There is increasing interest in utilizing Graphical Processing Units for general-purpose computations. While substantial effort has been made on improving the programmability and performance of General Purpose GPU systems, little attention has been paid to verifying the correctness of the programs running on these systems. We present a preliminary automated symbolic verifier based on mechanical...

General purpose application development for GPUs (GPGPU) has recently gained momentum as a cost-effective approach for accelerating dataand compute-intensive applications, pushed to the forefront by the introduction of Cbased programming environments such as NVIDIA’s CUDA, [1], OpenCL [2], and Intel’s Ct [3]. While significant effort has been focused on developing and evaluating applications an...

In view of the problem that computing shortest paths in a graph is a complex and time-consuming process, and the traditional algorithm that rely on the CPU as computing unit solely can't meet the demand of real-time processing, in this paper, we present an all-pairs shortest paths algorithm using MPI+CUDA hybrid programming model, which can take use of the overwhelming computing power of the GP...

We present new versions of the previously published C and CUDA programs for solving the dipolar Gross-Pitaevskii equation in one, two, and three spatial dimensions, which calculate stationary and non-stationary solutions by propagation in imaginary or real time. Presented programs are improved and parallelized versions of previous programs, divided into three packages according to the type of p...

Recently, OpenCL, a new open programming standard for GPGPU programming, has become available in addition to CUDA. OpenCL can support various compute devices due to its higher abstraction programming framework. Since there is a semantic gap between OpenCL and compute devices, the OpenCL C compiler plays important roles to exploit the potential of compute devices and therefore its capability sho...

We discuss several strategies to implement Dykstra’s projection algorithm on NVIDIA’s compute unified device architecture (CUDA). Dykstra’s algorithm is the central step in and the computationally most expensive part of statistical multi-resolution methods. It projects a given vector onto the intersection of convex sets. Compared with a CPU implementation our CUDA implementation is one order of...

MOTIVATION Mathematical modelling is central to systems and synthetic biology. Using simulations to calculate statistics or to explore parameter space is a common means for analysing these models and can be computationally intensive. However, in many cases, the simulations are easily parallelizable. Graphics processing units (GPUs) are capable of efficiently running highly parallel programs and...

Modern Graphic Processing Units (GPUs) offer significant performance speedup over conventional processors. Programming on GPU for general purpose applications has become an important research area. CUDA programming model provides a C-like interface and is widely accepted. However, since hardware vendors do not disclose enough underlying architecture details, programmers have to optimize their a...

Radar Signal Processing algorithms place strong real-time performance demands on computer architectures. These algorithms do however have an inherent data-parallelism that allows for great performance on massively parallel architectures, such as the Graphics Processing Unit (GPUs). Recently, using GPUs for other than graphics processing has become a possibility through the CUDA and OpenCL (Open...

The finite-difference time-domain (FDTD) method has been commonly utilized in the numerical solution of electromagnetic (EM) waves propagation through the plasma media. However, the FDTD method may bring about a significant increment in additional run-times consuming for computationally large and complicated EM problems. Graphics Processing Unit (GPU) computing based on Compute Unified Device A...