High Performance Computing Co-Design Strategies

J. Ang
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引用次数: 1

Abstract

The MEMSYS Call for Papers contains this passage: Many of the problems we see in the memory system are cross-disciplinary in nature -- their solution would likely require work at all levels, from applications to circuits. Thus, while the scope of the problem is memory, the scope of the solutions will be much wider. The Department of Energy's (DOE) high performance computing (HPC) community is thinking about how to define, support and execute work at all levels for the development of future supercomputers to run our portfolio of mission applications. Borrowing a concept from embedded computing, the DOE HPC community is calling our work at all levels co-design [1]. Co-design for embedded computing is focused on hardware/software partitioning of activities to execute a well-defined task within specific constraints. Co-design for general-purpose HPC has many dimensions for both the work to be performed and the constraints, e.g. hardware designs, runtime software, applications and algorithms. The subject of this extended abstract is a description of two alternative DOE HPC co-design strategies. While DOE co-design efforts include more than the memory system, as noted in the MEMSYS call, the memory system impacts applications, circuits and all levels between.
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高性能计算协同设计策略
MEMSYS征文中包含了这样一段话:我们在存储系统中看到的许多问题本质上都是跨学科的——它们的解决方案可能需要从应用到电路的各个层面的工作。因此,虽然问题的范围是内存,但解决方案的范围将更广。美国能源部(DOE)高性能计算(HPC)社区正在考虑如何定义、支持和执行各级工作,以开发未来的超级计算机,以运行我们的任务应用组合。借用嵌入式计算的概念,DOE HPC社区将我们在各个层面的工作称为协同设计[1]。嵌入式计算的协同设计侧重于活动的硬件/软件划分,以便在特定的约束条件下执行定义良好的任务。通用高性能计算的协同设计在工作和约束方面都有很多方面,例如硬件设计、运行时软件、应用程序和算法。这个扩展摘要的主题是描述两种可选的DOE高性能计算协同设计策略。虽然美国能源部的协同设计工作不仅仅包括存储系统,正如MEMSYS呼叫中所指出的那样,存储系统影响应用、电路以及两者之间的所有层面。
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