Collaborative Research: PPoSS: Planning: Towards an Integrated, Full-stack System for Memory-centric Computing

协作研究：PPoSS：规划：面向以内存为中心的计算的集成全栈系统

• 批准号: 2028825
• 负责人: Peng Jiang
• 金额: $ 6.45万
• 依托单位: University of Iowa
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2028825&HistoricalAwards=false
• 关键词: Collaborative; Research; PPoSS; Planning; Towards

As the volume of data being processed by today’s systems continues to increase, the traditional organization of memory systems is shifting to accommodate that accelerating growth. Data-centric applications such as irregular graph-mining algorithms, distributed machine learning, and genome sequencing require a large amount of data to compute and store, and generate massive amounts of intermediate data to move around the compute resources. Memory-centric computing is a potential solution to overcome the performance bottleneck of current systems. Near or in-memory computing can mitigate the bandwidth limitations with fewer data movements between the memory and host processing units; a remote memory pool with a fast interconnect shared by all processing units can overcome the current capacity constraints. Both solutions are promising for breaking down the memory wall. However, it is challenging to release the power of both solutions with direct integration. In this project, the investigators propose an integrated, full-stack system to enable memory-centric computing (SMC2). The system will incorporate the emerging near-memory data processors (NDP) and an extensible remote memory pool to minimize the performance impact of memory accesses in graph-mining applications. The research tasks include optimizations in architecture, the software/hardware interface, programming models/compilers, and performance models/optimization. First, the architecture is revisited to utilize the NDP hardware to build an active memory system that supports intelligent data prefetch and speculative data push. Next, the system software is redesigned to support NDP function calls, data-push operations, and virtualization. Then, with new system abstractions, a new programming model is proposed to allow programmers to specify which tasks can run on the NDP resources, and to support efficient NDP-to-NDP communication. Lastly, a new system performance model and optimization framework are incorporated. By putting the four pieces together, the proposed system support can maximize the performance of memory-centric computing with new system abstractions and theories.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.

随着当今系统处理的数据量不断增加，传统的内存系统组织正在发生转变，以适应以数据为中心的应用程序的加速增长，例如不规则图形挖掘算法、分布式机器学习和基因组测序需要大量数据。以内存为中心的计算是克服当前系统性能瓶颈的潜在解决方案，可以缓解带宽限制。内存和主机之间的数据移动更少处理单元；具有由所有处理单元共享的快速互连的远程内存池可以克服当前的容量限制。然而，通过直接集成来释放这两种解决方案的能力是具有挑战性的。在该项目中，研究人员提出了一种集成的全堆栈系统来实现以内存为中心的计算（SMC2）。该系统将整合新兴的近内存数据处理器（NDP）和可扩展的远程内存池，以最大限度地减少性能影响。图挖掘中的内存访问研究任务包括架构、软件/硬件接口、编程模型/编译器和性能模型/优化的优化，首先，重新审视架构，利用 NDP 硬件构建支持智能数据预取和存储的主动存储系统。接下来，系统软件被重新设计以支持NDP函数调用、数据推送操作和虚拟化，然后，通过新的系统抽象，提出了一种新的编程模型，以允许程序员指定哪些任务可以在NDP上运行。最后，通过将这四个部分结合在一起，所提出的系统支持可以通过新的系统抽象最大限度地提高以内存为中心的计算的性能。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Rethinking graph data placement for graph neural network training on multiple GPUs

重新思考多 GPU 上图神经网络训练的图数据放置

• DOI: 10.1145/3503221.3508435
• 发表时间: 2022-03
• 期刊: Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
• 作者: Song, Shihui;Jiang, Peng
• 通讯作者: Jiang, Peng

Rethinking graph data placement for graph neural network training on multiple GPUs

重新思考多 GPU 上图神经网络训练的图数据放置

• DOI: 10.1145/3524059.3532384
• 发表时间: 2022-03-28
• 期刊: Proceedings of the 36th ACM International Conference on Supercomputing
• 作者: Shihui Song;Peng Jiang
• 通讯作者: Peng Jiang

STMatch: Accelerating Graph Pattern Matching on GPU with Stack-Based Loop Optimizations

STMatch：通过基于堆栈的循环优化加速 GPU 上的图形模式匹配

• DOI: 10.1109/sc41404.2022.00058
• 发表时间: 2022-11-01
• 期刊: SC22: International Conference for High Performance Computing, Networking, Storage and Analysis
• 作者: Yi;Peng Jiang
• 通讯作者: Peng Jiang