Efficient memory and GPU operations for Tiramisu compiler

The increasing complexity of computer architectures with different memory hierarchies and parallelism characteristics makes generating efficient code a difficult task. Achieving high performance requires complex schedules and data layout transformations which might not be easy to express in a low level language. TIRAMISU [3] is an optimization framework for generating efficient code for different platforms including CPU, GPU, and distributed systems. It combines the polyhedral intermediate representation with rich scheduling and data layout commands, creating a high level interface to generate high performance code. In this thesis, we present new memory interfaces and GPU operators implemented to extend TIRAMISU compiler. We demonstrate that these features enable users to generate high performance GPU code with concise TIRAMISU programs. We also evaluate TIRAMISU's GPU backend with two benchmarks, matrix multiplication and a recurrent neural network architecture, showing that TIRAMISU outperforms other polyhedral compilers and popular library implementations.

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

2019

Author dc:creator

• Akkas, Abdurrahman.

Advisor dc:contributor.advisor

• Saman P. Amarasinghe.

• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/123074

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/123074