CAREER: Programming the Existing and Emerging Memory Systems for Extreme-scale Parallel Performance

职业：对现有和新兴内存系统进行编程以实现超大规模并行性能

• 批准号: 1652732
• 负责人: Yonghong Yan
• 金额: $ 60万
• 依托单位: Oakland University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-02-01 至 2018-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1652732&HistoricalAwards=false
• 关键词: CAREER; Programming; Existing; Emerging; Memory

High performance computing (HPC) focuses on using numerical model to simulate complex science and engineering phenomena, such as galaxies, weather and climate, molecular interactions, electric power grids, and aircraft in flight. Over the next decade the goal is to build HPC parallel system capable of extreme-scale performance (one exaflop (1018)operations per second) and processing exabyte (1018) of data. However, one of the biggest challenges of achieving extreme-scale performance is what is known as the hardware memory wall, which is about the growing gap between the speed of computation performed by CPU and the speed of supplying data to the CPU from memory systems (about x100 time slower). The low performance efficiency of modern HPC system (average 60% and could be as low as 5%) manifests the memory wall impact since a huge amount of computation cycles are wasted for waiting for the arrival of input data. It becomes very critical to create effective software solutions for achieving the computation potential of hardware and for improving the efficiency and usability of the existing and future computing system. Such solutions will significantly benefit a broad range of disciplines that use parallel computers to solve scientific and engineering problems, and accelerate scientific discovery and problem solving to improve quality of life of the society. This CAREER project develops innovative software techniques to address the programming and performance challenges of the existing and emerging memory systems: 1) a portable abstract machine model for programming, compiling and executing parallel applications, 2) new programming interface and model for data mapping, movement, and consistency, and 3) machine-aware compilation and data-aware scheduling techniques to realize an asynchronous task flow execution model to hide the latency of data movement. It addresses the memory wall challenge by developing a memory-centric programming paradigm for helping achieve extreme-scale performance of parallel applications with minimum impairment to programmability. For education, the project involves a broader community starting from high school in the area of HPC and computer science.

高性能计算（HPC）专注于使用数值模型模拟复杂的科学和工程现象，例如星系，天气和气候，分子相互作用，电力电网和飞行中的飞机。 在接下来的十年中，目标是构建能够具有极端性能的HPC并行系统（每秒一次Exaflop（1018）操作）和处理Exabyte（1018）数据。但是，实现极端性能的最大挑战之一是所谓的硬件记忆墙，这是关于CPU执行的计算速度与从内存系统中向CPU提供数据的速度之间的差距的日益增长（大约x100时间慢）。现代HPC系统的低性能效率（平均60％，可能低至5％）表现出记忆墙的影响，因为浪费了大量的计算周期来等待输入数据的到来。 创建有效的软件解决方案来实现硬件的计算潜力并提高现有计算系统的效率和可用性，这变得非常重要。这种解决方案将大大受益于广泛的学科，这些学科使用平行的计算机解决科学和工程问题，并加速科学发现和解决问题，以改善社会的生活质量。该职业项目开发了创新的软件技术，以应对现有和新兴内存系统的编程和性能挑战：1）用于编程，编译和执行并行应用程序的便携式抽象机器模型，2）2）用于数据映射，移动的新编程接口和模型，以及一致性以及3）机器感知的编译和数据感知调度技术，以实现异步任务流执行模型，以隐藏数据移动的延迟。它通过开发以内存为中心的编程范式来帮助实现并行应用的极端性能，以最小的可编程性损害来解决内存墙的挑战。对于教育，该项目涉及一个更广泛的社区，从HPC和计算机科学领域的高中开始。

项目成果

Evaluation of Knight Landing High Bandwidth Memory for HPC Workloads

• DOI: 10.1145/3149704.3149766
• 发表时间: 2017-11
• 期刊: Proceedings of the Seventh Workshop on Irregular Applications: Architectures and Algorithms
• 作者: S. Salehian;Yonghong Yan

Comparison of Threading Programming Models

• DOI: 10.1109/ipdpsw.2017.141
• 发表时间: 2017-05
• 期刊: 2017 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)
• 作者: S. Salehian;Jiawen Liu;Yonghong Yan

Principles of Memory-Centric Programming for High Performance Computing

高性能计算的以内存为中心的编程原理

• DOI: 10.1145/3145617.3158212
• 发表时间: 2017
• 期刊: MCHPC'17 Proceedings of the Workshop on Memory Centric Programming for HPC
• 作者: Yan, Yonghong;Brightwell, Ron;Sun, Xian-He
• 通讯作者: Sun, Xian-He