Application performance analysis and efficient execution on systems with multi-core CPUs, GPUs and MICs: a case study with microscopy image analysis

• Authors: George Teodoro, University of Brasília; Tahsin Kurc, Stony Brook University; Guilherme Andrade, Federal University of Minas Gerais; Jun Kong, Emory University; Renato Ferreira, Federal University of Minas Gerais; Joel Saltz, Stony Brook University
• Journal Title: International Journal of High Performance Computing Applications
• Volume: Volume 31, Number 1
• Publisher: SAGE Publications | 2017-01-01, Pages 32-51
• Type of Work: Article | Post-print: After Peer Review
• Publisher DOI: http://doi.org/10.1177/1094342015594519
• Subjects: Biology, Bioinformatics; Computer Science
• Keywords: Science & Technology; Technology; Computer Science, Hardware & Architecture; Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods; Computer Science; Hybrid systems; GPGPU; Intel Xeon Phi; cooperative execution; image analysis; MODEL; ARCHITECTURES; ALGORITHMS

Abstract

We carry out a comparative performance study of multi-core CPUs, GPUs and Intel Xeon Phi (Many Integrated Core-MIC) with a microscopy image analysis application. We experimentally evaluate the performance of computing devices on core operations of the application. We correlate the observed performance with the characteristics of computing devices and data access patterns, computation complexities, and parallelization forms of the operations. The results show a significant variability in the performance of operations with respect to the device used. The performances of operations with regular data access are comparable or sometimes better on a MIC than that on a GPU. GPUs are more efficient than MICs for operations that access data irregularly, because of the lower bandwidth of the MIC for random data accesses. We propose new performance-aware scheduling strategies that consider variabilities in operation speedups. Our scheduling strategies significantly improve application performance compared to classic strategies in hybrid configurations.