Accelerator-Oriented Algorithm Transformation for Temporal Data Mining

TL;DR

This paper introduces a novel GPU-oriented algorithm transformation for temporal data mining, specifically for frequent episode discovery, enabling efficient analysis of neural spike train data on cost-effective hardware.

Contribution

It presents a new approach to redesign algorithms for GPU architectures, focusing on problem decomposition and data layout, improving performance over traditional CPU and prior GPU methods.

Findings

Significant speedup over CPU implementations.

Effective GPU adaptation for complex data mining algorithms.

Demonstrated scalability across multiple datasets.

Abstract

Temporal data mining algorithms are becoming increasingly important in many application domains including computational neuroscience, especially the analysis of spike train data. While application scientists have been able to readily gather multi-neuronal datasets, analysis capabilities have lagged behind, due to both lack of powerful algorithms and inaccessibility to powerful hardware platforms. The advent of GPU architectures such as Nvidia's GTX 280 offers a cost-effective option to bring these capabilities to the neuroscientist's desktop. Rather than port existing algorithms onto this architecture, we advocate the need for algorithm transformation, i.e., rethinking the design of the algorithm in a way that need not necessarily mirror its serial implementation strictly. We present a novel implementation of a frequent episode discovery algorithm by revisiting "in-the-large" issues such as problem decomposition as well as "in-the-small" issues such as data layouts and memory access patterns. This is non-trivial because frequent episode discovery does not lend itself to GPU-friendly data-parallel mapping strategies. Applications to many datasets and comparisons to CPU as well as prior GPU implementations showcase the advantages of our approach.