Feb, 17

OpenCL Evaluation for Numerical Linear Algebra Library Development

With the help of of CUDA [7], [6], many applications improved their performance by using GPUs. In our project called Matrix Algebra on GPU and Multicore Architectures (MAGMA) [10], we mainly focus on dense linear algebra routines similar to those from LAPACK [1]. Other than CUDA, there exist other frameworks that allow platformindependent programming for […]
Feb, 17

Evaluating one-sided programming models for GPU cluster computations

The Global Array toolkit (GA) [1] is a powerful framework for implementing algorithms with irregular communication patterns, such as those of quantum chemistry. On the other hand, accelerators such as GPUs have shown great potential for important kernels in quantum chemistry, for example, atomic integral generation [2] and dense linear algebra in correlated methods [3]. […]
Feb, 17

GPU Accelerated Particle System for Triangulated Surface Meshes

Shape analysis based on images and implicit surfaces has been an active area of research for the past several years. Particle systems have emerged as a viable solution to represent shapes for statistical analysis. One of the most widely used representations of shapes in computer graphics and visualization is the triangular mesh. It is desirable […]
Feb, 17

Medium-Grained Functions Mapping using Modern GPUs

The map is a higher-order function that applies a given function to the list or lists of elements producing the list of results. The mapped function is applied to each element of the list independently, thus can be performed for all elements in parallel, making the GPU an interesting platform to be implemented on. Although […]
Feb, 17

Simulations of Large Membrane Regions using GPU-enabled Computations – Preliminary Results

In this short paper we present a GPU code for MD simulations of large membrane regions in the NVT and NVE ensembles with explicit solvent. We give an overview of the code and present preliminary performance results.
Feb, 17

Dynamically scheduled Cholesky factorization on multicore architectures with GPU accelerators

Although the hardware has dramatically changed in the last few years, nodes of multicore chips augmented by Graphics Processing Units (GPUs) seem to be a trend of major importance. Previous approaches for scheduling dense linear operations on such a complex node led to high performance but at the double cost of not using the potential […]
Feb, 17

A Strategy for Automatically Generating High Performance CUDA Code for a GPU Accelerator from a Specialized Fortran Code Expression

Recent microprocessor designs concentrate upon adding cores rather than increasing clock speeds in order to achieve enhanced performance. As a result, in the last few years computational accelerators featuring many cores per chip have begun to appear in high performance scientific computing systems. The IBM Cell processor, with its 9 heterogeneous cores, was the first […]
Feb, 17

Accelerating Algorithms on GPUs in SCIRun: the Conjugate Gradient Case Study

The goal of this research is to integrate graphics processing units (GPUs) into SCIRun, a biomedical problem solving environment, in a way that is transparent to the scientist. We have developed a portable mechanism that allows seamless coexistence of CPU and accelerated GPU computations to provide the best performance while also providing ease of use. […]
Feb, 17

Takagi Factorization on GPU using CUDA

Takagi factorization or symmetric singular value decomposition is a special form of SVD applicable to symmetric complex matrices. The computation takes advantage of symmetry to reduce computation and storage requirements. The Jacobi method with chess tournament ordering was used to perform the computation in parallel on a GPU using the CUDA programming model. We were […]
Feb, 17

Automatically Tuned Dense Linear Algebra for Multicore+GPU

The Multicore+GPU architecture has been adopted in some of the fastest supercomputers listed on the TOP500. The MAGMA project aims to develop a dense linear algebra library similar to LAPACK but for heterogeneous/hybrid architectures processors like Multicore+GPU. However, to provide portable performance, manual parameter tuning is required. This paper presents automatically tuned LU factorization. The […]
Feb, 16

GpuC: Data parallel language extension to CUDA

In recent years, Graphics Processing Units (GPUs) have emerged as a powerful accelerator for general-purpose computations. Current approaches to program GPUs are still relatively low-level programming models such as Compute Unified Device Architecture (CUDA), a programming model from NVIDIA, and Open Compute Language (OpenCL), created by Apple in cooperation with others. These two programming models […]
Feb, 16

Enhancing the simulation of P systems for the SAT problem on GPUs

GPUs constitute nowadays a solid alternative for high performance computing, and the advent of CUDA/OpenCL allow programmers a friendly model to accelerate a broad range of applications. The way GPUs exploit parallelism differ from multi-core CPUs, which raises new challenges to take advantage of its tremendous computing power. In this respect, P systems or Membrane […]
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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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