Apr, 26

Study on acceleration technique for two-dimensional FDTD algorithm based on GPU

The Parallel finite difference time domain (FDTD) algorithm is an important method to 1 enhance the speed in multiple data FDTD operation. The improvement of graphics processing unit (GPU) performance, especially the emergence of Computer Unit Device Architecture (CUDA), offers parallel FDTD method an efficient and simple solution. First of all, this paper explains parallel […]
Apr, 26

Making Human Connectome Faster: GPU Acceleration of Brain Network Analysis

The research on complex Brain Networks plays a vital role in understanding the connectivity patterns of the human brain and disease-related alterations. Recent studies have suggested a noninvasive way to model and analyze human brain networks by using multi-modal imaging and graph theoretical approaches. Both the construction and analysis of the Brain Networks require tremendous […]
Apr, 26

Performance Analysis of a New Real-Time Elastographic Time Constant Estimator

New elastographic techniques such as poroelastography and viscoelasticity imaging aim at imaging the temporal mechanical behavior of tissues. These techniques usually involve the use of curve fitting methods being applied to noisy data to estimate new elastographic parameters. As of today, however, current elastographic implementations of poroelastography and viscoelasticity imaging methods are in general too […]
Apr, 26

Model-T: Rethinking the OS for terabit speeds

This paper presents Model-T, an OS network stack designed to scale to terabit rates through pipelined execution of micro operations. Model-T parallelizes execution on multicore chips and enforces lockstep processing to maximize shared L2 data cache (d-cache) hitrate. Executing all operations without hitting main memory more than once (if at all) is the key design […]
Apr, 26

Research on ATI-CAL for accelerating FBP reconstruction

Accelerating CT reconstruction algorithms with general purpose GPU has attracted plenty of attention in recent years. Many researchers have studied the techniques of implement CT reconstruction algorithms on different GPUs and different code development environment to explore their capability and performance of acceleration. This work is to investigate the performance of stream computing of filtered […]
Apr, 25

GPU accelerated fast FEM deformation simulation

In this paper we present a general FEM (finite element method) solution that enables fast dynamic deformation simulation on the newly available GPU (graphics processing unit) hardware with compute unified device architecture (CUDA) from NVIDIA. CUDA-enabled GPUs harness the power of 128 processors which allow data parallel computations. Compared to the previous GPGPU, it is […]
Apr, 25

A GPU implementation for two MIMO-OFDM detectors

Two real-valued signal models based on selective spanning with fast enumeration (SSFE) and layered orthogonal lattice detector (LORD) algorithms are implemented on a Nvidia graphics processing unit (GPU). A 2×2 multiple-input multiple-output (MIMO) antenna system with 16-quadrature amplitude modulation (16-QAM) is assumed. The chosen level update vector for SSFE is based on computer simulation results […]
Apr, 25

Parallel 3D Finite Difference Time Domain Simulations on Graphics Processors with Cuda

Parallel Finite Difference Time Domain (FDTD) method has been explored over past few years because of the expensive computation needed for its application. And General Purpose Graphics Processing Units (GPGPU), especially Computer Unit Device Architecture (CUDA) model, has been offered an efficient and simple solution. This paper analyzes parallel FDTD method and CUDA architecture, presents […]
Apr, 25

MultiGPU computing using MPI or OpenMP

The GPU computing follows the trend of GPGPU, driven by the innovations in both hardware and programming languages made available to nongraphic programmers. Since some problems require an important time to solve or data quantities that do not fit on one single GPU, the logical continuation was to make use of multiple GPUs. In order […]
Apr, 25

A real time Breast Microwave Radar imaging reconstruction technique using simt based interpolation

Breast Microwave Radar(BMR) is a novel imaging modality that is capable of producing high contrast images and can detect tumors of at least 4mm. To properly visualize the responses from the breast structures, BMR data sets must be reconstructed. In this paper, a real time BMR image formation technique is proposed. This approach is based […]
Apr, 25

Improving numerical reproducibility and stability in large-scale numerical simulations on GPUs

The advent of general purpose graphics processing units (GPGPU’s) brings about a whole new platform for running numerically intensive applications at high speeds. Their multi-core architectures enable large degrees of parallelism via a massively multi-threaded environment. Molecular dynamics (MD) simulations are particularly well-suited for GPU’s because their computations are easily parallelizable. Significant performance improvements are […]
Apr, 25

Parallelizing Motion JPEG 2000 with CUDA

Due to the rapid growth of graphics processing unit (GPU) processing capability, using GPU as a coprocessor for assisting the CPU in computing massive data has become indispensable. Nvidia’s CUDA general-purpose graphical processing unit (GPGPU) architecture can greatly benefit single instruction multiple thread (SIMT) styled, computationally expensive programs. Video encoding, to an extent, is an […]
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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
  • GPU device 0: AMD/ATI Radeon HD 5870 2GB, 850MHz
  • 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
  • GPU device 0: AMD/ATI Radeon HD 7970 3GB, 1000MHz
  • 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

Completed OpenCL project should be uploaded via User dashboard (see instructions and example there), compilation and execution terminal output logs will be provided to the user.

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