12463

Posts

Jul, 10

Random Fields Generation on the GPU with the Spectral Turning Bands Method

Random Field (RF) generation algorithms are of paramount importance for many scientific domains, such as astrophysics, geostatistics, computer graphics and many others. Some examples are the generation of initial conditions for cosmological simulations or hydrodynamical turbulence driving. In the latter a new random field is needed every time-step. Current approaches commonly make use of 3D […]
Jul, 10

Understanding the SIMD Efficiency of Graph Traversal on GPU

Graph is a widely used data structure and graph algorithms, such as breadth-first search (BFS), are regarded as key components in a great number of applications. Recent studies have attempted to accelerate graph algorithms on highly parallel graphics processing unit (GPU). Although many graph algorithms based on large graphs exhibit abundant parallelism, their performance on […]
Jul, 9

Utilizing state-of-art NeuroES and GPGPU to optimize Mario AI

CONTEXT: Reinforcement Learning (RL) is a time consuming effort that requires a lot of computational power as well. There are mainly two approaches to improving RL efficiency, the theoretical mathematics and algorithmic approach or the practical implementation approach. In this study, the approaches are combined in an attempt to reduce time consumption. OBJECTIVES: We investigate […]
Jul, 9

Parallelization of Multipattern Matching on GPU

Pattern matching is a highly computationally intensive operation used in SNORT system but due to the increasingly storage capacity and the link speed the amount of data that need to be match against pattern is increased rapidly and traditional system is fail to match that data. GPU Computing Have attracted lots of attention due to […]
Jul, 9

Tile Based Procedural Terrain Generation in Real-Time

CONTEXT: Procedural Terrain Generation refers to the algorithmical creation of terrains with limited or no user input. Terrains are an important piece of content in many video games and other forms of simulations. OBJECTIVES: In this study a tile-based approach to creating endless terrains is investigated. The aim is to find if real-time performance is […]
Jul, 9

Using GPU for query of email spam detection systems and IDS

The scope of this research paper is one very important aspects nowadays, the security and management of one of the most important services the email and all of the alike online services today. This paper attempts to investigate the possible benefits of using standard signature-driven spam detection logic in combination with algorithm for network intrusion […]
Jul, 9

Analysis of RSA algorithm using GPU programming

Modern-day computer security relies heavily on cryptography as a means to protect the data that we have become increasingly reliant on. The main research in computer security domain is how to enhance the speed of RSA algorithm. The computing capability of Graphic Processing Unit as a co-processor of the CPU can leverage massive-parallelism. This paper […]
Jul, 7

GiMMiK – Generating Bespoke Matrix Multiplication Kernels for Various Hardware Accelerators; Applications in High-Order Computational Fluid Dynamics

Matrix multiplication is a fundamental linear algebra routine ubiquitous in all areas of science and engineering. Highly optimised BLAS libraries (cuBLAS and clBLAS on GPUs) are the most popular choices for an implementation of the General Matrix Multiply (GEMM) in software. However, performance of library GEMM is poor for small matrix sizes. In this thesis […]
Jul, 7

Solving the Examination Timetabling Problem in GPUs

The examination timetabling problem belongs to the class of combinatorial optimization problems and is of great importance for every University. In this paper, a hybrid evolutionary algorithm running on a GPU is employed to solve the examination timetabling problem. The hybrid evolutionary algorithm proposed has a genetic algorithm component and a greedy steepest descent component. […]
Jul, 7

Analysis of Surface Folding Patterns of DICCCOLS Using the GPU-Optimized Geodesic Field Estimate

Localization of cortical regions of interests (ROIs) in the human brain via analysis of Diffusion Tensor Imaging (DTI) data plays a pivotal role in basic and clinical neuroscience. In recent studies, 358 common cortical landmarks in the human brain, termed as Dense Individualized and Common Connectivity-based Cortical Landmarks (DICCCOLs), have been identified. Each of these […]
Jul, 7

Parallelization of the Symmetric Indefinite Factorization

Parallel computing is a topic that became very popular in the last few decades. Parallel computers are being used in many different areas of science such as astrophysics, climate modelling, quantum chemistry, fluid dynamics and medicine. Parallel programming is a type of programming where computations can be performed concurrently on different processors or devices. There […]
Jul, 7

Fast and Efficient Lossless Image Compression Based on CUDA Parallel Wavelet Tree Encoding

Lossless compression is still in high demand in medical image applications despite improvements in the computing capability and decrease in storage cost in recent years. With the development of General Purpose Graphic Processing Unit (GPGPU) computing techniques, sequential lossless image compression algorithms can be modified to achieve more efficiency and speed. Backward Coding of Wavelet […]
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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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