Adam Polak
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. […]
Ken Miura, Tetsuaki Mano, Atsushi Kanehira, Yuichiro Tsuchiya, Tatsuya Harada
MILJS is a collection of state-of-the-art, platform-independent, scalable, fast JavaScript libraries for matrix calculation and machine learning. Our core library offering a matrix calculation is called Sushi, which exhibits far better performance than any other leading machine learning libraries written in JavaScript. Especially, our matrix multiplication is 177 times faster than the fastest JavaScript benchmark. […]
Dzmitry Razmyslovich, Guillermo Marcus, Markus Gipp, Marc Zapatka, Andreas Szillus
In this paper we present an implementation of the Smith-Waterman algorithm. The implementation is done in OpenCL and targets high-end GPUs. This implementation is capable of computing similarity indexes between reference and query sequences. The implementation is designed for the sequence alignment paths calculation. In addition, it is capable of handling very long reference sequences […]
Sergey Voronin, Per-Gunnar Martinsson
This document describes an implementation in C of a set of randomized algorithms for computing partial Singular Value Decompositions (SVDs). The techniques largely follow the prescriptions in the article "Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions," N. Halko, P.G. Martinsson, J. Tropp, SIAM Review, 53(2), 2011, pp. 217-288, but with some […]
Sparsh Mittal, Matt Poremba, Jeffrey Vetter, Yuan Xie
To enable the design of large sized caches, novel memory technologies (such as non-volatile memory) and novel fabrication approaches (e.g. 3D stacking) have been explored. The existing modeling tools, however, cover only few memory technologies, CMOS technology nodes and fabrication approaches. We present DESTINY, a tool for modeling 3D (and 2D) cache designs using SRAM, […]
Roman Iakymchuk, David Defour, Sylvain Collange, Stef Graillat
On modern parallel architectures, floating-point computations may become non-deterministic and, therefore, non-reproducible mainly due to non-associativity of floating-point operations. We propose an algorithm to solve dense triangular systems by leveraging the standard parallel triangular solver and our, recently introduced, multi-level exact summation approach. Finally, we present implementations of the proposed fast reproducible triangular solver and […]
Thomas Weber
The adaptive subdivision step for surface tessellation is a key component of the Reyes rendering pipeline. While this operation has been successfully parallelized for execution on the GPU using a breadth-first traversal, the resulting implementations are limited by their high worst-case memory consumption and high global memory bandwidth utilization. This report proposes an alternate strategy […]
Edgardo Mejia-Roa, Daniel Tabas-Madrid, Javier Setoain, Carlos Garcia, Francisco Tirado, Alberto Pascual-Montano
BACKGROUND: In the last few years, the Non-negative Matrix Factorization (NMF) technique has gained a great interest among the Bioinformatics community, since it is able to extract interpretable parts from high-dimensional datasets. However, the computing time required to process large data matrices may become impractical, even for a parallel application running on a multiprocessors cluster. […]
Yash Ukidave, Fanny Nina Paravecino, Leiming Yu, Charu Kalra, Amir Momeni, Zhongliang Chen, Nick Materise, Brett Daley, Perhaad Mistry, David Kaeli
Heterogeneous systems consisting of multi-core CPUs, Graphics Processing Units (GPUs) and many-core accelerators have gained widespread use by application developers and data-center platform developers. Modern day heterogeneous systems have evolved to include advanced hardware and software features to support a spectrum of application patterns. Heterogeneous programming frameworks such as CUDA, OpenCL, and OpenACC have all […]
Gabriele Cocco
The last few years has seen activity towards programming models, languages and frameworks to address the increasingly wide range and broad availability of heterogeneous computing resources through raised programming abstraction and portability across different platforms. The effort spent in simplifying parallel programming across heterogeneous platforms is often outweighed by the need for low-level control over […]
Leiming Yu, Yash Ukidave, David Kaeli
Speech recognition is used in a wide range of applications and devices such as mobile phones, in-car entertainment systems and web-based services. Hidden Markov Models (HMMs) is one of the most popular algorithmic approaches applied in speech recognition. Training and testing a HMM is computationally intensive and time-consuming. Running multiple applications concurrently with speech recognition […]
Andrey Vladimirov
Common techniques for fine-tuning the performance of automatically vectorized loops in applications for Intel Xeon Phi coprocessors are discussed. These techniques include strength reduction, regularizing the vectorization pattern, data alignment and aligned data hint, and pointer disambiguation. In addition, the loop tiling technique of memory traffic tuning is shown. The optimization methods are illustrated on […]
View View   Download Download (PDF)   
Page 1 of 8112345...102030...Last »

* * *

* * *

Like us on Facebook

HGPU group

218 people like HGPU on Facebook

Follow us on Twitter

HGPU group

1406 peoples are following HGPU @twitter

* * *

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 12.2
  • SDK: 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.

The information send to hgpu.org will be treated according to our Privacy Policy

HGPU group © 2010-2015 hgpu.org

All rights belong to the respective authors

Contact us: