Graphics Processing Units (GPUs) are suitable for highly data parallel algorithms such as image processing, due to their massive parallel processing power. Many image processing applications use the histogramming algorithm, which fills a set of bins ...

A frequently used method of clustering is a technique called k-means clustering. The k-means algorithm consists of two steps: A map step, which is simple to execute on a GPU, and a reduce step, which is more problematic. Previous researchers have used a ...

Branch divergence has a significant impact on the performance of GPU programs. We propose two novel software-based optimizations, called iteration delaying and branch distribution that aim to reduce branch divergence. Iteration delaying targets a ...

We generalize a method for avoiding GPU shared communication when dealing with a downsweep pattern. We apply this generalization to Cyclic Reduction, a tridiagonal solver with this pattern. Previously, Cyclic Reduction suffered poor performance when ...

Graphics Processing Units (GPU) have become the platform of choice for accelerating a large range of data parallel and task parallel applications. Both AMD and NVIDIA have developed GPU implementations targeted at the high performance computing market. ...

The Lucas-Lehmer test for Mersenne primality can be efficiently parallelized for GPU-based computation. The gpuLucas project implements an irrational-base discrete weighted transform approach (IBDWT) using balanced-integers, non-power-of-two transforms, ...

Numeric simulations often generate large amounts of data that need to be stored or sent to other compute nodes. This paper investigates whether GPUs are powerful enough to make real-time data compression and decompression possible in such environments, ...

A dense 3-d terrain model obtained using reconstruction methods from aerial images is represented in a probabilistic volumetric framework. The choice of probabilistic representation is to represent inherent ambiguity in reconstruction of surface from ...

In this paper we present the design and implementation of a dynamic instrumentation infrastructure for PTX programs that procedurally transforms kernels and manages related data structures. We show how performing instrumentation within the GPU Ocelot ...

Many developers have begun to realize that heterogeneous multi-core and many-core computer systems can provide significant performance opportunities to a range of applications. Typical applications possess multiple components that can be parallelized; ...

As system architects strive for increased density and power efficiency, the traditional compute node is being augmented with an increasing number of graphics processing units (GPUs). The integration of multiple GPUs per node introduces complex ...

We propose a system-independent representation of sparse matrix formats that allows a compiler to generate efficient, system-specific code for sparse matrix operations. To show the viability of such a representation we have developed a compiler that ...

Performance of applications running on GPUs is mainly affected by hardware occupancy and global memory latency. Scientific applications that rely on analysis using unstructured grids could benefit from the high performance capabilities provided by GPUs, ...