An Application-Oriented Approach for Accelerating Data-Parallel Computation with Graphics Processing Unit
2009) An Application-Oriented Approach for Accelerating Data-Parallel Computation with Graphics Processing Unit. Technical Report TR-09-05, Computer Science, Virginia Tech. (
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Abstract
This paper presents a novel parallelization and quantitative characterization of various optimization strategies for data-parallel computation on a graphics processing unit (GPU) using NVIDIA's new GPU programming framework, Compute Unified Device Architecture (CUDA). CUDA is an easy-to-use development framework that has drawn the attention of many different application areas looking for dramatic speed-ups in their code. However, the performance tradeoffs in CUDA are not yet fully understood, especially for data-parallel applications. Consequently, we study two fundamental mathematical operations that are common in many data-parallel applications: convolution and accumulation. Specifically, we profile and optimize the performance of these operations on a 128-core NVIDIA GPU. We then characterize the impact of these operations on a video-based motion-tracking algorithm called vector coherence mapping, which consists of a series of convolutions and dynamically weighted accumulations, and present a comparison of different implementations and their respective performance profiles.
Item Type: | Departmental Technical Report |
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Keywords: | Video tracking, GPGPU, convolution, accumulation |
Subjects: | Computer Science > Parallel Computation Computer Science > Algorithms and Data Structure |
ID Code: | 1064 |
Deposited By: | Cao, Assistant Professor Yong |
Deposited On: | 04 March 2009 |