Recently, general purpose GPU (GPGPU) programming has spread rapidly after CUDA was first introduced to write parallel programs in high-level languages for NVIDIA GPUs. While a GPU exploits data parallelism very effectively, task-level parallelism is exploited as a multi-threaded program on a multicore CPU. For such a heterogeneous platform that consists of a multicore CPU and GPU, in this pape...

General-purpose computing on graphics processing units (GPGPU) has recently gained considerable attention in various domains such as bioinformatics, databases and distributed computing. GPGPU is based on using the GPU as a co-processor accelerator to offload computationally-intensive tasks from the CPU. This study starts from the observation that a number of GPU features (such as overlapping co...

Title of dissertation: OPTIMIZATION TECHNIQUES FOR MAPPING ALGORITHMS AND APPLICATIONS ONTO CUDA GPU PLATFORMS AND CPU-GPU HETEROGENEOUS PLATFORMS Jing Wu, Doctor of Philosophy, 2014 Dissertation directed by: Professor Joseph F JaJa, Department of Electrical and Computer Engineering An emerging trend in processor architecture seems to indicate the doubling of the number of cores per chip every ...

In this paper we present GPU-Quicksort, an efficient Quicksort algorithm suitable for highly parallel multi-core graphics processors. Quicksort has previously been considered as an inefficient sorting solution for graphics processors, but we show that GPU-Quicksort often performs better than the fastest known sorting implementations for graphics processors, such as radix and bitonic sort. Quick...

We investigate the performance of two approaches for matrix inversion based on Gaussian (LU factorization) and Gauss-Jordan eliminations. The target architecture is a current general-purpose multicore processor connected to a graphics processor (GPU). Parallelism is extracted in both processors by linking sequential versions of the codes with multi-threaded implementations of BLAS. Our results ...