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GTC On-Demand

Performance Optimization
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Java Image Processing: How Runtime Compilation Transforms Memory-Bound into Compute-Bound
Florent Duguet (ALTIMESH)
A wide variety of image processing algorithms are typically parallel. However, depending on filter-size or neighborhood search pattern, memory access is critical for performances. We'll show how loop reordering and memory locality fine-tuning help ...Read More
A wide variety of image processing algorithms are typically parallel. However, depending on filter-size or neighborhood search pattern, memory access is critical for performances. We'll show how loop reordering and memory locality fine-tuning help achieve best performance. Using Hybridizer to automate Java byte-code transformation to CUDA source code, and using new CUDA feature Run Time Compilation, we transformed execution from memory-bound to compute-bound. Applying this technique to oil and gas image processing algorithms results in interactive response time on production-size datasets.  Back
 
Keywords:
Performance Optimization, Energy Exploration, Video and Image Processing, GTC Silicon Valley 2016 - ID S6314
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