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Posts

Jul, 24

Massively Deep Artificial Neural Networks for Handwritten Digit Recognition

Greedy Restrictive Boltzmann Machines yield an fairly low 0.72% error rate on the famous MNIST database of handwritten digits. All that was required to achieve this result was a high number of hidden layers consisting of many neurons, and a graphics card to greatly speed up the rate of learning.
Jul, 22

On the use of deep Boltzmann machines for road signs classification

The Deep Boltzmann Machine (DBM) has been proved to be one of the most effective machine learning generative models in discriminative tasks. They’ve been able to overcome other generative, and even discriminative models, on relatively simple tasks, such as digits recognition, and also on more complex tasks such as simple objects recognition. However, there’re only […]
Jul, 22

Boosting GPU Virtualization Performance with Hybrid Shadow Page Tables

The increasing adoption of Graphic Process Unit (GPU) to computation-intensive workloads has stimulated a new computing paradigm called GPU cloud (e.g., Amazon’s GPU Cloud), which necessitates the sharing of GPU resources to multiple tenants in a cloud. However, state-of-the-art GPU virtualization techniques such as gVirt still suffer from non-trivial performance overhead for graphics memory-intensive workloads […]
Jul, 22

Raspberry Pi based System for Visual Object Detection and Tracking

The aim of this thesis is to explore different methods for helping computers interpret the real world visually, investigate solutions to those methods offered by the open-sourced computer vision library, OpenCV, and implement some of these in a Raspberry Pi based application for detecting and keeping track of objects. The main focus rests on the […]
Jul, 22

An algebraic parallel treecode in arbitrary dimensions

We present a parallel treecode for fast kernel summation in high dimensions – a common problem in data analysis and computational statistics. Fast kernel summations can be viewed as approximation schemes for dense kernel matrices. Treecode algorithms (or simply treecodes) construct low-rank approximations of certain off-diagonal blocks of the kernel matrix. These blocks are identified […]
Jul, 22

Generating Binary Optimal Codes Using Heterogeneous Parallel Computing

Generation of optimal codes is a well known problem in coding theory. Many computational approaches exist in the literature for finding record breaking codes. However generating codes with long lengths n using serial algorithms is computationally very expensive, for example the worst case time complexity of a Greedy algorithm is O(n4^n). In order to improve […]
Jul, 20

GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers

GROMACS is one of the most widely used open-source and free software codes in chemistry, used primarily for dynamical simulations of biomolecules. It provides a rich set of calculation types, preparation and analysis tools. Several advanced techniques for free-energy calculations are supported. In version 5, it reaches new performance heights, through several new and enhanced […]
Jul, 20

Performance Analysis of GPU-Accelerated Filter-Based Source Finding for HI Spectral Line Image Data

Searching for sources of electromagnetic emission in spectral-line radio astronomy interferometric data is a computationally intensive process. Parallel programming techniques and High Performance Computing hardware may be used to improve the computational performance of a source finding program. However, it is desirable to further reduce the processing time of source finding in order to decrease […]
Jul, 20

Accelerating a Movie Recommender System Using VirtualCL on a Heterogeneous GPU Cluster

Present day market offers a large number of movies which overwhelm people with choices. In order to quickly navigate through all the possible movies and find the interesting ones, the user can take advantage of recommender systems for movies. This thesis studies a movie recommender system which uses image processing and computer vision algorithms. The […]
Jul, 20

Parallel Programming in Actor-Based Applications via OpenCL

GPU and multicore hardware architectures are commonly used in many different application areas to accelerate problem solutions relative to single CPU architectures. The typical approach to accessing these hardware architectures requires embedding logic into the programming language used to construct the application; the two primary forms of embedding are: calls to API routines to access […]
Jul, 20

Improving performance portability for GPU-specific OpenCL kernels on multi-core/many-core CPUs by analysis-based transformations

OpenCL is an open heterogeneous programming framework. Although OpenCL programs are functionally portable, it does not provide performance portability, so code transformation often plays an irreplaceable role. When adapting GPU-specific OpenCL kernels to run on multi-core/many-core CPUs, coarsening the thread granularity is necessary and thus extensively used. However, locality concerns exposed in GPU-specific OpenCL code […]
Jul, 17

GPU-based visualization of domain-coloured algebraic Riemann surfaces

We examine an algorithm for the visualization of domain-coloured Riemann surfaces of plane algebraic curves. The approach faithfully reproduces the topology of the surface and also preserves some of its geometry. We discuss how the algorithm can be implemented efficiently in OpenGL with geometry shaders, and (less efficiently) even in WebGL with multiple render targets […]
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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: nVidia GeForce GTX 560 Ti 2GB, 822MHz
  • 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: nVidia CUDA Toolkit 6.5.14, AMD APP SDK 3.0
Node 2
  • GPU device 0: AMD/ATI Radeon HD 7970 3GB, 1000MHz
  • GPU device 1: AMD/ATI Radeon HD 5870 2GB, 850MHz
  • CPU: Intel Core i7-2600 @ 3.4GHz
  • RAM: 16GB
  • OS: OpenSUSE 12.3
  • SDK: AMD APP SDK 3.0

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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