Modeling and System Support to Balance the Resource Demand and Supply in High Performance Computing

平衡高性能计算中资源需求和供给的建模和系统支持

• 批准号: 0643640
• 负责人: Xiaodong Zhang
• 金额: $ 27.55万
• 依托单位: Ohio State University Research Foundation -DO NOT USE
• 依托单位国家: 美国
• 项目类别: Continuing grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-11-01 至 2008-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0643640&HistoricalAwards=false
• 关键词: Modeling; System; Support; Balance; Resource

Xiaodong, Zhang0405909High performance computing in multiprocessors and clusters has been mainly characterized for performance evaluation and guided for performance optimization by communication-oriented models, such as the LogP model, which considers the communication latency as the dominant factor contributing the performance degradation. With the rapid development and advances of commodity processors and network technologies, a modern cluster is equipped with fast interconnection networks, where each node has a high capacity with increasingly fast CPUs and larger memory. Unfortunately, the speed gaps between the CPU and the memory and the I/O storage continue to grow, seriously limiting the cluster computing efficiency. Since the dominant bottleneck concerns have been dramatically changed from communication bandwidths to memory bandwidths, there are several limits of using a LopP-like model. First, balancing and well-utilizing the CPU, memory and storage resources in clusters is a serious issue to be addressed because this is a major source limiting the sustained performance. Second, one common phenomenon due to the large CPU and memory speed gap is that CPU cycles are over-supplied while the memory and I/O bandwidths are highly demanded and not sufficient.The research in this project will address the growing concern of unbalanced resource demand and supply in cluster computing, we propose several related research projects. The first objective is to develop memory hierarchy oriented analytical performance models and experimental tools to quantitatively provide the insights into the resource demand and supply in high performance cluster computing, which guide users and computer architects to optimize their system designs and program implementations. This will be a general model covering both communication and memory effects. The second objective is to concentrate our efforts on two critical issues to improve the sustained performance: (1) locality exploitation and (2) latency reduction beyond the on-chip cache level by proposing two novel and cost-effective memory system designs and their implementations.The project will design and build a structured PSP scheme and its implementation in the cluster to decentralize the resource management. These methods will be tested on three types of large real-world and data intensive applications: the CFD computation, a direct numerical simulation of turbulence, and Internet multimedia data delivery.

Xiaodong，Zhang 0405909多处理器和集群中的高性能计算主要用于性能评估，并通过面向通信的模型（例如LogP模型）来指导性能优化，该模型将通信延迟视为导致性能下降的主要因素。 随着商用处理器和网络技术的快速发展和进步，现代集群配备了快速互连网络，其中每个节点具有越来越快的CPU和更大的内存的高容量。 不幸的是，CPU与内存和I/O存储之间的速度差距不断扩大，严重限制了集群计算效率。 由于主要的瓶颈问题已经从通信带宽到存储器带宽发生了巨大的变化，因此使用Lopp模型存在一些限制。 首先，平衡和充分利用集群中的CPU、内存和存储资源是一个需要解决的严重问题，因为这是限制持续性能的主要来源。 第二，由于CPU和内存的速度差距很大，一个常见的现象是CPU周期过剩，而内存和I/O带宽的要求很高，并没有足够的。本项目的研究将解决日益关注的资源需求和供应不平衡的集群计算，我们提出了几个相关的研究项目。 第一个目标是开发面向内存层次结构的性能分析模型和实验工具，以定量地提供对高性能集群计算中资源需求和供应的洞察，指导用户和计算机架构师优化他们的系统设计和程序实现。 这将是一个通用的模型，涵盖通信和记忆效应。 第二个目标是集中我们的努力在两个关键问题，以提高持续的性能：（1）局部性开发和（2）通过提出两种新颖的和成本效益的存储系统设计和实现的延迟减少超出片上缓存级别。该项目将设计和构建一个结构化的PSP方案和它的实现在集群分散的资源管理。 这些方法将在三种类型的大型现实世界和数据密集型应用程序上进行测试：CFD计算，湍流的直接数值模拟和互联网多媒体数据传输。