no code implementations • 12 Sep 2023 • Pedro Valero-Lara, Alexis Huante, Mustafa Al Lail, William F. Godoy, Keita Teranishi, Prasanna Balaprakash, Jeffrey S. Vetter
We evaluate the use of the open-source Llama-2 model for generating well-known, high-performance computing kernels (e. g., AXPY, GEMV, GEMM) on different parallel programming models and languages (e. g., C++: OpenMP, OpenMP Offload, OpenACC, CUDA, HIP; Fortran: OpenMP, OpenMP Offload, OpenACC; Python: numpy, Numba, pyCUDA, cuPy; and Julia: Threads, CUDA. jl, AMDGPU. jl).
no code implementations • 27 Jun 2023 • William F. Godoy, Pedro Valero-Lara, Keita Teranishi, Prasanna Balaprakash, Jeffrey S. Vetter
We evaluate AI-assisted generative capabilities on fundamental numerical kernels in high-performance computing (HPC), including AXPY, GEMV, GEMM, SpMV, Jacobi Stencil, and CG.
no code implementations • 30 Mar 2021 • Marc Jordà, Pedro Valero-Lara, Antonio J. Peña
Convolutions are the core operation of deep learning applications based on Convolutional Neural Networks (CNNs).
