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.

这一创新-技术转换伙伴关系(PFI-TT)项目的广泛影响/商业潜力使张量代数编译器(TACO)未来能够商业化，该编译器利用稀疏数据处理来揭示隐藏的数据属性，从而可以在许多领域带来改进。随着世界变得更加数字化和高度仪表化，稀疏的数据集被捕获并存储在云上的中央存储库中。这些庞大的数据集存在于所有可以想象到的领域，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。然而，分析和使用这些数据需要以高性能和可伸缩的方式在稀疏数据集上使用许多复张量和矩阵代数算法。到目前为止，最先进的系统过于复杂或能力有限，无法让从业者充分利用这些数据集的潜力。这个PFI翻译研究和技术开发项目将使用编译器技术使稀疏数据处理变得简单，学生、科学家、数据分析师和工程师都可以访问。TACO是第一个稀疏张量代数编译器，可以接受任何复杂张量代数表达式(其中每个张量可以来自任何现有的稀疏和密集张量格式)，并生成可与当前最先进的手动优化库相媲美的高性能中央处理单元(CPU)和图形处理单元(GPU)代码。使用TACO，稀疏张量代数可以与密集张量代数和数组代码处于相同的编译器转换和代码生成基础上。拟议的项目将使Taco商业化，使复杂数据的分析变得更容易。TACO的力量和简单性可能会带来更多的分析和更好的算法，进一步扩大经济和社会影响。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。