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An enhancer of memory and network for applications with large-capacity data and non-continuous data accessing

机译:用于具有大容量数据和非连续数据访问的应用程序的内存和网络增强器

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The performance of memory and I/O systems is insufficient to catch up with that of COTS (Commercial Off-The-Shelf) CPU. PC clusters using COTS CPU have been employed for HPC. A cache-based processor is far less effective than a vector processor in applications with low spatial locality. Moreover, for HPC, Google-like server farms and database processing, insufficient capacity of main memory poses a serious problem. Power consumption of a Google-like server farm or a high-end HPC PC cluster is huge. In order to overcome these problems, we propose a concept of a memory and network enhancer equipped with scatter and gather vector access functions, high-performance network connectivity, and capacity extensibility. Communication mechanisms named LHS and LHC are also proposed. LHS and LHC are architectures for reducing latency for mixed messages with small controlling data and large data body. Examples of the killer applications of this new type of hardware are presented. This paper presents not only concepts and simulations but also real hardware prototypes named DIMMnet-2 and DIMMnet-3. This paper presents the evaluations concerning memory issues and network issues. We evaluate the module with NAS CG benchmark class C and Wisconsin benchmarks as applications with memory issues. Although evaluation for CG class C is difficult with conventional cycle-accurate simulation methods, we obtained the result for class C with our original method. As a result, we find that the module can improve its maximum performance about 25 times more with Wisconsin benchmarks. However, the results on a cache-based PC show the cache-line flushing degrades acceleration ratio. This shows the high potential of the proposed extended memory module and processors in combination with DMA-based main memory access such as SPU on Cell/B.E. that does not need cache-line flushing. The LHS and LHC communication mechanisms are evaluated in this paper. The evaluations of their effects on latency are shown.
机译:内存和I / O系统的性能不足以赶上COTS(商用现货)CPU的性能。使用COTS CPU的PC群集已用于HPC。在空间局部性较低的应用程序中,基于缓存的处理器远不如矢量处理器有效。此外,对于HPC,类似Google的服务器场和数据库处理,主内存容量不足会带来严重的问题。类似Google的服务器场或高端HPC PC群集的功耗非常大。为了克服这些问题,我们提出了一种内存和网络增强器的概念,该增强器具有分散和收集矢量访问功能,高性能网络连接性和容量可扩展性。还提出了名为LHS和LHC的通信机制。 LHS和LHC是用于减少具有较小控制数据和较大数据主体的混合消息的延迟的体系结构。给出了这种新型硬件的杀手级应用示例。本文不仅介绍了概念和模拟,还介绍了名为DIMMnet-2和DIMMnet-3的实际硬件原型。本文介绍了有关内存问题和网络问题的评估。我们将具有NAS CG基准C类和威斯康星州基准的模块评估为存在内存问题的应用程序。尽管使用常规的周期精确仿真方法很难评估CG类C,但是我们还是使用原始方法获得了C类的结果。结果,我们发现,使用威斯康星基准测试,该模块可以将其最大性能提高约25倍。但是,基于缓存的PC上的结果显示,缓存行刷新会降低加速比。这表明,与基于DMA的主存储器访问(如Cell / B.E上的SPU)相结合,所提出的扩展存储模块和处理器具有很高的潜力。不需要缓存行刷新。本文对LHS和LHC的通信机制进行了评估。显示了它们对延迟的影响的评估。

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