Fixed memory management for GPU, now working with OpenMP and CUDA
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@ -16,21 +16,36 @@ RAJAStream<T>::RAJAStream(const unsigned int ARRAY_SIZE, const int device_index)
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{
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{
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RangeSegment seg(0, ARRAY_SIZE);
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RangeSegment seg(0, ARRAY_SIZE);
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index_set.push_back(seg);
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index_set.push_back(seg);
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#ifdef RAJA_TARGET_CPU
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d_a = new T[ARRAY_SIZE];
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d_a = new T[ARRAY_SIZE];
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d_b = new T[ARRAY_SIZE];
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d_b = new T[ARRAY_SIZE];
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d_c = new T[ARRAY_SIZE];
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d_c = new T[ARRAY_SIZE];
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#else
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cudaMallocManaged((void**)&d_a, sizeof(T)*ARRAY_SIZE, cudaMemAttachGlobal);
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cudaMallocManaged((void**)&d_b, sizeof(T)*ARRAY_SIZE, cudaMemAttachGlobal);
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cudaMallocManaged((void**)&d_c, sizeof(T)*ARRAY_SIZE, cudaMemAttachGlobal);
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cudaDeviceSynchronize();
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#endif
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}
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}
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template <class T>
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template <class T>
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RAJAStream<T>::~RAJAStream()
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RAJAStream<T>::~RAJAStream()
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{
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{
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#ifdef RAJA_TARGET_CPU
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delete[] d_a;
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delete[] d_a;
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delete[] d_b;
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delete[] d_b;
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delete[] d_c;
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delete[] d_c;
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#else
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cudaFree(d_a);
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cudaFree(d_b);
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cudaFree(d_c);
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#endif
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}
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}
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template <class T>
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template <class T>
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void RAJAStream<T>::write_arrays(const std::vector<T>& a, const std::vector<T>& b, const std::vector<T>& c)
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void RAJAStream<T>::write_arrays(
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const std::vector<T>& a, const std::vector<T>& b, const std::vector<T>& c)
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{
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{
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std::copy(a.begin(), a.end(), d_a);
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std::copy(a.begin(), a.end(), d_a);
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std::copy(b.begin(), b.end(), d_b);
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std::copy(b.begin(), b.end(), d_b);
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@ -38,48 +53,59 @@ void RAJAStream<T>::write_arrays(const std::vector<T>& a, const std::vector<T>&
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}
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}
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template <class T>
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template <class T>
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void RAJAStream<T>::read_arrays(std::vector<T>& a, std::vector<T>& b, std::vector<T>& c)
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void RAJAStream<T>::read_arrays(
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std::vector<T>& a, std::vector<T>& b, std::vector<T>& c)
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{
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{
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std::copy(d_a, d_a + array_size - 1, a.data());
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std::copy(d_a, d_a + array_size, a.data());
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std::copy(d_b, d_b + array_size - 1, b.data());
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std::copy(d_b, d_b + array_size, b.data());
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std::copy(d_c, d_c + array_size - 1, c.data());
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std::copy(d_c, d_c + array_size, c.data());
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}
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}
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template <class T>
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template <class T>
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void RAJAStream<T>::copy()
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void RAJAStream<T>::copy()
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{
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{
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T* a = d_a;
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T* c = d_c;
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forall<policy>(index_set, [=] RAJA_DEVICE (int index)
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forall<policy>(index_set, [=] RAJA_DEVICE (int index)
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{
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{
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d_c[index] = d_a[index];
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c[index] = a[index];
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});
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});
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}
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}
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template <class T>
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template <class T>
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void RAJAStream<T>::mul()
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void RAJAStream<T>::mul()
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{
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{
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T* b = d_b;
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T* c = d_c;
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const T scalar = 3.0;
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const T scalar = 3.0;
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forall<policy>(index_set, [=] RAJA_DEVICE (int index)
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forall<policy>(index_set, [=] RAJA_DEVICE (int index)
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{
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{
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d_b[index] = scalar*d_c[index];
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b[index] = scalar*c[index];
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});
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});
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}
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}
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template <class T>
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template <class T>
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void RAJAStream<T>::add()
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void RAJAStream<T>::add()
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{
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{
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T* a = d_a;
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T* b = d_b;
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T* c = d_c;
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forall<policy>(index_set, [=] RAJA_DEVICE (int index)
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forall<policy>(index_set, [=] RAJA_DEVICE (int index)
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{
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{
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d_c[index] = d_a[index] + d_b[index];
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c[index] = a[index] + b[index];
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});
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});
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}
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}
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template <class T>
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template <class T>
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void RAJAStream<T>::triad()
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void RAJAStream<T>::triad()
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{
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{
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T* a = d_a;
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T* b = d_b;
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T* c = d_c;
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const T scalar = 3.0;
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const T scalar = 3.0;
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forall<policy>(index_set, [=] RAJA_DEVICE (int index)
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forall<policy>(index_set, [=] RAJA_DEVICE (int index)
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{
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{
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d_a[index] = d_b[index] + scalar*d_c[index];
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a[index] = b[index] + scalar*c[index];
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});
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});
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}
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}
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@ -14,15 +14,15 @@
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#define IMPLEMENTATION_STRING "RAJA"
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#define IMPLEMENTATION_STRING "RAJA"
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#ifdef RAJA_USE_CUDA
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#ifdef RAJA_TARGET_CPU
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const size_t block_size = 128;
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typedef RAJA::IndexSet::ExecPolicy<
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RAJA::seq_segit,
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RAJA::cuda_exec_async<block_size>> policy;
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#else
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typedef RAJA::IndexSet::ExecPolicy<
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typedef RAJA::IndexSet::ExecPolicy<
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RAJA::seq_segit,
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RAJA::seq_segit,
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RAJA::omp_parallel_for_exec> policy;
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RAJA::omp_parallel_for_exec> policy;
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#else
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const size_t block_size = 128;
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typedef RAJA::IndexSet::ExecPolicy<
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RAJA::seq_segit,
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RAJA::cuda_exec<block_size>> policy;
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#endif
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#endif
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template <class T>
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template <class T>
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