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

张晓东，多处理器和集群高性能计算的主要特征是性能评估，并以面向通信的模型为指导进行性能优化，如LogP模型，该模型认为通信延迟是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联网络，其中每个节点都具有高容量，CPU越来越快，内存也越来越大。遗憾的是，CPU与内存和I/O存储之间的速度差距继续扩大，严重限制了集群计算的效率。由于主要的瓶颈问题已从通信带宽显著改变为内存带宽，因此使用类似LopP的模型有几个限制。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。其次，由于CPU和内存速度差距较大，一个常见的现象是CPU周期供过于求，而内存和I/O带宽需求很高且不足。本项目的研究针对集群计算中日益增长的资源供需失衡问题，我们提出了几个相关的研究项目。第一个目标是开发面向存储层次的分析性能模型和实验工具，以定量地了解高性能集群计算中的资源需求和供应，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。这将是一个涵盖沟通和记忆效应的一般模型。第二个目标是将精力集中在两个关键问题上以提高持续性能：(1)局部性开发和(2)通过提出两种新颖且经济高效的存储系统设计和实现来降低超过片上高速缓存级的延迟。该项目将设计和构建结构化的PSP方案及其在集群中的实现，以分散资源管理。这些方法将在三种类型的大型真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。

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.