Apr, 5

Enabling Energy-Efficient Analysis of Massive Neural Signals Using GPGPU

Analysis of neural signals (such as EEG) has long been a hot topic in neuroscience community due to neural signals’ nonlinear and non-stationary features. Recent advances of experimental methods and neuroscience research have made neural signals constantly massive and analysis of these signals highly compute-intensive. Analysis of neural signals has been routinely performed upon CPU-based […]
Apr, 5

GPGPU-Aided Ensemble Empirical-Mode Decomposition for EEG Analysis During Anesthesia

Ensemble empirical-mode decomposition (EEMD) is a novel adaptive time-frequency analysis method, which is particularly suitable for extracting useful information from noisy nonlinear or nonstationary data. Unfortunately, since the EEMD is highly compute-intensive, the method does not apply in real-time applications on top of commercial-off-the-shelf computers. Aiming at this problem, a parallelized EEMD method has been […]
Apr, 5

GpuWars: Design and Implementation of a GPGPU Game

The GPUs (Graphics Processing Units) have evolved into extremely powerful and flexible processors, allowing its usage for processing different data. This advantage can be used in game development to optimize the game loop. Most GPGPU works deals only with some steps of the game loop, allowing to the CPU to process most of the game […]
Apr, 5

Development of nonlinear filter bank system for real-time beautification of facial video using GPGPU

A nonlinear filter bank named as an ∈-filter bank is implemented for real-time processing of video in order to make the skin in human faces look beautified. General-purpose computing on graphics processing units (GPGPU) is utilized for this real-time implementation. GPGPU has quite high computational power, and the facial beautification system using the ∈-filter bank […]
Apr, 5

Neuromorphic models on a GPGPU cluster

There is currently a strong push in the research community to develop biological scale implementations of neuron based vision models. Systems at this scale are computationally demanding and have generally utilized more accurate neuron models, such as the Izhikevich and Hodgkin- Huxley models, in favor of the integrate and fire model. This paper examines the […]
Apr, 5

A GPGPU-Based Collision Detection Algorithm

A GPGPU-based collision detection algorithm is proposed. Firstly, the information of OBB hierarchy tree and triangles of tested objects are mapped into some data textures designed for GPGPU-based calculation, such as triangle vertex textures, bounding box size texture, tree node relationship texture, etc., then these textures are downloaded to GPU to complete the data preparation. […]
Apr, 5

GPGPU supported cooperative acceleration in molecular dynamics

Molecular dynamics simulations have become a significant computational approach to study complicated physical phenomena at the atomic level. Nevertheless, accurate simulations are limited in size and timescale by the available computing resources, which make the simulations very time-consuming. This consequentially leads to tremendous computational requirements. Therefore, the need for speeding up this process is crucial. […]
Apr, 5

Parallelizing Simulated Annealing-Based Placement Using GPGPU

Simulated annealing has became the de facto standard for FPGA placement engines since it provides high quality solutions and is robust under a wide range of objective functions. However, this method will soon become prohibitive due to its sequential nature and since the performance of single-core processor has stagnated. General purpose computing on graphics processing […]
Apr, 5

GPGPU-FDTD method for 2-dimensional electromagnetic field simulation and its estimation

For signal/power integrity analysis of the high density packages and printed circuit boards, the FDTD (Finite-Difference Time-Domain) method has been widely used. In order to apply to large-scale problems, a variety of acceleration techniques are required. This paper describes a GPGPU-FDTD (General Purpose computing on GPU (Graphic Processing Unit)-Finite-Difference Time-Domain) method for massively parallel electromagnetic […]
Apr, 4

A Case Study of SWIM: Optimization of Memory Intensive Application on GPGPU

Recently, GPGPU has been adopted well in the High Performance Computing (HPC) field. The limited global memory bandwidth poses a great challenge to many GPGPU programmers trying to exploit parallelism within the CPU-GPU heterogeneous platform. In this paper, we choose SWIM, a typical memory intensive application from the SPEC OMP 2001 benchmark suite, for case […]
Apr, 4

Many-Thread Aware Prefetching Mechanisms for GPGPU Applications

We consider the problem of how to improve memory latency tolerance in massively multithreaded GPGPUs when the thread-level parallelism of an application is not sufficient to hide memory latency. One solution used in conventional CPU systems is prefetching, both in hardware and software. However, we show that straightforwardly applying such mechanisms to GPGPU systems does […]
Apr, 4

GPGPU implementation of a synaptically optimized, anatomically accurate spiking network simulator

Simulation of biological spiking networks is becoming more relevant in understanding neuronal processes. An increasing proportion of these simulations focuses on large scale modeling efforts. Unfortunately the size of large networks is often limited by both computational power and memory. Computational power constrains both the maximum number of differential equations and the maximum number of […]
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Free GPU computing nodes at hgpu.org

Registered users can now run their OpenCL application at hgpu.org. We provide 1 minute of computer time per each run on two nodes with two AMD and one nVidia graphics processing units, correspondingly. There are no restrictions on the number of starts.

The platforms are

Node 1
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  • GPU device 1: AMD/ATI Radeon HD 6970 2GB, 880MHz
  • CPU: AMD Phenom II X6 @ 2.8GHz 1055T
  • RAM: 12GB
  • OS: OpenSUSE 13.1
  • SDK: AMD APP SDK 2.9
Node 2
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  • GPU device 1: nVidia GeForce GTX 560 Ti 2GB, 822MHz
  • CPU: Intel Core i7-2600 @ 3.4GHz
  • RAM: 16GB
  • OS: OpenSUSE 12.2
  • SDK: nVidia CUDA Toolkit 6.0.1, AMD APP SDK 2.9

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