Utilizing GPU tensor cores for algorithmic acceleration

There has been a surge in the demand for a Domain Specific Architecture due to wide ranging deep learning applications like Image classification, speech recognition, in healthcare, self-driving cars etc. Matrix Multiplication acceleration has been a popular design choice when creating these specialized units to boost deep learning training and inference. Nvidia's Volta architecture introduced Tensor Cores which promised a 3 times speedup over their Pascal architecture. Despite the favorable performance gains, these accelerators have not been applied extensively to a wider class of algorithms. Through this thesis we introduce novel ways of mapping various algorithms on the Tensor Cores. We implemented Tensor Core based reduction, power iteration and Fast Fourier Transform (FFT) and show that effectively utilizing GPU compute resources would result in substantial gains in performance. Our reduction gave a 1.5 times speedup against CUB API; power iteration gave on average 2 times the speedup against Thrust and cuBLAS based implementation while our FFT implementation was able to outperform cuFFT with up to 8 times the speedup.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2020

Author dc:creator

• Durrani, Sultan Hayat Khan

Contributors dc:contributor

• Hwu, Wen-Mei W

• Computer Architecture
• GPU
• Tensor Cores
• CUDA
• FFT

Statement dc:rights

• Copyright 2020 Sultan Hayat Khan Durrani

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/108349

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/108349