Posts
Oct, 28
An Implementation of the Smooth Particle Mesh Ewald Method on GPU Hardware
The smooth particle mesh Ewald summation method is widely used to efficiently compute long-range electrostatic force terms in molecular dynamics simulations, and there has been considerable work in developing optimized implementations for a variety of parallel computer architectures. We describe an implementation for Nvidia graphical processing units (GPUs) which are general purpose computing devices with […]
Oct, 28
Fuzzy ART Neural Network Parallel Computing on the GPU
Graphics Processing Units (GPUs) have evolved into powerful programmable processors, faster than Central Processing Units (CPUs) regarding the execution of parallel algorithms. In this paper, an implementation of a Fuzzy ART Neural Network on the GPU is presented. Experimental results show training process is slower on the GPU than on a dual-core Pentium 4 at […]
Oct, 28
GAMER: a GPU-Accelerated Adaptive Mesh Refinement Code for Astrophysics
We present the newly developed code, GAMER (GPU-accelerated Adaptive MEsh Refinement code), which has adopted a novel approach to improve the performance of adaptive mesh refinement (AMR) astrophysical simulations by a large factor with the use of the graphic processing unit (GPU). The AMR implementation is based on a hierarchy of grid patches with an […]
Oct, 28
High performance cellular level agent-based simulation with FLAME for the GPU
Driven by the availability of experimental data and ability to simulate a biological scale which is of immediate interest, the cellular scale is fast emerging as an ideal candidate for middle-out modelling. As with bottom-up’ simulation approaches, cellular level simulations demand a high degree of computational power, which in large-scale simulations can only be achieved […]
Oct, 28
CrystalGPU: Transparent and Efficient Utilization of GPU Power
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 […]
Oct, 28
Fast CGH computation using S-LUT on GPU
In computation of full-parallax computer-generated hologram (CGH), balance between speed and memory usage is always the core of algorithm development. To solve the speed problem of coherent ray trace (CRT) algorithm and memory problem of look-up table (LUT) algorithm without sacrificing reconstructed object quality, we develop a novel algorithm with split look-up tables (S-LUT) and […]
Oct, 28
All-pairs shortest-paths for large graphs on the GPU
The all-pairs shortest-path problem is an intricate part in numerous practical applications. We describe a shared memory cache efficient GPU implementation to solve transitive closure and the all-pairs shortest-path problem on directed graphs for large datasets. The proposed algorithmic design utilizes the resources available on the NVIDIA G80 GPU architecture using the CUDA API. Our […]
Oct, 28
An integrated GPU power and performance model
GPU architectures are increasingly important in the multi-core era due to their high number of parallel processors. Performance optimization for multi-core processors has been a challenge for programmers. Furthermore, optimizing for power consumption is even more difficult. Unfortunately, as a result of the high number of processors, the power consumption of many-core processors such as […]
Oct, 27
GPU as a General Purpose Computing Resource
In the last few years, GPUs(Graphics Processing Units) have made rapid development. Their ever-increasing computing power and decreasing cost have attracted attention from both industry and academia. In addition to graphics applications, researchers are interested in using them for general purpose computing. Recently, NVIDIA released a new computing architecture, CUDA (Compute Uni¿ed Device Architecture), for […]
Oct, 27
Fast and scalable list ranking on the GPU
General purpose programming on the graphics processing units (GPGPU) has received a lot of attention in the parallel computing community as it promises to offer the highest performance per dollar. The GPUs have been used extensively on regular problems that can be easily parallelized. In this paper, we describe two implementations of List Ranking, a […]
Oct, 27
Parallelization of cellular neural networks on GPU
Recently, cellular neural networks (CNNs) have been demonstrated to be a highly effective paradigm applicable in a wide range of areas. Typically, CNNs can be implemented using VLSI circuits, but this would unavoidably require additional hardware. On the other hand, we can also implement CNNs purely by software; this, however, would result in very low […]
Oct, 27
GPU-Based FFT Computation for Multi-Gigabit WirelessHD Baseband Processing
The next generation Graphics Processing Units (GPUs) are being considered for non-graphics applications. Millimeter wave (60 Ghz) wireless networks that are capable of multi-gigabit per second (Gbps) transfer rates require a significant baseband throughput. In this work, we consider the baseband of WirelessHD, a 60 GHz communications system, which can provide a data rate of […]