Efficient Kernel Fusion Techniques for Massive Video Data Analysis on GPGPUs

TL;DR

This paper introduces a novel kernel fusion technique for GPGPU-based video analysis, significantly enhancing execution speed and data throughput for real-time facial feature tracking applications.

Contribution

It presents a new optimization model and algorithms for kernel fusion on GPGPUs, improving performance in massive video data processing.

Findings

Performance improved 2-3 times over sequential execution

Reduced data traffic leads to better throughput

Effective for real-time facial feature tracking

Abstract

Kernels are executable code segments and kernel fusion is a technique for combing the segments in a coherent manner to improve execution time. For the first time, we have developed a technique to fuse image processing kernels to be executed on GPGPUs for improving execution time and total throughput (amount of data processed in unit time). We have applied our techniques for feature tracking on video images captured by a high speed digital video camera where the number of frames captured varies between 600-1000 frames per second. Image processing kernels are composed of multiple simple kernels, which executes on the input image in a given sequence. A set of kernels that can be fused together forms a partition (or fused kernel). Given a set of Kernels and the data dependencies between them, it is difficult to determine the partitions of kernels such that the total performance is maximized (execution time and throughput). We have developed and implemented an optimization model to find such a partition. We also developed an algorithm to fuse multiple kernels based on their data dependencies. Additionally, to further improve performance on GPGPU systems, we have provided methods to distribute data and threads to processors. Our model was able to reduce data traffic, which resulted better performance.The performance (both execution time and throughput) of the proposed method for kernel fusing and its subsequent execution is shown to be 2 to 3 times higher than executing kernels in sequence. We have demonstrated our technique for facial feature tracking with applications to Neuroscience.