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| Content Provider | ACM Digital Library |
|---|---|
| Author | Gregg, Chris Mistry, Perhaad Hazelwood, Kim Rubin, Norman Kaeli, David |
| Abstract | Many developers have begun to realize that heterogeneous multi-core and many-core computer systems can provide significant performance opportunities to a range of applications. Typical applications possess multiple components that can be parallelized; developers need to be equipped with proper performance tools to analyze program flow and identify application bottlenecks. In this paper, we analyze and profile the components of the Speeded Up Robust Features (SURF) Computer Vision algorithm written in OpenCL. Our profiling framework is developed using built-in OpenCL API function calls, without the need for an external profiler. We show we can begin to identify performance bottlenecks and performance issues present in individual components on different hardware platforms. We demonstrate that by using run-time profiling using the OpenCL specification, we can provide an application developer with a fine-grained look at performance, and that this information can be used to tailor performance improvements for specific platforms. |
| Starting Page | 1 |
| Ending Page | 8 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781450305693 |
| DOI | 10.1145/1964179.1964193 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2011-03-05 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Surf Computer vision Performance tools Profiling Opencl Heterogeneous computing Gpgpu |
| Content Type | Text |
| Resource Type | Article |
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