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: 2015254
• 负责人: Yonghong Yan
• 金额: $ 49.87万
• 依托单位: University of North Carolina at Charlotte
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2015254&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来模拟复杂的科学和工程现象，如星系，天气和气候，分子相互作用，电网和飞行中的飞机。 在未来十年中，目标是构建能够实现极高性能（每秒1018次操作）和处理1018 EB数据的HPC并行系统。然而，实现极端规模性能的最大挑战之一是所谓的硬件内存墙，这是关于CPU执行的计算速度和从内存系统向CPU提供数据的速度之间的差距越来越大（大约慢100倍）。现代HPC系统的低性能效率（平均60%，可能低至5%）表明了内存墙的影响，因为大量的计算周期被浪费在等待输入数据的到来。 为了实现硬件的计算潜力，提高现有和未来计算系统的效率和可用性，创建有效的软件解决方案变得非常关键。这些解决方案将大大有利于使用并行计算机解决科学和工程问题的广泛学科，并加速科学发现和问题解决，以提高社会生活质量。这个CAREER项目开发创新的软件技术，以解决现有和新兴的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的可移植抽象机器模型，2）用于数据映射、移动和一致性的新编程接口和模型，以及3）机器感知编译和数据感知调度技术，以实现异步任务流执行模型，从而隐藏数据移动的延迟。它解决了内存墙的挑战，通过开发一个以内存为中心的编程范式，以帮助实现并行应用程序的极大规模的性能与最小的损害可编程性。在教育方面，该项目涉及从高中开始的HPC和计算机科学领域的更广泛的社区。

项目成果

CUDAMicroBench: Microbenchmarks to Assist CUDA Performance Programming

CUDAMicroBench：辅助 CUDA 性能编程的微基准

• DOI: 10.1109/ipdpsw52791.2021.00068
• 发表时间: 2021
• 期刊: 2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW
• 作者: Yi, Xinyao;Stokes, David;Yan, Yonghong;Liao, Chunhua
• 通讯作者: Liao, Chunhua

Generating and Analyzing Program Call Graphs using Ontology

使用本体生成和分析程序调用图

• DOI: 10.1109/protools56701.2022.00008
• 发表时间: 2022
• 期刊: 2022 IEEE/ACM Workshop on Programming and Performance Visualization Tools (ProTools
• 作者: Dorta, Ethan;Yan, Yonghong;Liao, Chunhua
• 通讯作者: Liao, Chunhua

FreeCompilerCamp.org: Training for OpenMP Compiler Development from Cloud

FreeCompilerCamp.org：从云端进行 OpenMP 编译器开发培训

• 发表时间: 2020
• 期刊: Journal of computational science education
• 作者: Wang, Anjia;Mishra, Alok;Liao, Chunhua;Yan, Yonghong;Chapman, Barbara
• 通讯作者: Chapman, Barbara

UPIR: Toward the Design of Unified Parallel Intermediate Representation for Parallel Programming Models

UPIR：面向并行编程模型的统一并行中间表示的设计

• DOI: 10.1145/3559009.3569646
• 发表时间: 2022
• 期刊: PACT '22: Proceedings of the International Conference on Parallel Architectures and Compilation Techniques
• 作者: Wang, Anjia;Yi, Xinyao;Yan, Yonghong
• 通讯作者: Yan, Yonghong

RDS: a cloud-based metaservice for detecting data races in parallel programs

RDS：一种基于云的元服务，用于检测并行程序中的数据竞争

• DOI: 10.1145/3468737.3494089
• 发表时间: 2021
• 期刊: UCC '21: Proceedings of the 14th IEEE/ACM International Conference on Utility and Cloud Computing
• 作者: Shi, Yaying;Wang, Anjia;Yan, Yonghong;Liao, Chunhua
• 通讯作者: Liao, Chunhua