WACO: Learning workload-aware co-optimization of the format and schedule of a sparse tensor program

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.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2023

Author dc:creator

• Won, Jaeyeon

Advisors dc:contributor.advisor

• Emer, Joel S.
• Amarasinghe, Saman

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/152757

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/152757