Jul, 3

International Conference on Frontiers of Signal Processing (ICFSP), 2015

Topics: Adaptive Filtering & Signal Processing Ad-Hoc and Sensor Networks Analog and Mixed Signal Processing Array Signal Processing Audio and Electroacoustics Audio/Speech Processing and Coding Bioimaging and Signal Processing Biometrics & Authentification Biosignal Processing & Understanding Communication and Broadband Networks Communication Signal processing Computer Vision & Virtual Reality Cryptography and Network Security Design and Implementation […]
Jul, 1

Buffer overflow vulnerabilities in CUDA: a preliminary analysis

We present a preliminary study of buffer overflow vulnerabilities in CUDA software running on GPUs. We show how an attacker can overrun a buffer to corrupt sensitive data or steer the execution flow by overwriting function pointers, e.g., manipulating the virtual table of a C++ object. In view of a potential mass market diffusion of […]
Jul, 1

Spectral Ewald Acceleration of Stokesian Dynamics for polydisperse suspensions

In this work we develop the Spectral Ewald Accelerated Stokesian Dynamics (SEASD), a novel computational method for dynamic simulations of polydisperse colloidal suspensions with full hydrodynamic interactions. SEASD is based on the framework of Stokesian Dynamics (SD) with extension to compressible solvents, and uses the Spectral Ewald (SE) method [Lindbo & Tornberg, J. Comput. Phys. […]
Jul, 1

Heterogeneous Network Embedding via Deep Architectures

Data embedding is used in many machine learning applications to create low-dimensional feature representations, which preserves the structure of data points in their original space. In this paper, we examine the scenario of a heterogeneous network with nodes and content of various types. Such networks are notoriously difficult to mine because of the bewildering combination […]
Jul, 1

Accelerating DNA Sequence Analysis using Intel Xeon Phi

Genetic information is increasing exponentially, doubling every 18 months. Analyzing this information within a reasonable amount of time requires parallel computing resources. While considerable research has addressed DNA analysis using GPUs, so far not much attention has been paid to the Intel Xeon Phi coprocessor. In this paper we present an algorithm for large-scale DNA […]
Jul, 1

The Potential of the Intel Xeon Phi for Supervised Deep Learning

Supervised learning of Convolutional Neural Networks (CNNs), also known as supervised Deep Learning, is a computationally demanding process. To find the most suitable parameters of a network for a given application, numerous training sessions are required. Therefore, reducing the training time per session is essential to fully utilize CNNs in practice. While numerous research groups […]
Jul, 1

5th International Conference on Information Communication and Management (ICICM), 2015

Topics: Information Engineering Communication Artificial Intelligence Signal Detection and Parameter Estimation Bioinformatics Signal, Image and Video Processing Software Engineering Speech and Audio Processing VLSI Design and Fabrication Wireless Communications Photonic Technologies Communications Transmission Parallel and Distributed Computing Network Communication Data Mining Mobile and Ubiquitous Computing Cryptography Ad hoc and Sensor Networks Algorithms and Data Structures […]
Jul, 1

International Conference on Information and Computer Technology (ICICT), 2015

Topics: Algorithms Artificial Intelligence Automated Software Engineering Bio-informatics Bioinformatics and Scientific Computing Biomedical Engineering Compilers and Interpreters Computational Intelligence Computer Animation Computer Architecture & VLSI Computer Architecture and Embedded Systems Computer Based Education Computer Games Computer Graphics & Virtual Reality Computer Graphics and Multimedia Computer Modeling Computer Networks Computer Networks and Data Communication Computer Security […]
Jul, 1

8th International Conference on Computer and Electrical Engineering (ICCEE), 2015

Topics: Computer Engineering Electrical Engineering Algorithm Advanced Power Semiconductors Computer Vision, Graphics and Intelligence Analogue and Digital Signal Processing Computational and Artificial Intelligence Biomedical Engineering Computer Vision Computer and AI Applications in Power Industry Computer Networks Control Science and Control Engineering Pattern Analysis and Recognition Distributed Generation, Fuel Cells and Renewable Energy Systems Computer Graphics […]
Jun, 30

AccFFT: A library for distributed-memory FFT on CPU and GPU architectures

We present a new library for parallel distributed Fast Fourier Transforms (FFT). Despite the large amount of work on FFTs, we show that significant speedups can be achieved for distributed transforms. The importance of FFT in science and engineering and the advances in high performance computing necessitate further improvements. AccFFT extends existing FFT libraries for […]
Jun, 30

CPU and GPU Implementation of QCD by using OpenCL

Recently, many particle physics applications can be parallelized by using multicore platforms such as CPU and GPU. In this paper, we propose a parallel processing approach for Quantum ChromoDynamics(QCD) application by using both CPU and GPU. Instead of distributing the parallelizable workload to either CPU or GPU, we distribute the workload simultaneously into both CPU […]
Jun, 30

Intra-Application Data-Communication Characterization

The growing demand of processing power is being satisfied mainly by an increase in the number of computing cores in a system. One of the main challenges to be addressed is efficient utilization of these architectures. This demands data-communication aware mapping of applications on these architectures. Appropriate tools are required to provide the detailed intra-application […]
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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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