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解决方案的时间。所采用的方法包括仿真与硬件辅助相结合。如果要实现新兴高端系统的优势，分布式仿真的内存性能必须得到提高。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）的系统的复杂性已经大大增加。内存速度、节点复杂性和系统规模的结合往往会导致性能不佳。多年来致力于提高并行和分布式系统中应用程序性能的深入研究已经使平均效率提高了5-10%。复杂性的持续指数增长使得通过调优来维持这些效率变得非常困难。大幅提高效率需要创新。该项目将通过创建一套新的软件工具来改善基于smp的集群中的分布式模拟性能，这些软件工具可以跨系统组件（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固有的可扩展性，同时提供了系统范围内的内存性能信息，而这些信息在没有复杂的侵入式软件分析或模拟的帮助下是不可用的。该项目将利用硬件分析、并行和分布式性能评估、统计数据减少分析、分析建模技术和工具开发，以及在商品cpu和nic上新兴的硬件计数器技术，为位置感知应用程序分析、分析和优化生产一个软件框架，并创建一个框架，提供大规模高端分布式系统中本地和远程内存访问的完整图片。

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 .