{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/152757"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/152757","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"WACO: Learning workload-aware co-optimization of the format and schedule of a sparse tensor program","abstract":"Leveraging the existence of the large number of zeros in sparse tensors offer a powerful way to solve complex problems efficiently in many applications. However, optimizing the performance of those applications poses a challenge. Sparse tensor programs must find the ideal balance between data format and implementation strategy to achieve optimal performance. This thesis presents WACO, a novel method of co-optimizing the format and schedule of a given sparsity pattern in a sparse tensor program. A core challenge in this thesis is the design of a lightweight cost model that accurately predicts the runtime of a sparse tensor program by considering the sparsity pattern, the format, and the schedule. The key idea in addressing this is exploiting a sparse convolutional network to learn meaningful features of the sparsity pattern and embedding a coupled behavior between the format and the schedule using a specially designed schedule template. In addition, within the enormous search space of co-optimization, our novel search strategy, an approximate nearest neighbor search, efficiently and accurately retrieves the best format and schedule for a given sparsity pattern. We evaluate WACO for four different algorithms (SpMV, SpMM, SDDMM, and MTTKRP) on a CPU using 726 different sparsity patterns. Our experimental results shows that WACO outperformed four state-of-the-art baselines, Intel MKL, Formatonly auto-tuner, TACO with a default schedule, and ASpT. Compared to the best of four baselines, WACO achieved 1.43×, 1.18×, 1.14×, and 1.27× average speedups on SpMV, SpMM, SDDMM, and MTTKRP, respectively.","abstract_html":"Leveraging the existence of the large number of zeros in sparse tensors offer a powerful way to solve complex problems efficiently in many applications. However, optimizing the performance of those applications poses a challenge. Sparse tensor programs must find the ideal balance between data format and implementation strategy to achieve optimal performance. This thesis presents WACO, a novel method of co-optimizing the format and schedule of a given sparsity pattern in a sparse tensor program. A core challenge in this thesis is the design of a lightweight cost model that accurately predicts the runtime of a sparse tensor program by considering the sparsity pattern, the format, and the schedule. The key idea in addressing this is exploiting a sparse convolutional network to learn meaningful features of the sparsity pattern and embedding a coupled behavior between the format and the schedule using a specially designed schedule template. In addition, within the enormous search space of co-optimization, our novel search strategy, an approximate nearest neighbor search, efficiently and accurately retrieves the best format and schedule for a given sparsity pattern. We evaluate WACO for four different algorithms (SpMV, SpMM, SDDMM, and MTTKRP) on a CPU using 726 different sparsity patterns. Our experimental results shows that WACO outperformed four state-of-the-art baselines, Intel MKL, Formatonly auto-tuner, TACO with a default schedule, and ASpT. Compared to the best of four baselines, WACO achieved 1.43×, 1.18×, 1.14×, and 1.27× average speedups on SpMV, SpMM, SDDMM, and MTTKRP, respectively.","abstract_has_math":false,"creators":["Won, Jaeyeon"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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However, optimizing the performance of those applications poses a challenge. Sparse tensor programs must find the ideal balance between data format and implementation strategy to achieve optimal performance. This thesis presents WACO, a novel method of co-optimizing the format and schedule of a given sparsity pattern in a sparse tensor program. A core challenge in this thesis is the design of a lightweight cost model that accurately predicts the runtime of a sparse tensor program by considering the sparsity pattern, the format, and the schedule. The key idea in addressing this is exploiting a sparse convolutional network to learn meaningful features of the sparsity pattern and embedding a coupled behavior between the format and the schedule using a specially designed schedule template. In addition, within the enormous search space of co-optimization, our novel search strategy, an approximate nearest neighbor search, efficiently and accurately retrieves the best format and schedule for a given sparsity pattern. We evaluate WACO for four different algorithms (SpMV, SpMM, SDDMM, and MTTKRP) on a CPU using 726 different sparsity patterns. Our experimental results shows that WACO outperformed four state-of-the-art baselines, Intel MKL, Formatonly auto-tuner, TACO with a default schedule, and ASpT. Compared to the best of four baselines, WACO achieved 1.43×, 1.18×, 1.14×, and 1.27× average speedups on SpMV, SpMM, SDDMM, and MTTKRP, respectively."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["S.M."]},{"key":"dc:title","label":"Title","values":["WACO: Learning workload-aware co-optimization of the format and schedule of a sparse tensor program"]}]}],"canonical_facts":{"dc:contributor.advisor":["Emer, Joel S.","Amarasinghe, Saman"],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science"],"dc:creator":["Won, Jaeyeon"],"dc:date.accessioned":["2023-11-02T20:13:42Z"],"dc:date.available":["2023-11-02T20:13:42Z"],"dc:date.issued":["2023-09"],"dc:description.abstract":["Leveraging the existence of the large number of zeros in sparse tensors offer a powerful way to solve complex problems efficiently in many applications. 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In addition, within the enormous search space of co-optimization, our novel search strategy, an approximate nearest neighbor search, efficiently and accurately retrieves the best format and schedule for a given sparsity pattern. We evaluate WACO for four different algorithms (SpMV, SpMM, SDDMM, and MTTKRP) on a CPU using 726 different sparsity patterns. Our experimental results shows that WACO outperformed four state-of-the-art baselines, Intel MKL, Formatonly auto-tuner, TACO with a default schedule, and ASpT. 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