Collaborative Research: CSR(SMA): Scalable performance modeling and analysis framework

该项目将探索新的软件技术，通过提高新兴分布式系统的内存效率来缩短应用程序的解决时间。将采取的方法包括模拟和硬件辅助相结合。如果要实现新兴高端系统的好处，分布式仿真的存储性能必须得到改善。为了隐藏内存延迟的影响，在大规模集群中用作构建块(例如SMP)的系统的复杂性大幅增加。内存速度、节点复杂性和系统规模的组合往往会导致性能较差。多年来，旨在提高并行和分布式系统中应用程序性能的密集研究已经导致了5%-10%的平均效率。复杂性的持续指数增长使得通过调优保持这些效率具有挑战性。大幅提高效率将需要创新。该项目将通过创建一套新的软件工具来提高基于SMP的集群中的分布式模拟性能，这些工具能够跨系统组件(例如CPU和NIC)访问协调的硬件计数器信息。初步结果表明，这些多组件技术具有内在的可扩展性，同时提供以前在没有复杂、侵入式软件分析或模拟的情况下无法获得的系统范围的内存性能信息。该项目将利用硬件分析、并行和分布式性能评估、统计数据简化分析、分析建模技术和工具开发，并结合商用CPU和NIC上的新兴硬件计数器技术来产生一个用于位置感知应用程序分析、分析和优化的软件框架，并创建一个框架来提供大规模高端分布式系统中本地和远程内存访问的全貌。

The project will explore novel software technologies to improve the time to solution of applications by increasing memory efficiency on emerging distributed systems. The approaches to be pursued include combination of simulation and hardware assists. The memory performance of distributed simulations must improve if the benefits of emergent high-end systems are to be realized. To hide the effects of memory latency, the complexity of systems used as building blocks (e.g. SMPs) in large-scale clusters has increased substantially. The combination of memory speed, node complexity and system scale often leads to poor performance. Years of intense research aimed at improving the performance of applications in parallel and distributed systems have led to average efficiencies of 5-10%. The continued exponential increase in complexity makes maintaining these efficiencies through tuning challenging. Improving efficiency dramatically will require innovation. The project will improve distributed simulation performance in SMP-based clusters through creation of a new set of software tools that enable access to coordinated hardware counter information across system components (e.g. CPU and NIC). Preliminary results indicate these multi-component techniques are inherently scalable while providing system-wide memory performance information previously unavailable without the aid of sophisticated, intrusive software profiling or simulation.The project will leverage hardware profiling, parallel and distributed performance evaluation, statistical data reduction analysis, analytical modeling techniques and tool development with emerging hardware counter technologies on commodity CPUs and NICs to produce a software framework for locality-aware application profiling, analysis and optimization, and create a framework that provides a complete picture of local and remote memory accesses in a largescale, high-end distributed systems .