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

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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.