Fast Multistage Compilation of Machine Learning
Computation Graphs
Name
dighe-kdighe-meng-eecs-2024-thesis.pdf
Description
Thesis PDF
Size
1.35 MB
Format
Adobe PDF
Checksum (MD5)
b24f16404909471326cae33811167a93
Author(s)
Dighe, Kaustubh
Advisor(s)
Amarasinghe, Saman
Date Issued
May 2024
Publisher
Massachusetts Institute of Technology
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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