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SAMOS
2015
Springer

Learning-based analytical cross-platform performance prediction

8 years 7 months ago
Learning-based analytical cross-platform performance prediction
—As modern processors are becoming increasingly complex, fast and accurate performance prediction is crucial during the early phases of hardware and software co-development. To accurately and efficiently predict the performance of a given software workload is, however, a challenging problem. Traditional cycle-accurate simulation is often too slow, while analytical models are not sufficiently accurate or still require target-specific execution statistics that may be slow or difficult to obtain. In this paper, we propose a novel learning-based approach for synthesizing analytical models that can accurately predict the performance of a workload on a target platform from various performance statistics obtained directly on a host platform using built-in hardware counters. Our learning approach relies on a one-time training phase using a cycle-accurate reference of the chosen target processor. We train our models on over 15,000 program instances from the ACM-ICPC programming contest da...
Xinnian Zheng, Pradeep Ravikumar, Lizy K. John, An
Added 17 Apr 2016
Updated 17 Apr 2016
Type Journal
Year 2015
Where SAMOS
Authors Xinnian Zheng, Pradeep Ravikumar, Lizy K. John, Andreas Gerstlauer
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