Lessons Learned: Contacts: [email protected], Patrick.Viry

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Lessons Learned: Contacts: Mathias.Beck@Unige.Ch, Patrick.Viry

Running Java on GPUs An ESA Innovation Triangle Initiative with: • ESA’s Gaia mission • Observatoire de Genève • Ateji All Gaia code is in Java Introducing OpenCL or CUDA in the development process would be a huge disruption Ateji PX embeds parallelism in Java Multi-core parallelism already available, GPU target developed during this project →Port compute-intensive period search algorithms to the GPU Deeming, Lomb-Scargle, String-Length How it works: • Source-to-source translation from fragments of Java to OpenCL • Computation locality and communications expressed with Ateji PX extensions 1. Express parallelism with || Benchmarks: for(Obs o : observations) ... Becomes || (Obs o : observations) ... ► Computation now uses all available cores. 2. Express distribution to GPU with # annotations || (#OpenCL(), Obs o : observs) // these branches run on GPU || (...) // these branches run on CPU ► Mix of multicore and GPU computation 3. Express communication with message passing Channel c = ...; [ Lessons learned: || c ! value; || c ? value; • Parallelism easy to introduce in the code ] • No disruption in the Java devt. process ► Overlap of computation and communication • Pretty good speed-up on multi-cores • Similar perf. figures for GPU and 16-core GPU 4. Map the GPU memory hierarchy to lexical scope • But depend heavily on the hardware • Still a lot of room for improvement • Code needs refactoring for performance on GPU, such as loop pipelining and workgroup sizing • Even though we code in Java, detailed knowledge of GPU architecture is required • Performance / price ratio on multi-core vs. CPU • Not decisive • But hardware is evolving rapidly Contacts: [email protected], [email protected] Downloads & Documentation: www.ateji.com/px .

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Multiprocessing Contents
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