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Posts

Jul, 28

Simulating spiking neural networks on massively parallel graphical processing units using a code generation approach with GeNN

A major challenge in computational neuroscience is to achieve high performance for real-time simulations of full size brain networks. Recent advances in GPU technology provide massively parallel, low-cost and efficient hardware that is widely available on the computer market. However, the comparatively low-level programming that is necessary to create an efficient GPU-compatible implementation of neuronal […]
Jul, 28

Improved Finite Difference Schemes for a 3-D Viscothermal Wave Equation on a GPU

Viscothermal effects in air lead to a damping of high frequencies over time. Such effects cannot be neglected in large-scale room acoustics simulations for the full audible bandwidth. In this study, full-bandwidth room acoustics is modelled using a variant of the three-dimensional wave equation including viscothermal losses in air following from a simplification of the […]
Jul, 28

CUDT: A CUDA Based Decision Tree Algorithm

Decision tree is one of the famous classification methods in data mining. Many researches have been proposed, which were focusing on improving the performance of decision tree. However, those algorithms are developed and run on traditional distributed systems. Obviously the latency could not be improved while processing huge data generated by ubiquitous sensing node in […]
Jul, 26

Towards fast and certified multiple-precision libraries

Many numerical problems require higher precision than the conventional floating-point (single, double) formats. One solution is to use multiple precision libraries such as GNU MPFR, which allow the manipulation of very high precision numbers. But their generality (they are able to handle numbers with millions of digits), is a quite heavy alternative when medium precision […]
Jul, 26

Parallel Computations for Hierarchical Agglomerative Clustering using CUDA

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 […]
Jul, 26

Performance Efficient DNA Sequence Detection on GPU Using Parallel Pattern Matching Approach

Bioinformatics is the field of science which applies computer science and information technology to the problems of biological science. One of the most useful applications of bioinformatics is sequence analysis. Sequence analysis, which is the process of subjecting a DNA, RNA to any wide range of analytical approaches, involves methodologies like sequence alignment and searches […]
Jul, 26

ReGen: Optimizing Genetic Selection Algorithms for Heterogeneous Computing

GenSel is a genetic selection analysis tool used to determine which genetic markers are informational for a given trait. Performing genetic selection related analyses is a time consuming and computationally expensive task. Due to an expected increase in the number of genotyped individuals, analysis times will increase dramatically. Therefore, optimization efforts must be made to […]
Jul, 26

Parallel solutions of static Hamilton-Jacobi equations for simulations of geological folds

Two new algorithms for numerical solution of static Hamilton-Jacobi equations are presented. These algorithms are designed to work efficiently on different parallel computing architectures, and numerical results for multicore CPU and GPU implementations are reported and discussed. The numerical experiments show that the proposed solution strategies scale well with the computational power of the hardware. […]
Jul, 24

ADHA: Automatic Data layout framework for Heterogeneous Architectures

Data layouts play a crucial role in determining the performance of a given application running on a given architecture. Existing parallel programming frameworks for both multicore and heterogeneous systems leave the onus of selecting a data layout to the programmer. Therefore, shifting the burden of data layout selection to optimizing compilers can greatly enhance programmer […]
Jul, 24

Multi-Core Programming Design Patterns: Stream Processing Algorithms for Dynamic Scene Perceptions

We have implemented, tested, validated and benchmarked a scalable parallel implementations of the integral histogram algorithm critical for computer vision tasks for fast multiscale subwindow-based object searching, motion analysis and content-based image retrieval applications. Several integral histogram kernels using CUDA optimizations for many core GPUs were investigated. The integral histogram algorithm was also parallelized using […]
Jul, 24

Abstraction and Implementation of Unstructured Grid Algorithms on Massively Parallel Heterogeneous Architectures

The last decade saw the long tradition of frequency scaling of processing units grind to a halt, and efforts were re-focused on maintaining computational growth by other means; such as increased parallelism, deep memory hierarchies and complex execution logic. After a long period of "boring productivity", a host of new architectures, accelerators, programming languages and […]
Jul, 24

Electromagnetic Computation and Visualization of Transmission Particle Model and its Simulation Based on GPU

Electromagnetic calculation plays an important role in both military and civic fields. Some methods and models proposed for calculation of electromagnetic wave propagation in a large range, bring heavy burden in CPU computation, and also require huge amount of memory. Using the GPU to accelerate computation and visualization can reduce the computational burden on the […]
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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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