2026-09-18T19:04:26Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1567742024-09-17T04:07:02Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Amarasinghe, Saman
Dighe, Kaustubh
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2024-09-16T13:48:18Z
2024-09-16T13:48:18Z
2024-05
2024-07-11T14:37:05.421Z
https://hdl.handle.net/1721.1/156774
https://orcid.org/0009-0006-9656-1946
Machine learning applications are increasingly requiring fast and more computational power. Many applications like language models have become so large that they are run on distributed systems in parallel. However, getting into the details of optimally scheduling or even just running machine learning models on distributed systems can be a distraction for researchers ideating models. Hence there has been development of abstractions to facilitate running machine learning models in parallel on distributed systems. We present a compiler for the StreamIt language- a language made for abstract signal processing and multicore programming. We use that abstraction as a way to distribute the computation of machine learning models programmed in PyTorch.
M.Eng.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright retained by author(s)
https://rightsstatements.org/page/InC-EDU/1.0/
Fast Multistage Compilation of Machine Learning
Computation Graphs
Thesis
application/pdf
Master
Master of Engineering in Electrical Engineering and Computer Science
Publication
unknown
unknown
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Computation Graphs</Title>
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<Abstract>Machine learning applications are increasingly requiring fast and more computational power. Many applications like language models have become so large that they are run on distributed systems in parallel. However, getting into the details of optimally scheduling or even just running machine learning models on distributed systems can be a distraction for researchers ideating models. Hence there has been development of abstractions to facilitate running machine learning models in parallel on distributed systems. We present a compiler for the StreamIt language- a language made for abstract signal processing and multicore programming. We use that abstraction as a way to distribute the computation of machine learning models programmed in PyTorch.</Abstract>
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