Improving the Programmability of A Distributed Hardware Accelerator

Sparse iterative matrix algorithms are critical to many scientific and engineering workloads, yet they perform poorly on conventional hardware. (Ōmeteōtl, a new hardware accelerator with a distributed-memory and task-based execution model, aims to address these performance bottlenecks. However, programming for (Ōmeteōtl is low-level, error-prone, and far removed from the simplicity of typical iterative formulations. This thesis presents Lapis, a domain-specific language and compiler that allows users to express sparse matrix algorithms in high-level Python code and automatically generates efficient C++ code for (Ōmeteōtl. Lapis abstracts away data partitioning and task orchestration, reducing implementation complexity: for example, it lowers lines of code by 30× for conjugate gradients and 46× for power iteration. Despite this abstraction, generated code achieves 75.7% to 92.6% of the performance of manually written implementations across several benchmarks.

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

2025

Author dc:creator

• Shwatal, Nathan A.

Advisor dc:contributor.advisor

• Sanchez, Daniel

Statement dc:rights

• Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
• Copyright retained by author(s)

Licence dc:rights.uri

• https://creativecommons.org/licenses/by-nc-nd/4.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/162938

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/162938