Jan, 2

Performance comparison of Lattice Boltzmann fluid flow simulation using OpenCL and CUDA frameworks

This paper presents performance comparison, of the lid-driven cavity flow simulation, with Lattice Boltzmann method, example, between CUDA and OpenCL parallel programming frameworks. CUDA is parallel programming model developed by NVIDIA for leveraging computing capabilities of their products. OpenCL is an open, royalty free, standard developed by Khronos group for parallel programming of heterogeneous devices […]
Jan, 2

GPU-based acceleration of free energy calculations in solid state physics

Obtaining a thermodynamically accurate phase diagram through numerical calculations is a computationally expensive problem that is crucially important to understanding the complex phenomena of solid state physics, such as superconductivity. In this work we show how this type of analysis can be significantly accelerated through the use of modern GPUs. We illustrate this with a […]
Jan, 2

Disjunctive Normal Networks

Artificial neural networks are powerful pattern classifiers; however, they have been surpassed in accuracy by methods such as support vector machines and random forests that are also easier to use and faster to train. Backpropagation, which is used to train artificial neural networks, suffers from the herd effect problem which leads to long training times […]
Dec, 30

Characterization of OpenCL on a Scalable FPGA Architecture

The recent release of Altera’s SDK for OpenCL has greatly eased the development of FPGA-based systems. Research have shown performance improvements brought by OpenCL using a single FPGA device. However, to meet the objectives of high performance computing, OpenCL needs to be evaluated using multiple FPGAs. This work has proposed a scalable FPGA architecture for […]
Dec, 30

Extending OmpSs to support CUDA and OpenCL in C, C++ and Fortran Applications

CUDA and OpenCL are the most widely used programming models to exploit hardware accelerators. Both programming models provide a C-based programming language to write accelerator kernels and a host API used to glue the host and kernel parts. Although this model is a clear improvement over a low-level and ad-hoc programming model for each hardware […]
Dec, 30

A Tool for Automatic Suggestions for Irregular GPU Kernel Optimization

Future computing systems, from handhelds all the way to supercomputers, will be more parallel and more heterogeneous than today’s systems to provide more performance without an increase in power consumption. Therefore, GPUs are increasingly being used to accelerate general-purpose applications, including applications with data-dependent, irregular memory access patterns and control flow. The growing complexity, non-uniformity, […]
Dec, 30

Spectral classification using convolutional neural networks

There is a great need for accurate and autonomous spectral classification methods in astrophysics. This thesis is about training a convolutional neural network (ConvNet) to recognize an object class (quasar, star or galaxy) from one-dimension spectra only. Author developed several scripts and C programs for datasets preparation, preprocessing and post-processing of the data. EBLearn library […]
Dec, 30

How to Correctly Deal With Pseudorandom Numbers in Manycore Environments – Application to GPU programming with Shoverand

Stochastic simulations are often sensitive to the source of randomness that characterizes the statistical quality of their results. Consequently, we need highly reliable Random Number Generators (RNGs) to feed such applications. Recent developments try to shrink the computation time by relying more and more General Purpose Graphics Processing Units (GP-GPUs) to speed-up stochastic simulations. Such […]
Dec, 30

To Use or Not to Use: Graphics Processing Units for Pattern Matching Algorithms

String matching is an important part in today’s computer applications and Aho-Corasick algorithm is one of the main string matching algorithms used to accomplish this. This paper discusses that when can the GPUs be used for string matching applications using the Aho-Corasick algorithm as a benchmark. We have to identify the best unit to run […]
Dec, 26

Automatic Tuning of Local Memory Use on GPGPUs

The use of local memory is important to improve the performance of OpenCL programs. However, its use may not always benefit performance, depending on various application characteristics, and there is no simple heuristic for deciding when to use it. We develop a machine learning model to decide if the optimization is beneficial or not. We […]
Dec, 26

Accelerating Correlation Power Analysis Using Graphics Processing Units

Correlation Power Analysis (CPA) is a type of power analysis based side channel attack that can be used to derive the secret key of encryption algorithms including DES (Data Encryption Standard) and AES (Advanced Encryption Standard). A typical CPA attack on unprotected AES is performed by analysing a few thousand power traces that requires about […]
Dec, 26

Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs

Deep Convolutional Neural Networks (DCNNs) have recently shown state of the art performance in high level vision tasks, such as image classification and object detection. This work brings together methods from DCNNs and probabilistic graphical models for addressing the task of pixel-level classification (also called "semantic image segmentation"). We show that responses at the final […]
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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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