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Update LLM prompt for sparse_axpy to reflect the output 'z' as a dense vector #34

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Mar 25, 2025
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Update LLM prompt for sparse_axpy to reflect the output 'z' as a dense vector #34

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2239edc
Update LLM prompt for sparse_axpy to reflect the output 'z' as a dens…
harshithamenon Mar 24, 2025
894c9e7
explicitly denote z as dense
Dando18 Mar 25, 2025
933e7e6
update prompts json
Dando18 Mar 25, 2025
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2,878 changes: 1,439 additions & 1,439 deletions prompts/generation-prompts.json
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4 changes: 2 additions & 2 deletions prompts/raw/sparse_la/48_sparse_la_sparse_axpy/cuda
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Expand Up @@ -3,11 +3,11 @@ struct Element {
double value;
};

/* Compute z = alpha*x+y where x and y are sparse vectors of size Nx and Ny. Store the result in z.
/* Compute z = alpha*x+y where x and y are sparse vectors of size Nx and Ny. Store the result in the dense vector z.
Use CUDA to compute in parallel. The kernel is launched with at least as many threads as values in x or y.
Example:

input: x=[{5, 12}, {8, 3}, {12, -1}], y=[{3, 1}, {5, -2}, {7, 1}, {8, -3}], alpha=1
output: z=[{3, 1}, {5, 10}, {7, 1}, {12, -1}]
output: z=[0, 0, 0, 1, 0, 10, 0, 1, 0, 0, 0, 0, -1]
*/
__global__ void sparseAxpy(double alpha, const Element *x, const Element *y, double *z, size_t Nx, size_t Ny, size_t N) {
4 changes: 2 additions & 2 deletions prompts/raw/sparse_la/48_sparse_la_sparse_axpy/hip
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Original file line number Diff line number Diff line change
Expand Up @@ -3,11 +3,11 @@ struct Element {
double value;
};

/* Compute z = alpha*x+y where x and y are sparse vectors of size Nx and Ny. Store the result in z.
/* Compute z = alpha*x+y where x and y are sparse vectors of size Nx and Ny. Store the result in the dense vector z.
Use AMD HIP to compute in parallel. The kernel is launched with at least as many threads as values in x or y.
Example:

input: x=[{5, 12}, {8, 3}, {12, -1}], y=[{3, 1}, {5, -2}, {7, 1}, {8, -3}], alpha=1
output: z=[{3, 1}, {5, 10}, {7, 1}, {12, -1}]
output: z=[0, 0, 0, 1, 0, 10, 0, 1, 0, 0, 0, 0, -1]
*/
__global__ void sparseAxpy(double alpha, const Element *x, const Element *y, double *z, size_t Nx, size_t Ny, size_t N) {
4 changes: 2 additions & 2 deletions prompts/raw/sparse_la/48_sparse_la_sparse_axpy/kokkos
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Original file line number Diff line number Diff line change
Expand Up @@ -3,11 +3,11 @@ struct Element {
double value;
};

/* Compute z = alpha*x+y where x and y are sparse vectors. Store the result in z.
/* Compute z = alpha*x+y where x and y are sparse vectors. Store the result in the dense vector z.
Use Kokkos to compute in parallel. Assume Kokkos has already been initialized.
Example:

input: x=[{5, 12}, {8, 3}, {12, -1}], y=[{3, 1}, {5, -2}, {7, 1}, {8, -3}], alpha=1
output: z=[{3, 1}, {5, 10}, {7, 1}, {12, -1}]
output: z=[0, 0, 0, 1, 0, 10, 0, 1, 0, 0, 0, 0, -1]
*/
void sparseAxpy(double alpha, Kokkos::View<const Element*> &x, Kokkos::View<const Element*> &y, Kokkos::View<double*> &z) {
4 changes: 2 additions & 2 deletions prompts/raw/sparse_la/48_sparse_la_sparse_axpy/mpi
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Expand Up @@ -3,12 +3,12 @@ struct Element {
double value;
};

