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Draft: Matrix Transpose Lesson 11 for Kernel #40

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Draft: Matrix Transpose Lesson 11 for Kernel #40

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f76c892
Preliminary kernel transpose for lesson 11.
rchen20 Jun 30, 2025
7198498
Use global policies.
rchen20 Jul 3, 2025
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50 changes: 50 additions & 0 deletions Intro_Tutorial/lessons/11_raja_device_kernel/11_raja_transpose_kernel.cpp
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Original file line number Diff line number Diff line change
@@ -0,0 +1,50 @@
#include <iostream>

#include "RAJA/RAJA.hpp"
#include "umpire/Umpire.hpp"
#include "umpire/strategy/QuickPool.hpp"

int main()
{
constexpr int N{10000};
constexpr int M{7000};
double* a{nullptr};
double* a_t{nullptr};

auto& rm = umpire::ResourceManager::getInstance();

auto allocator = rm.getAllocator("UM");
auto pool = rm.makeAllocator<umpire::strategy::QuickPool>("POOL", allocator);

a = static_cast<double *>(pool.allocate(N*M*sizeof(double)));
a_t = static_cast<double *>(pool.allocate(N*M*sizeof(double)));

constexpr int DIM = 2;

RAJA::View<double, RAJA::Layout<DIM>> A(a, N, M);
RAJA::View<double, RAJA::Layout<DIM>> A_t(a_t, M, N);

RAJA::TypedRangeSegment<int> row_range(0, N);
RAJA::TypedRangeSegment<int> col_range(0, M);
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@artv3 artv3 Jul 1, 2025

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Where is the TODO part of the exercise?


using EXEC_POL =
RAJA::KernelPolicy<
RAJA::statement::CudaKernel<
RAJA::statement::For<1, RAJA::cuda_global_size_y_loop<8>,
RAJA::statement::For<0, RAJA::cuda_global_size_x_direct<32>,
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@artv3 artv3 Jul 7, 2025

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Do you think we will have to explain the block sizes at the tutorial?

RAJA::statement::Lambda<0>
>
>
>
>;

RAJA::kernel<EXEC_POL>(RAJA::make_tuple(col_range, row_range),
[=] RAJA_DEVICE (int col, int row) {
A_t(col, row) = A(row, col);
});

pool.deallocate(a);
pool.deallocate(a_t);

return 0;
}
4 changes: 4 additions & 0 deletions Intro_Tutorial/lessons/11_raja_device_kernel/CMakeLists.txt
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Expand Up @@ -3,4 +3,8 @@ if (ENABLE_CUDA)
NAME 11_raja_device_kernel
SOURCES 11_raja_device_kernel.cpp
DEPENDS_ON RAJA umpire cuda)
blt_add_executable(
NAME 11_raja_transpose_kernel
SOURCES 11_raja_transpose_kernel.cpp
DEPENDS_ON RAJA umpire cuda)
endif()
50 changes: 50 additions & 0 deletions Intro_Tutorial/lessons/11_raja_device_kernel/solution/11_raja_transpose_kernel_solution.cpp
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#include <iostream>

#include "RAJA/RAJA.hpp"
#include "umpire/Umpire.hpp"
#include "umpire/strategy/QuickPool.hpp"

int main()
{
constexpr int N{10000};
constexpr int M{7000};
double* a{nullptr};
double* a_t{nullptr};

auto& rm = umpire::ResourceManager::getInstance();

auto allocator = rm.getAllocator("UM");
auto pool = rm.makeAllocator<umpire::strategy::QuickPool>("POOL", allocator);

a = static_cast<double *>(pool.allocate(N*M*sizeof(double)));
a_t = static_cast<double *>(pool.allocate(N*M*sizeof(double)));

constexpr int DIM = 2;

RAJA::View<double, RAJA::Layout<DIM>> A(a, N, M);
RAJA::View<double, RAJA::Layout<DIM>> A_t(a_t, M, N);

RAJA::TypedRangeSegment<int> row_range(0, N);
RAJA::TypedRangeSegment<int> col_range(0, M);

using EXEC_POL =
RAJA::KernelPolicy<
RAJA::statement::CudaKernel<
RAJA::statement::For<1, RAJA::cuda_global_size_y_loop<8>,
RAJA::statement::For<0, RAJA::cuda_global_size_x_direct<32>,
RAJA::statement::Lambda<0>
>
>
>
>;

RAJA::kernel<EXEC_POL>(RAJA::make_tuple(col_range, row_range),
[=] RAJA_DEVICE (int col, int row) {
A_t(col, row) = A(row, col);
});

pool.deallocate(a);
pool.deallocate(a_t);

return 0;
}