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Posts

Mar, 6

PONDER – A Real time software backend for pulsar and IPS observations at the Ooty Radio Telescope

This paper describes a new real-time versatile backend, the Pulsar Ooty Radio Telescope New Digital Efficient Receiver (PONDER), which has been designed to operate along with the legacy analog system of the Ooty Radio Telescope (ORT). PONDER makes use of the current state of the art computing hardware, a Graphical Processing Unit (GPU) and sufficiently […]
Mar, 6

Multi-GPU implementation of a VMAT treatment plan optimization algorithm

VMAT optimization is a computationally challenging problem due to its large data size, high degrees of freedom, and many hardware constraints. High-performance graphics processing units have been used to speed up the computations. However, its small memory size cannot handle cases with a large dose-deposition coefficient (DDC) matrix. This paper is to report an implementation […]
Mar, 6

An OpenCL-based Monte Carlo dose calculation engine (oclMC) for coupled photon-electron transport

Monte Carlo (MC) method has been recognized the most accurate dose calculation method for radiotherapy. However, its extremely long computation time impedes clinical applications. Recently, a lot of efforts have been made to realize fast MC dose calculation on GPUs. Nonetheless, most of the GPU-based MC dose engines were developed in NVidia CUDA environment. This […]
Mar, 3

An efficient solution for hazardous geophysical flows simulation using GPUs

The movement of poorly sorted material over steep areas constitutes a hazardous environmental problem. Computational tools help in the understanding and predictions of such landslides. The main drawback is the high computational effort required for obtaining accurate numerical solutions due to the high number of cells involved in the calculus. In order to overcome this […]
Mar, 3

Energy-and cost-efficient Lattice-QCD computations using graphics processing units

Quarks and gluons are the building blocks of all hadronic matter, like protons and neutrons. Their interaction is described by Quantum Chromodynamics (QCD), a theory under test by large scale experiments like the Large Hadron Collider (LHC) at CERN and in the future at the Facility for Antiproton and Ion Research (FAIR) at GSI. However, […]
Mar, 3

Adaptive Video Encoding Based on OpenCL Face Recognition

Video chatting is now a popular way of communication. However, poor network ruins the experience as the faces are blurred. To solve this problem, the team offers a solution to preserve the clarity of faces under limited transmission rate. In this project, the primary goal is to design a video encoder that reduces the size […]
Mar, 3

Adaptive Kinetic-Fluid Solvers for Heterogeneous Computing Architectures

This paper describes recent progress towards porting a Unified Flow Solver (UFS) to heterogeneous parallel computing. UFS is an adaptive kinetic-fluid simulation tool, which combines Adaptive Mesh Refinement (AMR) with automatic cell-by-cell selection of kinetic or fluid solvers based on continuum breakdown criteria. The main challenge of porting UFS to graphics processing units (GPUs) comes […]
Mar, 3

Counting Triangles in Large Graphs on GPU

The clustering coefficient and the transitivity ratio are concepts often used in network analysis, which creates a need for fast practical algorithms for counting triangles in large graphs. Previous research in this area focused on sequential algorithms, MapReduce parallelization, and fast approximations. In this paper we propose a parallel triangle counting algorithm for CUDA GPU. […]
Mar, 3

GPU Based Path Integral Control with Learned Dynamics

We present an algorithm which combines recent advances in model based path integral control with machine learning approaches to learning forward dynamics models. We take advantage of the parallel computing power of a GPU to quickly take a massive number of samples from a learned probabilistic dynamics model, which we use to approximate the path […]
Mar, 2

Iris Matching Algorithm on Many-Core Platforms

Biometrics matching has been widely adopted as a secure way for identification and verification purpose. However, the computation demand associated with running this algorithm on a big data set poses great challenge on the underlying hardware platform. Even though modern processors are equipped with more cores and memory capacity, the software algorithm still requires careful […]
Mar, 2

Model-driven optimisation of memory hierarchy and multithreading on GPUs

Due to their potentially high peak performance and energy efficiency, GPUs are increasingly popular for scientific computations. However, the complexity of the architecture makes it difficult to write code that achieves high performance. Two of the most important factors in achieving high performance are the usage of the GPU memory hierarchy and the way in […]
Mar, 2

Runtime Compilation of Array-Oriented Python Programs

The Python programming language has become a popular platform for data analysis and scientific computing. To mitigate the poor performance of Python’s standard interpreter, numerically intensive computations are typically offloaded to library functions written in high-performance compiled languages such as Fortran or C. When there is no efficient library implementation available for a particular algorithm, […]
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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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