PFI-TT: A tool to automatically generate and optimize programs to operate on complex big data

PFI-TT：自动生成和优化程序以处理复杂大数据的工具

• 批准号: 2044424
• 负责人: Saman Amarasinghe
• 金额: $ 25万
• 依托单位: Massachusetts Institute of Technology
• 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: 2021-07-15 至 2023-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2044424&HistoricalAwards=false
• 关键词: PFI; TT; tool; automatically; generate

The broader impact/commercial potential of this Partnerships for Innovation - Technology Translation (PFI-TT) project in enabling the future commercialization of a Tensor Algebra Compiler (TACO) that utilizes sparse data processing to expose hidden data properties that can lead to improvements in many fields. As the world becomes more digitized and heavily instrumented, sparse data sets are captured and stored in centralized repositories on the cloud. These enormous data sets are in every conceivable area, such as daily traffic information, social interactions, purchasing patterns, population genomics, and environmental monitoring. However, analyzing and using this data requires the use of many complex tensor and matrix algebra algorithms on sparse data sets in a high-performance and scalable manner. Thus far, the state-of-the-art systems are too complicated or have limited capabilities to let practitioners harness the full potential of these data sets. This PFI translational research and technology development project will use compiler technology to make sparse data processing simple and accessible to students, scientists, data analysts, and engineers.TACO is the first sparse tensor algebra compiler that can take any complex tensor algebra expression (where each tensor can be from any existing sparse and dense tensor format) and generate high-performance Central Processing Unit (CPU) and Graphics Processing Unit (GPU) code comparable to the current state-of-the-art, hand-optimized libraries. With TACO, sparse tensor algebra can be put on the same compiler transformation and code generation footing as the dense tensor algebra and array codes. The proposed project will commercialize TACO enabling easier analysis of complex data. The power and simplicity of TACO may lead to more analysis and better algorithms, further expanding the economic and societal impacts.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.

这种合作伙伴关系对创新 - 技术翻译（PFI -TT）项目的更广泛的影响/商业潜力在实现未来的张量代数编译器（TACO）的商业化中利用稀疏数据处理来公开隐藏的数据属性，这些数据属性可以在许多领域中导致改进。随着世界变得更加数字化和仪器，稀疏数据集被捕获并存储在云上的集中存储库中。这些庞大的数据集都在每个可能的领域，例如日常交通信息，社交互动，购买模式，人口基因组学和环境监测。但是，分析和使用这些数据需要以高性能和可扩展方式使用许多复杂的张量和矩阵代数算法。到目前为止，最新的系统太复杂了，或者具有有限的功能，无法使从业者充分利用这些数据集的潜力。该PFI翻译研究和技术开发项目将使用编译器技术来使学生，科学家，数据分析师和工程师简单且可以访问稀疏的数据处理。TACO是第一个稀疏的张量代数编译器，可以采用任何复杂的张量代数代数表达式（每个张量都可以从任何现有的稀疏形式和概述的较高的概述）和密集的构图（CORTARE）较高的过程中，并具有较高的征用量）（CORTARE），并构成了较高的概述（CERTARITAR）（CONTERTIAN），并具有较高的构图（CARTEARTER）。单位（GPU）代码可与当前最新的手工优化库相媲美。使用炸玉米饼，稀疏张量代数可以与密度张量代数和数组代码相同的编译器转换和代码生成基础。拟议的项目将使炸玉米饼商业化，以便更轻松地分析复杂数据。炸玉米饼的力量和简单性可能会导致更多的分析和更好的算法，进一步扩大经济和社会影响。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和更广泛影响的评估审查标准来通过评估来获得支持的。