Efficient GPU Implementation for Single Block Orthogonal Dictionary Learning

TL;DR

This paper introduces a GPU-accelerated implementation of the SBO dictionary learning algorithm using OpenCL, significantly speeding up the process while maintaining high data representation quality.

Contribution

The paper presents a novel lock-free GPU implementation of SBO with a map-reduce and PGAS model, improving speed over traditional methods.

Findings

Achieves significant acceleration in dictionary learning time

Maintains comparable data representation quality to PAK-SVD

Demonstrates effective GPU utilization with lock-free design

Abstract

Dictionary training for sparse representations involves dealing with large chunks of data and complex algorithms that determine time consuming implementations. SBO is an iterative dictionary learning algorithm based on constructing unions of orthonormal bases via singular value decomposition, that represents each data item through a single best fit orthobase. In this paper we present a GPGPU approach of implementing SBO in OpenCL. We provide a lock-free solution that ensures full-occupancy of the GPU by following the map-reduce model for the sparse-coding stage and by making use of the Partitioned Global Address Space (PGAS) model for developing parallel dictionary updates. The resulting implementation achieves a favourable trade-off between algorithm complexity and data representation quality compared to PAK-SVD which is the standard overcomplete dictionary learning approach. We present and discuss numerical results showing a significant acceleration of the execution time for the dictionary learning process.