A Simple Model for Portable and Fast Prediction of Execution Time and Power Consumption of GPU Kernels
- 30 December 2020
- journal article
- research article
- Published by Association for Computing Machinery (ACM) in ACM Transactions on Architecture and Code Optimization
- Vol. 18 (1), 1-25
- https://doi.org/10.1145/3431731
Abstract
Characterizing compute kernel execution behavior on GPUs for efficient task scheduling is a non-trivial task. We address this with a simple model enabling portable and fast predictions among different GPUs using only hardware-independent features. This model is built based on random forests using 189 individual compute kernels from benchmarks such as Parboil, Rodinia, Polybench-GPU, and SHOC. Evaluation of the model performance using cross-validation yields a median Mean Average Percentage Error (MAPE) of 8.86–52.0% for time and 1.84–2.94% for power prediction across five different GPUs, while latency for a single prediction varies between 15 and 108 ms.Keywords
Funding Information
- Federal Ministry of Education and Research of Germany (01IH16007)
This publication has 34 references indexed in Scilit:
- Flexible software profiling of GPU architecturesPublished by Association for Computing Machinery (ACM) ,2015
- GPUMech: GPU Performance Modeling Technique Based on Interval AnalysisPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2014
- Power Modeling for GPU Architectures Using McPATACM Transactions on Design Automation of Electronic Systems, 2014
- A Novel Computational Model for GPUs with Application to I/O Optimal Sorting AlgorithmsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2014
- SparrowPublished by Association for Computing Machinery (ACM) ,2013
- A Roofline Model of EnergyPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2013
- An adaptive performance modeling tool for GPU architecturesACM SIGPLAN Notices, 2010
- A bias correction for the minimum error rate in cross-validationThe Annals of Applied Statistics, 2009
- A regression-based approach to scalability predictionPublished by Association for Computing Machinery (ACM) ,2008
- A bridging model for parallel computationCommunications of the ACM, 1990