Collaborative Research: SHF: Medium: A hardware-software co-design approach for high-performance in-memory analytic data processing

协作研究：SHF：中：用于高性能内存分析数据处理的硬件软件协同设计方法

• 批准号: 2407690
• 负责人: Jignesh Patel
• 金额: $ 40万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2407690&HistoricalAwards=false
• 关键词: Collaborative; Research; SHF; Medium; hardware

Database analytics is crucial for decision-making across various industries and fields of inquiry. However, there is a challenge: analyzing large amounts of data using traditional methods takes more time and money as the volume of data grows. Resolving this issue is vital for enterprises to stay competitive through fast and accurate data-driven decision-making and to keep up with rapid growth in data volumes. In the past, hardware and software for analytics could be developed separately, benefiting from Moore's Law (doubling of transistor density with each transistor generation) and Dennard scaling (which allowed Moore's Law to proceed without increasing power density). This scaling allowed the industry to steadily improve the performance of general-purpose hardware. However, we have now reached the physical limits of these trends and need new hardware approaches to enhance analytics speed and efficiency. Furthermore, processing units are now much faster than memory, so applications with large volumes of data are increasingly bottlenecked by memory accesses. This research, therefore, focuses on memory devices, in particular designing ``intelligent'' memory capable of computing results near the stored data, and proposes a solution by redesigning both hardware and software components of an analytics pipeline to work synergistically, addressing the data analytics performance issue from the ground up. This innovative approach has the potential to significantly improve the efficiency of data analytics. The project is a collaboration between one database and software researcher at the University of Wisconsin-Madison (UW) and two computer architecture and systems researchers at Cornell University and the University of Virginia (UVA). The project is organized into four thrusts. Thrust 1 aims to develop mechanisms for in-place data analytics query processing on the dynamic random access memory (DRAM) side and explore the synergies between intelligent DRAM and other processing units. Thrust 2 focuses on processing in memory (PIM) designs for static random-access memory (SRAM)-based caches, exploring associative processing (AP) and its applicability to data analytics workloads. Thrust 3 takes a holistic approach to accelerate analytics queries across both SRAM and DRAM-based PIM designs. It proposes a Domain-Specific Language (DSL)-based approach using an operational algebra, decomposing queries into a dataflow graph and optimizing their execution across different PIMs and the Central Processing Unit (CPU). Finally, Thrust 4 addresses the need for evaluation frameworks in the database-hardware co-design approach by developing a simulation infrastructure and benchmarks that can be used by the broader architecture and database research communities. Besides training students involved in this project across the hardware-software boundaries, the project will also support outreach efforts in entrepreneurship education at UW. Additionally, there are outreach plans to Native American high school students through an effort at Cornell, and at UVA, the project will contribute to ongoing efforts to build long-term collaborations with Historically Black Colleges and Universities (HBCUs) and Minority-Serving Institutions (MSIs) in the Virginia area.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.

数据库分析对于各个行业和询问领域的决策至关重要。但是，存在一个挑战：随着数据量的增加，使用传统方法分析大量数据需要更多的时间和金钱。解决此问题对于企业，通过快速，准确的数据驱动的决策来保持竞争力至关重要，并跟上数据量的快速增长。过去，可以单独开发用于分析的硬件和软件，从摩尔的定律中受益（每次晶体管生成的晶体管密度增加一倍）和丹纳德缩放（这允许摩尔的定律在不增加功率密度的情况下进行）。这种扩展使该行业能够稳步提高通用硬件的性能。但是，我们现在已经达到了这些趋势的物理限制，需要新的硬件方法来提高分析速度和效率。此外，现在处理单元比内存要快得多，因此内存访问越来越多地瓶颈具有大量数据的应用程序。因此，这项研究的重点是内存设备，特别是设计能够在存储数据附近计算结果的``智能''内存，并通过重新设计分析管道的硬件和软件组件来协同工作，从而从头开始解决数据分析性能问题，从而提出了解决方案。这种创新的方法有可能显着提高数据分析的效率。该项目是威斯康星大学麦迪逊分校（UW）的一个数据库与软件研究人员之间的合作，以及康奈尔大学和弗吉尼亚大学（UVA）的两名计算机建筑和系统研究人员。该项目分为四个推力。推力1旨在开发在动态随机访问存储器（DRAM）侧的现场数据分析处理的机制，并探索智能DRAM和其他处理单元之间的协同作用。推力2着重于存储器中的处理（PIM）设计，用于基于静态的随机访问存储器（SRAM）基于基于静态的caches，探索关联处理（AP）及其对数据分析工作负载的适用性。 Thrust 3采用了一种整体方法来加速基于SRAM和DRAM PIM设计的分析查询。它提出了使用操作代数的基于域特异性语言（DSL）的方法，将查询分解为数据流图，并在不同的PIMS和中央处理单元（CPU）中优化其执行。最后，推力4通过开发模拟基础架构和基准，可以在数据库硬件共同设计方法中进行评估框架的需求，并可以由更广泛的体系结构和数据库研究社区使用。除了培训跨硬件软件边界参与该项目的学生外，该项目还将支持UW企业家教育的外展工作。此外，通过在康奈尔（Cornell）的努力向美国原住民高中生制定了宣传计划，在UVA，该项目将有助于与历史上的黑人学院和大学（HBCUS）（HBCUS）（HBCUS）和少数派服务机构（MSIS）（MSIS（MSIS）建立长期合作，弗吉尼亚州的弗吉尼亚地区。 标准。