Collaborative Research: PPoSS: LARGE: A Full-Stack Architecture for Sparse Computation
协作研究:PPoSS:LARGE:稀疏计算的全栈架构
基本信息
- 批准号:2216978
- 负责人:
- 金额:$ 55万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Continuing Grant
- 财政年份:2022
- 资助国家:美国
- 起止时间:2022-10-01 至 2027-09-30
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
Computer systems have been designed and optimized primarily for dense computations, i.e., those that process regularly structured data. But current systems are ill-suited to sparse computations, i.e., those that process unstructured data. Sparse computations are very common because many relations and interactions are sparse. For example, most people are not friends and most neurons are not directly connected. Sparse computations take advantage of this sparsity by encoding and processing only meaningful relations, such as storing only the non-zero elements of a matrix. These applications are crucial in many domains, like deep learning, data analytics, and scientific computing, but their irregular structure makes them inefficient and hard to scale in currentsystems, wasting billions of dollars yearly. This project aims to redesign the computing stack to provide first-class support for sparse computations. The project's novelties include a full system stack that spans programming languages, compilers, and specialized hardware architectures and large-scale computer systems. The project's impacts include making future parallel systems much more versatile, scalable, energy efficient and easier to program.This project takes a coordinated approach across the system stack to unlock the performance and scalability of sparse computations, because they pose challenges that cannot be addressed at a single layer. For example, sparse computations have a rich space of choices in algorithm, data representation, and schedule, which current languages and compilers cannot capture or optimize properly. The right choice of algorithm and data representation are often unknown in advance and may change at run-time, thwarting the rigid division between current compilers and schedulers. Irregular, data-dependent control and memory accesses stymie compiler analysis, hinder parallelization, make poor use of hardware, and introduce numerous side channels that thwart security. Finally, their data-intensive nature is a poor match to the processors and accelerators pervasive in current clusters and datacenters, which optimize for compute operations rather than to minimize data movement. To tackle these challenges, this project will develop a full system stack spanning domain-specific languages, a tightly integrated compiler and scheduler, and specialized hardware architectures and high-performance, multi-node computer systems and networks. This stack is built around a unifying abstraction, anovel sparse intermediate representation that (1) encodes semantic information on key sparse data structures and their iterations, (2) enables optimizing compiler transformations and dynamic scheduling decisions, and (3) can be easily compiled to parallel architectures, including graphics processing units (GPUs), general-purpose processors, our proposed specialized architecture, and their combination. The full stack will be designed with security at the forefront, leveraging novel cross-layer techniques to achieve secure high performance. This system will be rigorously evaluated using a broad set of sparse applications and at a wide range of system scales, including large-scale clusters with hundreds of GPUs or tens of specialized processors. By innovating across the full software and hardware stack, these techniques will achieve performance, scalability, and efficiency gains that single-layer approaches cannot provide.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.
