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Analysis of a Computational Biology Simulation Technique on Emerging Processing Architectures

机译:新兴处理架构上的计算生物学仿真技术分析

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Multi-paradigm, multi-threaded and multi-core computing devices available today provide several orders of magnitude performance improvement over mainstream microprocessors. These devices include the STI Cell Broadband Engine, graphical processing units (GPU) and the Cray massively-multithreaded processors - available in desktop computing systems as well as proposed for supercomputing platforms. The main challenge in utilizing these powerful devices is their unique programming paradigms. GPUs and the Cell systems require code developers to manage code and data explicitly, while the Cray multithreaded architecture requires them to generate a very large number of threads or independent tasks concurrently. In this paper, we explain strategies for optimizing a molecular dynamics (MD) calculation that is used in biomolecular simulations on three devices: Cell, GPU and MTA-2. We show that the Cray MTA-2 system requires minimal code modification and does not outperform the microprocessor runs; but it demonstrates an improved workload scaling behavior over the microprocessor implementation. On the other hand, substantial porting and optimization efforts on the Cell and the GPU systems result in a 5times to 6times improvement, respectively, over a 2.2 GHz Opteron system.
机译:当今可用的多范例,多线程和多核计算设备与主流微处理器相比,性能提高了几个数量级。这些设备包括STI单元宽带引擎,图形处理单元(GPU)和Cray大规模多线程处理器-可在台式机计算系统中使用,也可在超级计算平台中使用。利用这些功能强大的设备的主要挑战是其独特的编程范例。 GPU和Cell系统要求代码开发人员显式管理代码和数据,而Cray多线程体系结构要求它们同时生成大量线程或独立任务。在本文中,我们介绍了用于优化分子动力学(MD)计算的策略,该算法用于以下三种设备的生物分子模拟:Cell,GPU和MTA-2。我们证明了Cray MTA-2系统需要最少的代码修改,并且性能不超过微处理器。但它展示了在微处理器实现方面改进的工作负载扩展行为。另一方面,与2.2 GHz Opteron系统相比,在Cell和GPU系统上的大量移植和优化工作分别使性能提高了5倍至6倍。

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