RT Conference Proceedings
T1 A Hybrid Piece-Wise Slowdown Model for Concurrent Kernel Execution on GPU
A1 López Albelda, Bernabé
A1 Castro, Francisco M.
A1 González-Linares, José María
A1 Guil-Mata, Nicolás
K1 GKS (Sistema de ordenador)
K1 Kernel, Funciones de
AB 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.
PB José Cano, Phil Trinder
YR 2022
FD 2022-08
LK https://hdl.handle.net/10630/24921
UL https://hdl.handle.net/10630/24921
LA eng
NO Universidad de Málaga. Campus de Excelencia Internacional Andalucía TechP18-FR-3130UMA20-FEDERJA-059PID2019-105396RB-I00
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 3 mar 2026