计算机系统主要是针对密集计算而设计和优化的,即,那些处理常规结构化数据的人。但是当前的系统不适合稀疏计算,即,那些处理非结构化数据的人。稀疏计算非常常见,因为许多关系和交互都是稀疏的。例如,大多数人不是朋友,大多数神经元没有直接连接。稀疏计算通过仅编码和处理有意义的关系来利用这种稀疏性,例如仅存储矩阵的非零元素。这些应用程序在许多领域都至关重要,如深度学习,数据分析和科学计算,但它们的不规则结构使它们效率低下,难以在当前系统中扩展,每年浪费数十亿美元。该项目旨在重新设计计算堆栈,为稀疏计算提供一流的支持。该项目的新颖之处包括一个完整的系统堆栈,涵盖编程语言、编译器、专用硬件架构和大型计算机系统。该项目的影响包括使未来的并行系统更加通用、可扩展、节能和更容易编程。该项目在整个系统堆栈中采用协调方法,以释放稀疏计算的性能和可扩展性,因为它们带来了无法在单个层解决的挑战。例如,稀疏计算在算法、数据表示和调度方面具有丰富的选择空间,而当前的语言和编译器无法正确地捕获或优化这些选择空间。算法和数据表示的正确选择通常是事先未知的,并且可能在运行时发生变化,这阻碍了当前编译器和编译器之间的严格划分。不规则的、依赖于数据的控制和内存访问阻碍了编译器的分析,阻碍了并行化,使硬件的利用率很低,并引入了许多阻碍安全性的侧通道。最后,它们的数据密集型本质与当前集群和数据中心中普遍存在的处理器和加速器不匹配,这些处理器和加速器针对计算操作进行了优化,而不是最大限度地减少数据移动。为了应对这些挑战,该项目将开发一个完整的系统堆栈,涵盖特定领域的语言,紧密集成的编译器和调度器,以及专门的硬件架构和高性能,多节点计算机系统和网络。这个堆栈是围绕一个统一的抽象,一个新的稀疏中间表示,(1)编码关键稀疏数据结构及其迭代的语义信息,(2)能够优化编译器转换和动态调度决策,(3)可以很容易地编译到并行架构,包括图形处理单元(GPU),通用处理器,我们提出的专用架构,和它们组合。整个堆栈的设计将安全性放在首位,利用新颖的跨层技术来实现安全的高性能。该系统将使用广泛的稀疏应用程序和广泛的系统规模进行严格评估,包括具有数百个GPU或数十个专用处理器的大规模集群。通过在整个软件和硬件堆栈中进行创新,这些技术将实现单层方法无法提供的性能、可扩展性和效率提升。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
期刊论文数量(2)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
RT-kNNS Unbound: Using RT Cores to Accelerate Unrestricted Neighbor Search
RT-kNNS Unbound:使用 RT 内核加速无限制邻居搜索
- DOI:10.1145/3577193.3593738
- 发表时间:2023
- 期刊:
- 影响因子:0
- 作者:Nagarajan, Vani;Mandarapu, Durga;Kulkarni, Milind
- 通讯作者:Kulkarni, Milind
RT-DBSCAN: Accelerating DBSCAN using Ray Tracing Hardware
RT-DBSCAN:使用光线追踪硬件加速 DBSCAN
- DOI:10.1109/ipdps54959.2023.00100
- 发表时间:2023
- 期刊:
- 影响因子:0
- 作者:Nagarajan, Vani;Kulkarni, Milind
- 通讯作者:Kulkarni, Milind
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Milind Kulkarni其他文献
Can paediatric surgical registrars safely perform supervised hypospadias surgery?
儿科手术注册员可以在监督下安全地进行尿道下裂手术吗?
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:2
- 作者:
Charlotte Hughes;Hazem Mosa;Sandra Johnson;J. Parr;Ravindar Anbarasan;Milind Kulkarni;A. Mathur - 通讯作者:
A. Mathur
InContext: simple parallelism for distributed applications
InContext:分布式应用程序的简单并行性
- DOI:
10.1145/1996130.1996144 - 发表时间:
2011 - 期刊:
- 影响因子:0
- 作者:
Sunghwan Yoo;Hyojeong Lee;C. Killian;Milind Kulkarni - 通讯作者:
Milind Kulkarni
Garbage Collection for Mostly Serialized Heaps
大多数序列化堆的垃圾收集
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Chaitanya Koparkar;Vidush Singhal;Aditya Gupta;Mike Rainey;Michael Vollmer;Artem Pelenitsyn;Sam Tobin;Milind Kulkarni;Ryan R. Newton - 通讯作者:
Ryan R. Newton
Scheduling Transformation and Dependence Tests for Recursive Programs
递归程序的调度转换和依赖性测试
- DOI:
- 发表时间:
2018 - 期刊:
- 影响因子:0
- 作者:
Kirshanthan Sundararajah;Milind Kulkarni - 通讯作者:
Milind Kulkarni
The Centre for Market and Public Organisation One Kind of Democracy One Kind of Democracy
市场与公共组织中心 一种民主 一种民主
- DOI:
- 发表时间:
- 期刊:
- 影响因子:0
- 作者:
