A Machine Learning Approach for Performance Prediction and Scheduling on Heterogeneous CPUs

  • Daniel Nemirovsky Barcelona Supercomputing Center
  • Tugberk Arkose Barcelona Supercomputing Center
  • Nikola Markovic Microsoft
  • Mario Nemirovsky Barcelona Supercomputing Center
  • Osman Unsal Barcelona Supercomputing Center
  • Adrian Cristal Barcelona Supercomputing Center

Abstract


As heterogeneous systems become more ubiquitous, computer architects will need to develop novel CPU scheduling techniques capable of exploiting the diversity of computational resources. Accurately estimating the performance of applications on different heterogeneous resources can provide a significant advantage to heterogeneous schedulers seeking to improve system performance. Recent advances in machine learning techniques including artificial neural network models have led to the development of powerful and practical prediction models for a variety of fields. As of yet, however, no significant leaps have been taken towards employing machine learning for heterogeneous scheduling in order to maximize system throughput. In this paper we propose a unique throughput maximizing heterogeneous CPU scheduling model that uses machine learning to predict the performance of multiple threads on diverse system resources at the scheduling quantum granularity. We demonstrate how lightweight artificial neural networks (ANNs) can provide highly accurate performance predictions for a diverse set of applications thereby helping to improve heterogeneous scheduling efficiency. We show that online training is capable of increasing prediction accuracy but deepening the complexity of the ANNs can result in diminishing returns. Notably, our approach yields 25% to 31% throughput improvements over conventional heterogeneous schedulers for CPU and memory intensive applications.
Keywords: Throughput, Job shop scheduling, Neurons, Predictive models, Hardware, Processor scheduling, System performance, machine learning, artificial neural networks, scheduling, heterogeneous systems
Published
2017-10-17
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NEMIROVSKY, Daniel; ARKOSE, Tugberk; MARKOVIC, Nikola; NEMIROVSKY, Mario; UNSAL, Osman; CRISTAL, Adrian. A Machine Learning Approach for Performance Prediction and Scheduling on Heterogeneous CPUs. In: INTERNATIONAL SYMPOSIUM ON COMPUTER ARCHITECTURE AND HIGH PERFORMANCE COMPUTING (SBAC-PAD), 29. , 2017, Campinas/SP. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2017 . p. 121-128.