2026-05-27T05:29:46Zhttps://riuma.uma.es/rest/oai/requestoai:riuma.uma.es:10630/249212026-02-03T12:27:11Zcom_10630_2254col_10630_37959
RIUMA. Repositorio Institucional de la Universidad de Málaga
author
López Albelda, Bernabé
author
Castro, Francisco M.
author
González-Linares, José María
author
Guil-Mata, Nicolás
2022-09-07T12:14:47Z
2022-09-07T12:14:47Z
2022-08
https://hdl.handle.net/10630/24921
Current execution of kernels on GPUs allows improving the use of hardware resources and reducing the execution time of co-executed kernels. In addition, efficient kernel-oriented scheduling policies pursuing criteria based on fairness or Quality of Service can be implemented. However, achieved co-executing performance strongly depends on how GPU resources are partitioned between kernels. Thus, precise slowdown models that predict accurate co-execution performance must be used to fulfill scheduling policy requirements.
Most recent slowdown models work with Spatial Multitask (SMT) partitioning, where Stream Multiprocessors (SMs) are distributed among tasks. In this work, we show that Simultaneous Multikernel (SMK) partitioning, where kernels share the SMs, obtains better performance. However, kernel interference in SMK occurs not only in global memory, as in the SMT case, but also within the SM, leading to high prediction errors. Here, we propose a modification of a previous state-of-the-art slowdown model to reduce median prediction error from 27.92% to 9.50%. Moreover, this new slowdown model is used to implement a scheduling policy that improves fairness by 1.41x on average compared to even partitioning, whereas previous models reach only 1.21x on average.
eng
GKS (Sistema de ordenador)
Kernel, Funciones de
A Hybrid Piece-Wise Slowdown Model for Concurrent Kernel Execution on GPU
conference output
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URL
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File
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3baac0dc98f085acb263fa4f04cb4b51
1361681
application/pdf
PresentacionEuroPar22.pdf