SPX: Collaborative Research: Global Address Programming with Accelerators

SPX：协作研究：使用加速器进行全局地址编程

基本信息

批准号：
1823037
负责人：
John Owens
金额：
$ 38.6万
依托单位：
University of California-Davis
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-10-01 至 2022-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1823037&HistoricalAwards=false
关键词：
SPX Collaborative Research Global Address

项目摘要

Large-scale computing today is dominated by parallel computing, where a large task is divided into many smaller tasks and those smaller tasks run at the same time. Traditionally each of those tasks run independently up to a common stopping point, then they halt, exchange information, and continue. This global stop-and-communicate step is quite expensive. This project instead pursues a different approach, where individual tasks directly communicate with other tasks asynchronously, without having to wait for a global stopping point. This approach is likely to yield better performance on large-scale computing tasks, specifically on what is becoming the dominant large-scale machine, a heterogeneous machine with many CPUs and othermany-core processors. The project will deliver a set of high-performance, open-source data structures and algorithm implementations to support irregular patterns of communication, notably those that arise in biology, graph analytics, and sparse linear algebra for machine learning. These will not only be directly useful for end users but also demonstrate how to design and engineer primitives for accelerator-equipped distributed-memory machines. The project also engages application developers (both in our groups and externally) to make the outcomes broadly useful.The project will develop a programming environment for accelerator-based HPC systems that integrates accelerators into a Partitioned Global Address Space (PGAS) model, which will allow direct communication between GPUs in a manner that is well suited to both applications and the underlying hardware. Specifically, GPU programming will be integrated with the UPC++ PGAS programming model ("GPUPC++"). The project will thus advance the state of the art in algorithms, programming models, and low-level support for the heterogeneous large-scale computers.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

当今的大规模计算以并行计算为主，其中大型任务分为许多较小的任务，而这些较小的任务同时运行。传统上，这些任务中的每个任务都独立运行到一个共同的停止点，然后停止，交换信息并继续。这个全球的停车场步骤非常昂贵。相反，该项目采用了另一种方法，在这种方法中，单个任务直接与其他任务不同步，而不必等待全球停止点。这种方法可能会在大规模计算任务上产生更好的性能，特别是在成为主要的大规模机器，这是一种具有许多CPU和其他核心处理器的异质机。该项目将提供一组高性能，开源数据结构和算法实现，以支持不规则的通信模式，尤其是在生物学，图形分析和用于机器学习的稀疏线性代数中出现的沟通模式。这些不仅对最终用户直接有用，而且还展示了如何为配备加速器的分布式计算机设计和工程师的原始功能。该项目还与应用程序开发人员（无论是在我们的小组和外部）互动，以使结果广泛有用。该项目将开发一个基于加速器的HPC系统的编程环境，该计划将加速器集成到分区的全球地址空间（PGAS）模型中，该模型将允许以适合应用程序的GPU在GPU之间进行直接通信，以适合于应用程序和下属的硬件。具体而言，GPU编程将与UPC ++ PGAS编程模型（“ GPUPC ++”）集成。因此，该项目将在算法，编程模型和对异质大规模计算机的低级支持方面提高艺术状况。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评估标准来评估的。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

A Roadmap for the GraphBLAS C++ API

GraphBLAS C API 路线图

DOI：
10.1109/ipdpsw50202.2020.00049
发表时间：
2020
期刊：
and Learning (IPDPSW
影响因子：
0
作者：
Brock, Benjamin;Buluc, Aydin;Mattson, Timothy G.;McMillan, Scott;Moreira, Jose E.
通讯作者：
Moreira, Jose E.

RDMA vs. RPC for Implementing Distributed Data Structures

DOI：
10.1109/ia349570.2019.00009
发表时间：
2019-10
期刊：
2019 IEEE/ACM 9th Workshop on Irregular Applications: Architectures and Algorithms (IA3)
影响因子：
0
作者：
Benjamin Brock;Yuxin Chen;Jiakun Yan;John Douglas Owens;A. Buluç;K. Yelick
通讯作者：
Benjamin Brock;Yuxin Chen;Jiakun Yan;John Douglas Owens;A. Buluç;K. Yelick

Considerations for a Distributed GraphBLAS API

分布式 GraphBLAS API 的注意事项

DOI：
10.1109/ipdpsw50202.2020.00048
发表时间：
2020
期刊：
and Learning
影响因子：
0
作者：
Brock, Benjamin;Buluc, Aydin;Mattson, Timothy G.;McMillan, Scott;Moreira, Jose E.;Pearce, Roger;Selvitopi, Oguz;Steil, Trevor
通讯作者：
Steil, Trevor

Atos: A Task-Parallel GPU Scheduler for Graph Analytics

Atos：用于图形分析的任务并行 GPU 调度程序

DOI：
10.1145/3545008.3545056
发表时间：
2022
期刊：
Proceedings of the 51st International Conference on Parallel Processing
影响因子：
0
作者：
Chen, Yuxin;Brock, Benjamin;Porumbescu, Serban;Buluc, Aydin;Yelick, Katherine;Owens, John
通讯作者：
Owens, John

Scalable Irregular Parallelism with GPUs: Getting CPUs Out of the Way

使用 GPU 实现可扩展的不规则并行：让 CPU 不再碍事

DOI：
发表时间：
2022
期刊：
International Conference for High Performance Computing Networking Storage and Analysis
影响因子：
0
作者：
Chen, Yuxin;Brock, Benjamin;Porumbescu, Șerban;Buluc, Aydın;Yelick, Katherine;Owens, John D.
通讯作者：
Owens, John D.