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GPU Performance Estimation using Software Rasterization and Machine Learning

2017; Association for Computing Machinery; Volume: 16; Issue: 5s Linguagem: Inglês

10.1145/3126557

1558-3465

Kenneth O‘Neal, Philip Brisk, Ahmed Abousamra, Zack Waters, Emily Shriver,

Software System Performance and Reliability

This paper introduces a predictive modeling framework to estimate the performance of GPUs during pre-silicon design. Early-stage performance prediction is useful when simulation times impede development by rendering driver performance validation, API conformance testing and design space explorations infeasible. Our approach builds a Random Forest regression model to analyze DirectX 3D workload behavior when executed by a software rasterizer, which we have extended with a workload characterizer to collect further performance information via program counters. In addition to regression models, this work produces detailed feature rankings which can provide valuable architectural insight, and accurate performance estimates for an Intel integrated Skylake generation GPU. Our models achieve reasonable out-of-sample-error rates of 14%, with an average simulation speedup of 327x.