Journal
INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS
Volume 31, Issue 1, Pages 32-51Publisher
SAGE PUBLICATIONS LTD
DOI: 10.1177/1094342015594519
Keywords
Hybrid systems; GPGPU; Intel Xeon Phi; cooperative execution; image analysis
Categories
Funding
- NCI [HHSN261200800001E, 1U24CA180924-01A1]
- NLM [R01LM011119-01, R01LM009239]
- National Institutes of Health (NIH) [RC4MD005964, K25CA181503]
- CNPq
- CAPES
- FINEP
- Fapemig
- INWEB
- NSF [OCI-0910735]
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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 with classic strategies in hybrid configurations.
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