1> view_a(this->d_a);
hc::array_view<T,1> view_p(this->d_b);
T sum = static_cast<T>(0);
try{
// launch a GPU kernel to compute the saxpy in parallel
hc::completion_future future_kernel = hc::parallel_for_each(hc::extent<1>(array_size)
, [&](hc::index<1> i) [[hc]] {
product[i] *= device_a[i];
hc::completion_future future_kernel = hc::parallel_for_each(view_a.get_extent(),
[&](hc::index<1> i) [[hc]] {
view_p[i] = view_p[i]*view_a[i];
});
future_kernel.wait();
}
@ -206,8 +212,11 @@ T HCStream<T>::dot()
throw;
}
std::cout << __FILE__ << ":" << __LINE__ << "\t" << "dot - for_each done\n";
std::vector<T> h_product(array_size,sum);
hc::copy(product,h_product.begin());
hc::copy(view_p,h_product.begin());
std::cout << __FILE__ << ":" << __LINE__ << "\t" << "dot - copy-out done\n";
sum = std::accumulate(h_product.begin(), h_product.end(),sum);