Dax Toolkit: A proposed framework for data analysis and visualization at Extreme Scale
Dax Toolkit: A proposed framework for data analysis and visualization at Extreme Scale
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Dax Toolkit：提出的超大规模数据分析和可视化框架
DOI：
10.1109/ldav.2011.6092323
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发表时间：
2011
期刊：
影响因子：
--
通讯作者：
K. Ma
中科院分区：
文献类型：
--
作者：
K. Moreland;Utkarsh Ayachit;Berk Geveci;K. Ma
专家们一致认为，百亿亿次计算机将由包含许多核心的处理器组成，这反过来又需要更高程度的并发性。软件将需要至少1000倍以上的并发性。目前，大多数并行分析和可视化算法都是通过对数据进行分区并在每个数据分区上并行运行串行算法来实现的。尽管这种方法很适合当前高性能计算的并发性，但它并没有表现出百亿亿级计算所需的适当的普遍并行性。数据分区太小，线程的开销太大，无法有效地利用超大规模机器中的所有核心。本文介绍了一种新的可视化框架，旨在展示极端规模机器所必需的普遍并行性。我们演示了该系统在GPU处理器上的使用，我们认为这是目前可用的最佳模拟exascale节点。
Experts agree that the exascale machine will comprise processors that contain many cores, which in turn will necessitate a much higher degree of concurrency. Software will require a minimum of a 1,000 times more concurrency. Most parallel analysis and visualization algorithms today work by partitioning data and running mostly serial algorithms concurrently on each data partition. Although this approach lends itself well to the concurrency of current high-performance computing, it does not exhibit the appropriate pervasive parallelism required for exascale computing. The data partitions are too small and the overhead of the threads is too large to make effective use of all the cores in an extreme-scale machine. This paper introduces a new visualization framework designed to exhibit the pervasive parallelism necessary for extreme scale machines. We demonstrate the use of this system on a GPU processor, which we feel is the best analog to an exascale node that we have available today.
DOI：
10.1177/1094342010391989
发表时间：
2011-02-01
影响因子：
3.1
作者：
Dongarra, Jack;Beckman, Pete;Yelick, Kathy
通讯作者：
Yelick, Kathy