Fast and scalable list ranking on the GPU

General purpose programming on the graphics processing units (GPGPU) has received a lot of attention in the parallel computing community as it promises to offer the highest performance per dollar. The GPUs have been used extensively on regular problems that can be easily parallelized. In this paper, we describe two implementations of List Ranking, a traditional irregular algorithm that is difficult to parallelize on such massively multi-threaded hardware. We first present an implementation of Wyllie’s algorithm based on pointer jumping. This technique does not scale well to large lists due to the suboptimal work done. We then present a GPUoptimized, Recursive Helman-J´aJ´a (RHJ) algorithm. Our RHJ implementation can rank a random list of 32 million elements in about a second and achieves a speedup of about 8-9 over a CPU implementation as well as a speedup of 3-4 over the best reported implementation on the Cell Broadband engine. We also discuss the practical issues relating to ...

• Type: Conference
• Year: 2009
• Where: ICS
• Authors: M. Suhail Rehman, Kishore Kothapalli, P. J. Narayanan
• Keywords: ICS 2009, Implementation, Irregular Algorithms, Theoretical Computer Science, Traditional Irregular Algorithm