Download Advanced Parallel Processing Technologies: 10th by Foivos S. Zakkak, Dimitrios Chasapis (auth.), Chenggang Wu, PDF

By Foivos S. Zakkak, Dimitrios Chasapis (auth.), Chenggang Wu, Albert Cohen (eds.)

This publication constitutes the refereed post-proceedings of the tenth foreign Symposium on complicated Parallel Processing applied sciences, APPT 2013, held in Stockholm, Sweden, in August 2013. The 30 revised complete papers provided have been conscientiously reviewed and chosen from sixty two submissions. The papers disguise quite a lot of themes taking pictures a few of the state-of-the-art and perform in parallel structure, parallel software program, concurrent and disbursed platforms, and cloud computing, with a spotlight on computing structures for large info applications.

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Tzenakis et al. but this may make metadata visible to the programmer, require significant additional changes to the program source and memory layout. We achieve the best of both solutions using a custom memory allocator that allows for fast lookup of metadata, while still hiding metadata management in the runtime system. The dependence analysis on blocks is quite similar to dependence tracking on whole objects. There can be, however, extra overhead, as a task argument may consist of multiple blocks, and dependencies must be tracked on each such block.

The kernel is data parallel, communication bound and memory intensive, thus highlighting the analysis overhead. 3× slowdown compared to OpenMP on 32 cores. 1 vs. 1 with OpenMP. BDDT allows the programmer to selectively apply or turn off the dependence analysis per task argument. The “w/o analysis” line in Figure 2(c) shows the performance of BDDT with dependence analysis disabled for all arguments, via data annotations. BDDT performs identical to OpenMP on up to 16 cores. For 16 cores or more, BDDT outperforms OpenMP by 15% to 45%, a difference that increases with the core count.

The “w/o analysis” line in Figure 2(c) shows the performance of BDDT with dependence analysis disabled for all arguments, via data annotations. BDDT performs identical to OpenMP on up to 16 cores. For 16 cores or more, BDDT outperforms OpenMP by 15% to 45%, a difference that increases with the core count. The result indicates that BDDT’s implementation of the runtime system is efficient, scalable, and can be used by both conventional task-based models and advanced models with out-of-order task execution capabilities.

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