Jul, 20

Water simulation for cell based sandbox games

This thesis work presents a new algorithm for simulating fluid based on the Navier-Stokes equations. The algorithm is designed for cell based sandbox games where interactivity and performance are the main priorities. The algorithm enforces mass conservation conservatively instead of enforcing a divergence free velocity field. A global scale pressure model that simulates hydrostatic pressure […]
Jul, 18

Unsupervised Asset Cluster Analysis Implemented with Parallel Genetic Algorithms on the NVIDIA CUDA Platform

During times of stock market turbulence and crises, monitoring the clustering behaviour of financial instruments allows one to better understand the behaviour of the stock market and the associated systemic risks. In the study undertaken, I apply an effective and performant approach to classify data clusters in order to better understand correlations between stocks. The […]
Jul, 18

Implementation & Parallelisation of FDTD code for Electromagnetic Scattering

The present report deals with the application of the algorithm for computation of electromagnetic field components using FDTD method developed by Kane Yee, to Cartesian meshes using total field formulation. For this purpose, code has been written for electromagnetic scattering computation in C language. For generation of code, some snippets from [1] have been used. […]
Jul, 18

GPU acceleration of Runge Kutta-Fehlberg and its comparison with Dormand-Prince method

There is a significant reduction of processing time and speedup of performance in computer graphics with the emergence of Graphic Processing Units (GPUs). GPUs have been developed to surpass Central Processing Unit (CPU) in terms of performance and processing speed. This evolution has opened up a new area in computing and researches where highly parallel […]
Jul, 18

Efficient On-the-fly Category Retrieval using ConvNets and GPUs

We investigate the gains in precision and speed, that can be obtained by using Convolutional Networks (ConvNets) for on-the-fly retrieval – where classifiers are learnt at run time for a textual query from downloaded images, and used to rank large image or video datasets. We make three contributions: (i) we present an evaluation of state-of-the-art […]
Jul, 18

Suitability of NVIDIA GPUs for SKA1-Low

In this memo we investigate the applicability of NVIDIA Graphics Processing Units (GPUs) for SKA1-Low station and Central Signal Processing (CSP)-level processing. Station-level processing primarily involves generating a single station beam which will then be correlated with other beams in CSP. Fine channelisation can be performed either at the station of CSP-level, while coarse channelisation […]
Jul, 17

Efficient implementation of computationally intensive algorithms on parallel computing platforms

Two different types of computationally intensive problems have been researched to investigate the design methodology of the acceleration and to give a high-performance implementation on parallel architectures. Each problem was accelerated via a different architecture, and the results of the investigation were summarized in different thesis groups. The design methodology proposed in Thesis 1 can […]
Jul, 17

Rapid Modelling of Interactive Geological Illustrations with Faults and Compaction

In this paper, we propose new methods for building geological illustrations and animations. We focus on allowing geologists to create their subsurface models by means of sketches, to quickly communicate concepts and ideas rather than detailed information. The result of our sketch-based modelling approach is a layer-cake volume representing geological phenomena, where each layer is […]
Jul, 17

The Use of GPUs for Solving the Computed Tomography Problem

Computed tomography (CT) is a widespread method used to study the internal structure of objects. The method has applications in medicine, industry and other fields of human activity. In particular, Electronic Imaging, as a species CT, can be used to restore the structure of nanosized objects. Accurate and rapid results are in high demand in […]
Jul, 17

Energy-Efficient Collective Reduce and Allreduce Operations on Distributed GPUs

GPUs gain high popularity in High Performance Computing, due to their massive parallelism and high performance per Watt. Despite their popularity, data transfer between multiple GPUs in a cluster remains a problem. Most communication models require the CPU to control the data flow; also intermediate staging copies to host memory are often inevitable. These two […]
Jul, 17

Optimal Periods for Probing Convergence of Infinite-stage Dynamic Programmings on GPUs

In this paper, we propose a basic technique to minimize the computational time in executing the infinite-stage dynamic programming (DP) on a GPU. The infinite-stage DP involves computations to probe whether a value function gets sufficiently close to the optimal one. Such computations for probing convergence become obvious when an infinite-stage DP is executed on […]
Jul, 16

GPUdrive: Reconsidering Storage Accesses for GPU Acceleration

GPU-accelerated data-intensive applications demonstrate in excess of ten-fold speedups over CPU-only approaches. However, file-driven data movement between the CPU and the GPU can degrade performance and energy efficiencies by an order of magnitude as a result of traditional storage latency and ineffectual memory management. In this paper, we first analyze these two critical performance bottlenecks […]
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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

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