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SC ... conference proceedings. SC (Conference : Supercomputing)

Pub Date : 2015-11-01 DOI: 10.1145/2807591.2807595

Patricia Kovatch, Anthony Costa, Zachary Giles, Eugene Fluder, Hyung Min Cho, Svetlana Mazurkova

As personalized medicine becomes more integrated into healthcare, the rate at which human genomes are being sequenced is rising quickly together with a concomitant acceleration in compute and storage requirements. To achieve the most effective solution for genomic workloads without re-architecting the industry-standard software, we performed a rigorous analysis of usage statistics, benchmarks and available technologies to design a system for maximum throughput. We share our experiences designing a system optimized for the "Genome Analysis ToolKit (GATK) Best Practices" whole genome DNA and RNA pipeline based on an evaluation of compute, workload and I/O characteristics. The characteristics of genomic-based workloads are vastly different from those of traditional HPC workloads, requiring different configurations of the scheduler and the I/O subsystem to achieve reliability, performance and scalability. By understanding how our researchers and clinicians work, we were able to employ techniques not only to speed up their workflow yielding improved and repeatable performance, but also to make more efficient use of storage and compute resources.

随着个性化医疗越来越多地融入医疗保健，人类基因组测序的速度正在迅速上升，随之而来的是计算和存储需求的加速。为了在不重新构建行业标准软件的情况下实现最有效的基因组工作负载解决方案，我们对使用统计数据、基准测试和可用技术进行了严格的分析，以设计最大吞吐量的系统。我们分享了基于计算、工作负载和I/O特性评估的“基因组分析工具包(GATK)最佳实践”全基因组DNA和RNA管道优化系统的设计经验。基于基因组的工作负载的特征与传统的HPC工作负载有很大的不同，需要不同的调度器和I/O子系统配置来实现可靠性、性能和可伸缩性。通过了解我们的研究人员和临床医生的工作方式，我们不仅能够采用技术来加快他们的工作流程，产生改进的和可重复的性能，而且还可以更有效地利用存储和计算资源。

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引用次数: 9

Mapping to Irregular Torus Topologies and Other Techniques for Petascale Biomolecular Simulation. 不规则环面拓扑映射及其他千万亿级生物分子模拟技术。

SC ... conference proceedings. SC (Conference : Supercomputing)

Pub Date : 2014-01-01 DOI: 10.1109/SC.2014.12

James C Phillips, Yanhua Sun, Nikhil Jain, Eric J Bohm, Laxmikant V Kalé

Currently deployed petascale supercomputers typically use toroidal network topologies in three or more dimensions. While these networks perform well for topology-agnostic codes on a few thousand nodes, leadership machines with 20,000 nodes require topology awareness to avoid network contention for communication-intensive codes. Topology adaptation is complicated by irregular node allocation shapes and holes due to dedicated input/output nodes or hardware failure. In the context of the popular molecular dynamics program NAMD, we present methods for mapping a periodic 3-D grid of fixed-size spatial decomposition domains to 3-D Cray Gemini and 5-D IBM Blue Gene/Q toroidal networks to enable hundred-million atom full machine simulations, and to similarly partition node allocations into compact domains for smaller simulations using multiple-copy algorithms. Additional enabling techniques are discussed and performance is reported for NCSA Blue Waters, ORNL Titan, ANL Mira, TACC Stampede, and NERSC Edison.

目前部署的千万亿次超级计算机通常使用三维或多维的环形网络拓扑结构。虽然这些网络在几千个节点上对拓扑无关的代码表现良好，但拥有20000个节点的领导机器需要拓扑感知，以避免通信密集型代码的网络争用。由于输入/输出节点专用或硬件故障，导致节点分配形状不规则，存在孔洞，拓扑适应非常复杂。在流行的分子动力学程序NAMD的背景下，我们提出了将固定大小的空间分解域的周期性3d网格映射到3-D Cray Gemini和5-D IBM Blue Gene/Q环形网络的方法，以实现数亿原子的全机器模拟，并使用多副本算法将节点分配到紧凑的域以进行较小的模拟。讨论了NCSA Blue Waters、ORNL Titan、ANL Mira、TACC Stampede和NERSC Edison的其他使能技术，并报告了其性能。

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引用次数: 31

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