Accelerating data clustering on GPU-based clusters under shared memory abstraction

Abstract

Many-core graphics processors are playing today an important role in the advancements of modern highly concurrent processors. Their ability to accelerate computation is being explored under several scientific fields. In the current paper we present the acceleration of a widely used data clustering algorithm, K-means, in the context of high performance GPU clusters. As opposed to most related implementation efforts that use MPI to port their target applications on a GPU cluster, our implementation follows the Software Distributed Shared Memory (SDSM) paradigm in order to distribute information and computation across the accelerator cluster. In order to investigate the efficiency of a programming model that offers shared memory abstraction on GPU clusters we present two implementations, one that is based on a SDSM implementation of OpenMP and another that utilizes the Pleiad cluster middleware on top of the Java platform. The first results show that such an implementation is feasible in order to accelerate a broad category of large scale, data intensive applications, among which K-means is a characteristic case.

GPU-based MapReduce for large-scale near-duplicate video retrieval

“…Recently, GPU clusters are realized to address large-scale computing problems. Karantasis et al [13] boost K-Means clustering with a GPU cluster by using shared memory abstraction. In [2], an online image search engine is designed with a GPU cluster.…”