/* Compute z = alpha*x+y where x and y are sparse vectors. Store the result in z.
/* Compute z = alpha*x+y where x and y are sparse vectors. Store the result in the dense vector z.
Use MPI to compute in parallel. Assume MPI has already been initialized.
Every rank has a complete copy of x and y. Store the result in z on rank 0.
Example:

input: x=[{5, 12}, {8, 3}, {12, -1}], y=[{3, 1}, {5, -2}, {7, 1}, {8, -3}], alpha=1
output: z=[{3, 1}, {5, 10}, {7, 1}, {12, -1}]
output: z=[0, 0, 0, 1, 0, 10, 0, 1, 0, 0, 0, 0, -1]
*/
void sparseAxpy(double alpha, std::vector<Element> const& x, std::vector<Element> const& y, std::vector<double> &z) {
4 changes: 2 additions & 2 deletions prompts/raw/sparse_la/48_sparse_la_sparse_axpy/mpi+omp
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Original file line number Diff line number Diff line change
Expand Up @@ -3,12 +3,12 @@ struct Element {
double value;
};

/* Compute z = alpha*x+y where x and y are sparse vectors. Store the result in z.
/* Compute z = alpha*x+y where x and y are sparse vectors. Store the result in the dense vector z.
Use MPI and OpenMP to compute in parallel. Assume MPI has already been initialized.
Every rank has a complete copy of x and y. Store the result in z on rank 0.
Example:

input: x=[{5, 12}, {8, 3}, {12, -1}], y=[{3, 1}, {5, -2}, {7, 1}, {8, -3}], alpha=1
output: z=[{3, 1}, {5, 10}, {7, 1}, {12, -1}]
output: z=[0, 0, 0, 1, 0, 10, 0, 1, 0, 0, 0, 0, -1]
*/
void sparseAxpy(double alpha, std::vector<Element> const& x, std::vector<Element> const& y, std::vector<double> &z) {
4 changes: 2 additions & 2 deletions prompts/raw/sparse_la/48_sparse_la_sparse_axpy/omp
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Original file line number Diff line number Diff line change
Expand Up @@ -3,11 +3,11 @@ struct Element {
double value;
};

/* Compute z = alpha*x+y where x and y are sparse vectors. Store the result in z.
/* Compute z = alpha*x+y where x and y are sparse vectors. Store the result in the dense vector z.
Use OpenMP to compute in parallel.
Example:

input: x=[{5, 12}, {8, 3}, {12, -1}], y=[{3, 1}, {5, -2}, {7, 1}, {8, -3}], alpha=1
output: z=[{3, 1}, {5, 10}, {7, 1}, {12, -1}]
output: z=[0, 0, 0, 1, 0, 10, 0, 1, 0, 0, 0, 0, -1]
*/
void sparseAxpy(double alpha, std::vector<Element> const& x, std::vector<Element> const& y, std::vector<double> &z) {
4 changes: 2 additions & 2 deletions prompts/raw/sparse_la/48_sparse_la_sparse_axpy/serial
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Original file line number Diff line number Diff line change
Expand Up @@ -3,10 +3,10 @@ struct Element {
double value;
};

/* Compute z = alpha*x+y where x and y are sparse vectors. Store the result in z.
/* Compute z = alpha*x+y where x and y are sparse vectors. Store the result in the dense vector z.
Example:

input: x=[{5, 12}, {8, 3}, {12, -1}], y=[{3, 1}, {5, -2}, {7, 1}, {8, -3}], alpha=1
output: z=[{3, 1}, {5, 10}, {7, 1}, {12, -1}]
output: z=[0, 0, 0, 1, 0, 10, 0, 1, 0, 0, 0, 0, -1]
*/
void sparseAxpy(double alpha, std::vector<Element> const& x, std::vector<Element> const& y, std::vector<double> &z) {