General-purpose computation on Graphics Processing Units (GPU) on personal computers has recently become an attractive alternative to parallel computing on clusters and supercomputers. We present the GPU-implementation of an accurate molecular dynamics algorithm for a system of spheres. The new hybrid CPU-GPU implementation takes into account all the degrees of freedom, including the quaternion...

Graphics processors (GPU) are interesting for nongraphics parallel computation because of the potential for more than an order of magnitude of speedup over CPUs. Because the GPU is often presented as a C-like abstraction like Nvidia’s CUDA, little is known about the hardware architecture of the GPU beyond the high-level descriptions documented by the manufacturer. We develop a suite of micro-be...

Graphics Processing Units (GPUs) have been emerged as powerful parallel compute platforms for various application domains. A GPU consists of hundreds or even thousands processor cores and adopts Single Instruction Multiple Threading (SIMT) architecture. Previously, we have proposed an approach that optimizes the Tabu Search algorithm for solving the Permutation Flowshop Scheduling Problem (PFSP...

Graph is a widely used data structure and graph algorithms, such as breadth-first search (BFS), are regarded as key components in a great number of applications. Recent studies have attempted to accelerate graph algorithms on highly parallel graphics processing unit (GPU). Although many graph algorithms based on large graphs exhibit abundant parallelism, their performance on GPU still faces for...

Modern graphics processing units (GPUs) have been at the leading edge of increasing chip-level parallelism over the last ten years, and the CUDA programming model has recently allowed us to exploit its power across many computational domains. Within them, dense linear algebra algorithms emerge like a natural fit for CUDA and the GPU because they are usually inherently parallel and can naturally...

The skyline operator returns records in a dataset that provide optimal trade-offs of multiple dimensions. State-of-theart skyline computation involves complex tree traversals, data-ordering, and conditional branching to minimize the number of point-to-point comparisons. Meanwhile, GPGPU computing offers the potential for parallelizing skyline computation across thousands of cores. However, atte...

Task parallelism is an attractive approach to automatically load balance the computation in a parallel system and adapt to dynamism exhibited by parallel systems. Exploiting task parallelism through work stealing has been extensively studied in shared and distributed-memory contexts. In this paper, we study the design of a system that uses work stealing for dynamic load balancing of task-parall...

As bioinspired methods usually need a high amount of computational resources, parallelization is an interesting alternative in order to decrease the execution time and to provide accurate results. In this sense, recently there has been a growing interest in developing parallel algorithms using graphic processing units (GPU) also referred as GPU computation. Advances in the video gaming industry...

In this paper we present a method to create textures on smooth surfaces of arbitrary topology using ReactionDiffusion systems in a graphics processing unit (GPU). To do this we use a parametrization of Catmull-Clark subdivision surfaces and obtain the metric information of the distortion caused by this parametrization, so we can calculate differential operators of functions defined on this surf...

There is increased interest in the use of graphics processing units (GPUs) for general purpose computation. This is because GPUs are almost two orders of magnitude faster in terms of floating point throughput compared to conventional CPUs. In this paper we investigate the use of graphics processing units for accelerating signal processing algorithms, specifically FIR filters and the FFT. We des...