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PRODID:-//Microsoft Corporation//Outlook MIMEDIR//EN
VERSION:2.0
BEGIN:VEVENT
DTSTART:20141118T231500Z
DTEND:20141119T010000Z
LOCATION:New Orleans Theater Lobby
DESCRIPTION;ENCODING=QUOTED-PRINTABLE:ABSTRACT: Modern GPUs can accelerate the execution of inherently data-parallel applications. However, real-world algorithm realizations on GPUs differ from synthetic benchmarks. In this paper we stress the performance limits of GPUs. We measure the performance of our CUDA implementation of a recommended image compression algorithm (CCSDS-122.0-B-1) for space data systems on NVIDIA GPUs for various image sizes and compare it to the execution on x86 CPUs. The control-intensive parts and irregular memory access patterns under-utilize the GPU architecture and effectively serialize large parts of the GPU kernel execution.=0AWe present the execution results on two systems with different CPU/GPU setups. In both systems, our GPU implementation of the algorithm eventually leads to a very moderate 1.1x to 2.1x speedup. Discussion of the performance bottlenecks leads to insights on how GPU execution can be improved in the future by either revising the algorithms or by supporting control-intensive execution on the GPU cores.
SUMMARY:Performance Grading of GPU-Based Implementation of Space Computing Systems Image Compression
PRIORITY:3
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