Título:
Towards reducing communications in sparse matrix kernels
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Autor:
Freire, Manuel
Marichal, Raúl
Dufrechou, Ernesto
Ezzatti, Pablo
Marichal, Raúl
Dufrechou, Ernesto
Ezzatti, Pablo
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Tipo de documento:
Preprint
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Palabras clave:
Sparse matrices
Memory access
Reordering technique
Matrix storage reduction
Memory access
Reordering technique
Matrix storage reduction
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Año de publicación:
2023
Contenido:
Resumen:
The significant presence that many-core devices like GPUs have these days, and their enormous computational power, motivates the study of sparse matrix operations in this hardware. The essential sparse
kernels in scientific computing, such as the sparse matrix-vector multiplication (SpMV), usually have many different high-performance GPU implementations. Sparse matrix problems typically imply memory-bound
operations, and this characteristic is particularly limiting in massively parallel processors. This work revisits the main ideas about reducing the volume of data required by sparse storage formats and advances in understanding some compression techniques. In particular, we study the use of index compression combined with sparse matrix reordering techniques.
The systematic experimental evaluation on a large set of real-world matrices confirms that this approach is promising, achieving meaningful data storage reductions.
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Financiadores:
FCE_3_2022_1_172419 - MODELAR: Modelado del desempeñO de métoDos numÉricos en pLataformas de hArdware heteRogéneas.
Citación:
Freire, M., Marichal, R., Dufrechou, E. y otros. Towards reducing communications in sparse matrix kernels [Preprint] Publicado en : Naiouf, M., Rucci, E., Chichizola, F., De Giusti, L. (eds) Cloud Computing, Big Data & Emerging Topics. JCC-BD&ET 2023. Communications in Computer and Information Science, vol 1828. Springer, Cham. https://doi.org/10.1007/978-3-031-40942-4_2.
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Licencia:
Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)
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| Ficheros | Descripción | Tamaño | Formato | ||
|---|---|---|---|---|---|
| FMDE23.pdf | Preprint | 679.39 KB | Adobe PDF |