2026-04-27T21:21:53Zhttps://uvadoc.uva.es/oai/requestoai:uvadoc.uva.es:10324/744712025-02-13T08:08:47Zcom_10324_1165com_10324_931com_10324_894col_10324_1335
UVaDOC
author
Cámara Moreno, Jesús
author
Cuenca, Javier
author
Boratto, Murilo
2025-01-27T20:03:13Z
2025-01-27T20:03:13Z
2023
Lecture Notes in Computer Science, 2023, Volume 14073, Pages 668-682
0302-9743
https://uvadoc.uva.es/handle/10324/74471
10.1007/978-3-031-35995-8_47
668
682
Lecture Notes in Computer Science
14073
1611-3349
This work presents several self-optimization strategies to improve the performance of task-based linear algebra software on heterogeneous systems. The study focuses on Chameleon, a task-based dense linear algebra software whose routines are computed using a tile-based algorithmic scheme and executed in the available computing resources of the system using a scheduler which dynamically handles data dependencies among the basic computational kernels of each linear algebra routine. The proposed strategies are applied to select the best values for the parameters that affect the performance of the routines, such as the tile size or the scheduling policy, among others. Also, parallel optimized implementations provided by existing linear algebra libraries, such as Intel MKL (on multicore CPU) or cuBLAS (on GPU) are used to execute each of the computational kernels of the routines. Results obtained on a heterogeneous system composed of several multicore and multiGPU are satisfactory, with performances close to the experimental optimum.
eng
Computación Heterogénea
Auto-Tuning
Improving the Performance of Task-Based Linear Algebra Software with Autotuning Techniques on Heterogeneous Architectures
info:eu-repo/semantics/article
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URL
https://uvadoc.uva.es/bitstream/10324/74471/1/Improving_Performance_Task-based_Libraries_ICCS_2023.pdf
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Improving_Performance_Task-based_Libraries_ICCS_2023.pdf