Siwan Anderson;P. Francois;Ashok Kotwal;Milind Kulkarni;Tim Murugkar;Gustavo Besley;Biju Bobonis;Jim Rao;Jim Fearon;Francesco Robinson;John Trebbi;Debraj Hoddinott;Nava Ray;Robin Ashraf;Garance Burgess;Dilip Genicot;Thomas Mookherjee;Fujiwara - 通讯作者:
Fujiwara
Milind Kulkarni的其他文献
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{{ truncateString('Milind Kulkarni', 18)}}的其他基金
Travel: Student Travel Grant for the Programming Languages Mentoring Workshop at PLDI 2022
旅费:PLDI 2022 编程语言指导研讨会的学生旅费补助
- 批准号:
2227746 - 财政年份:2022
- 资助金额:
$ 55万 - 项目类别:
Standard Grant
SHF: Small: A Composable, Sound Optimization Framework for Loops and Recursion
SHF:小型:用于循环和递归的可组合、完善的优化框架
- 批准号:
1908504 - 财政年份:2019
- 资助金额:
$ 55万 - 项目类别:
Standard Grant
SPX: Write Once, Run on Anything: Verified, Tuned Accelerator Kernels from High Level Specifications
SPX:一次写入,在任何设备上运行:根据高级规范进行验证、调整的加速器内核
- 批准号:
1919197 - 财政年份:2019
- 资助金额:
$ 55万 - 项目类别:
Standard Grant
NSF Student Travel Grant for 2019 Midwest Programming Languages Summit (MWPLS)
2019 年中西部编程语言峰会 (MWPLS) 的 NSF 学生旅费补助金
- 批准号:
1942074 - 财政年份:2019
- 资助金额:
$ 55万 - 项目类别:
Standard Grant
SPX: Collaborative Research: Eat your Wheaties: Multi-Grain Compilers for Parallel Builds at Every Scale
SPX:协作研究:吃你的小麦:用于各种规模并行构建的多粒度编译器
- 批准号:
1725672 - 财政年份:2017
- 资助金额:
$ 55万 - 项目类别:
Standard Grant
SI2-SSI: Collaborative Research: ParaTreet: Parallel Software for Spatial Trees in Simulation and Analysis
SI2-SSI:协作研究:ParaTreet:仿真和分析中的空间树并行软件
- 批准号:
1550525 - 财政年份:2016
- 资助金额:
$ 55万 - 项目类别:
Standard Grant
SHF: Small: Collaborative Research: Hybrid Static-Dynamic Analyses for RegionSerializability
SHF:小型:协作研究:区域可串行性的混合静态动态分析
- 批准号:
1422178 - 财政年份:2014
- 资助金额:
$ 55万 - 项目类别:
Standard Grant
XPS: FULL: FP: Collaborative Research: Taming parallelism: optimally exploiting high-throughput parallel architectures
XPS:完整:FP:协作研究:驯服并行性:最佳地利用高吞吐量并行架构
- 批准号:
1439126 - 财政年份:2014
- 资助金额:
$ 55万 - 项目类别:
Standard Grant
XPS: CLCCA: On the Hunt for Correctness and Performance Bugs in Large-scale Programs
XPS:CLCCA:寻找大型程序中的正确性和性能错误
- 批准号:
1337158 - 财政年份:2013
- 资助金额:
$ 55万 - 项目类别:
Standard Grant
CAREER:Toward a locality-enhancing transformation framework for irregular programs
职业生涯:为非正规项目建立一个增强地方性的转型框架
- 批准号:
1150013 - 财政年份:2012
- 资助金额:
$ 55万 - 项目类别:
Continuing Grant
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Research on Quantum Field Theory without a Lagrangian Description
- 批准号:24ZR1403900
- 批准年份:2024
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- 项目类别:省市级项目
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- 批准号:31224802
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- 批准号:10774081
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- 项目类别:面上项目
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