Languages and Compilers for Parallel Computing: 24th by Okwan Kwon, Fahed Jubair, Seung-Jai Min (auth.), Sanjay

By Okwan Kwon, Fahed Jubair, Seung-Jai Min (auth.), Sanjay Rajopadhye, Michelle Mills Strout (eds.)

This ebook constitutes the completely refereed post-conference lawsuits of the twenty fourth foreign Workshop on Languages and Compilers for Parallel Computing, LCPC 2011, held in castle Collins, CO, united states, in September 2011. the nineteen revised complete papers offered and 19 poster papers have been rigorously reviewed and chosen from fifty two submissions. The scope of the workshop spans the theoretical and functional points of parallel and high-performance computing, and goals parallel structures together with concurrent, multithreaded, multicore, accelerator, multiprocessor, and cluster systems.

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Extra info for Languages and Compilers for Parallel Computing: 24th International Workshop, LCPC 2011, Fort Collins, CO, USA, September 8-10, 2011. Revised Selected Papers

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It is not made structured or abstract by the programming model itself. Instead, it’s structure depends on the algorithms used by the programmer and can vary greatly from program to program. We can, however, take advantage of the same Sluice programming conventions that make translation feasible in the first place. Because program state is contained in the this scope, the system can easily look up the data. That is, it can determine the location, type and contents of a kernel’s state when its work function is called.

59 W, namely the eight core execution gives us 78% power reduction. These results show the simultaneous execution of multiple low-load application programs with OSCAR compiler’s power control gives us huge reduction in a single application average power consumption. 0 seconds, are executed in parallel. 7 seconds to 40 H. Mikami et al. process and manages to satisfy the real-time deadline with the almost highest clock frequency. Figure 7 shows consumed power when one to four intermediate-load AAC encoders are executed in parallel using different numbers of processor cores on the RP2.

This is the simplest form of task parallelism exposed by Sluice programs. To test this, we created a Sluice program that sequentially (because JavaScript is single threaded) creates, compiles, and executes a varying number of image kernels using the SKIR runtime. We give the runtime 8 worker threads (equal to the number of hardware threads) to run the kernels, so up to 8 kernel instances 28 J. Fifield and D. Grunwald               Fig. 8. Speedup of the nbody benchmark due to data parallelism when the CalculateForces kernel is run on a multi-threaded SKIR runtime compared to the same benchmark using a single-threaded SKIR runtime.

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