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• Learning-based Analytical Cross-Platform Performance Prediction (pdf, software)
  X. Zheng, P. Ravikumar, L. John, A. Gerstlauer.
  In International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation, 2015.
  
  Bibtex:
  
  @inproceedings{zheng2015learningb, author = "Xinnian Zheng AND Pradeep Ravikumar AND Lizy K. John AND Andreas Gerstlauer", title = "Learning-based Analytical Cross-Platform Performance Prediction", booktitle = "International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation", year = "2015", abstract = "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 database, and demonstrate the prediction accuracy on standard benchmark suites. Result show that our approach achieves on average more than 90% accuracy at 160× the speed compared to a cycle-accurate reference simulation." }