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持续增加，存储器系统的传统组织正在转变以适应这种加速增长。以数据为中心的应用程序，如不规则图挖掘算法、分布式机器学习和基因组测序，需要计算和存储大量数据，并生成大量中间数据以在计算资源中移动。以内存为中心的计算是克服当前系统性能瓶颈的一个潜在解决方案。近内存或内存计算可以减少内存和主机处理单元之间的数据移动，从而缓解带宽限制;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。这两种解决方案都有希望打破内存墙。然而，通过直接集成来释放这两种解决方案的功能是具有挑战性的。在这个项目中，研究人员提出了一个集成的全栈系统，以实现以内存为中心的计算（SMC 2）。该系统将结合新兴的近内存数据处理器（NDP）和可扩展的远程内存池，以最大限度地减少图形挖掘应用程序中内存访问对性能的影响。研究任务包括体系结构优化、软件/硬件接口优化、编程模型/编译器优化和性能模型/优化。首先，该架构被重新审视以利用NDP硬件来构建支持智能数据预取和推测性数据推送的主动存储器系统。接下来，重新设计系统软件以支持NDP函数调用、数据推送操作和虚拟化。然后，新的系统抽象，提出了一个新的编程模型，允许程序员指定哪些任务可以运行在NDP资源，并支持有效的NDP到NDP通信。最后，一个新的系统性能模型和优化框架。通过将这四个部分结合在一起，拟议的系统支持可以最大限度地提高以内存为中心的计算的性能，并采用新的系统抽象和理论。该奖项反映了NSF的法定使命，并通过使用基金会的知识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

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

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

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

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