We investigate multi-level parallelism on GPU clusters with MPI-CUDA and hybrid MPI-OpenMP-CUDA parallel implementations, in which all computations are done on the GPU using CUDA. We explore efficiency and scalability of incompressible flow computations using up to 256 GPUs on a problem with approximately 17.2 billion cells. Our work addresses some of the unique issues faced when merging fine-g...

With the development of synthetic aperture radar (SAR) technologies in recent years, the huge amount of remote sensing data brings challenges for real-time imaging processing. Therefore, high performance computing (HPC) methods have been presented to accelerate SAR imaging, especially the GPU based methods. In the classical GPU based imaging algorithm, GPU is employed to accelerate image proces...

We propose a four-tiered parallelization model for acceleration of the secure multiparty computation (SMC) on the CUDA based Graphic Processing Unit (GPU) cluster architecture. Specification layer is the top layer, which adopts the SFDL of Fairplay for specification of secure computations. The SHDL file generated by the SFDL compiler of Fairplay is used as inputs to the function layer, for whic...

The maximum entropy method (MEM) is a well known deconvolution technique in radio-interferometry. This method solves a non-linear optimization problem with an entropy regularization term. Other heuristics such as CLEAN are faster but highly user dependent. Nevertheless, MEM has the following advantages: it is unsupervised, it has a statistical basis, it has a better resolution and better image ...

Graphics Processing Units (GPU) in today’s desktops can well be thought of as a high performance parallel processor. Traditionally, parallel computing is the usage of multiple computing resources to execute computational problems simultaneously. Such computations are possible using multi-core CPUs or computers with multiple CPUs or by using a network of computers in parallel. Today’s GPUs are c...

Noise removal operation is commonly applied as pre-processing step before subsequent image processing tasks due to the occurrence of noise during acquisition or transmission process. A common problem in imaging systems by using CMOS or CCD sensors is appearance of  the salt and pepper noise. This paper presents Cellular Automata (CA) framework for noise removal of distorted image by the salt an...

In this paper, we apply Particle-based Volume Rendering (PBVR) technique using a current programmable GPU architecture. Recently, the increasing programmability of GPU offers an efficient method of SIMD parallel algorithm to solve the speed problem. Due to the each point or pixel can be calculated independently, we use programmable graphics hardware to delegate all expensive rendering tasks to ...

The finite element method (FEM) is a widely employed numerical technique for approximating the solution of partial differential equations (PDEs) in various science and engineering applications. Many of these applications benefit from fast execution of the FEM pipeline. One way to accelerate the FEM pipeline is by exploiting advances in modern computational hardware, such as the many-core stream...

The Neural GPU is a recent model that can learn algorithms such as multi-digit binary addition and binary multiplication in a way that generalizes to inputs of arbitrary length. We show that there are two simple ways of improving the performance of the Neural GPU: by carefully designing a curriculum, and by increasing model size. The latter requires a memory efficient implementation, as a naive...

High-accuracy optimization is the key component of time-sensitive applications in computer sciences such as machine learning, and we develop single-GPU Iterative Discrete Approximation Monte Carlo Optimization (IDAMCS) and multi-GPU IDA-MCS in our previous research. However, because of the memory capability constrain of GPUs in a workstation, single-GPU IDA-MCS and multiGPU IDA-MCS may be in lo...