12995
Achal Shah, Angshul Majumdar
Solving linear inverse problems where the solution is known to be sparse is of interest to both signal processing and machine learning research. The standard algorithms for solving such problems are sequential in nature – they tend to be slow for large scale problems. In the past, researchers have used Graphics Processing Units to accelerate […]
View View   Download Download (PDF)   
Daniel Povey, Xiaohui Zhang, Sanjeev Khudanpur
We describe the neural-network training framework used in the Kaldi speech recognition toolkit, which is geared towards training DNNs with large amounts of training data using multiple GPU-equipped or multi-core machines. In order to be as hardware-agnostic as possible, we needed a way to use multiple machines without generating excessive network traffic. Our method is […]
View View   Download Download (PDF)   
Zhenwen Dai, Andreas Damianou, James Hensman, Neil Lawrence
In this work, we present an extension of Gaussian process (GP) models with sophisticated parallelization and GPU acceleration. The parallelization scheme arises naturally from the modular computational structure w.r.t. datapoints in the sparse Gaussian process formulation. Additionally, the computational bottleneck is implemented with GPU acceleration for further speed up. Combining both techniques allows applying Gaussian […]
View View   Download Download (PDF)   
Hisham Mohamed
This thesis studies the scalability of the similarity search problem in large-scale multidimensional data. Similarity search, translating into the neighbour search problem, finds many applications for information retrieval, visualization, machine learning and data mining. The current exponential growth of data motivates the need for approximate and scalable algorithms. In most of existing algorithms and data-structures, […]
View View   Download Download (PDF)   
Sharan Chetlur, Cliff Woolley, Philippe Vandermersch, Jonathan Cohen, John Tran, Bryan Catanzaro, Evan Shelhamer
We present a library that provides optimized implementations for deep learning primitives. Deep learning workloads are computationally intensive, and optimizing the kernels of deep learning workloads is difficult and time-consuming. As parallel architectures evolve, kernels must be reoptimized for new processors, which makes maintaining codebases difficult over time. Similar issues have long been addressed in […]
View View   Download Download (PDF)   
Michal Gorawski
The problem of load balancing is one of the crucial features in distributed data warehouse systems. In this article original load balancing algorithms are presented. The Adaptive Load Balancing Algorithms for Queries (ALBQ) and the algorithms that use grammars and learning machines in managing the ETL process. These two algorithms base the load balancing on […]
View View   Download Download (PDF)   
William F. Ogilvie, Pavlos Petoumenos, Zheng Wang, Hugh Leather
Building effective optimization heuristics is a challenging task which often takes developers several months if not years to complete. Predictive modelling has recently emerged as a promising solution, automatically constructing heuristics from training data. However, obtaining this data can take months per platform. This is becoming an ever more critical problem and if no solution […]
View View   Download Download (PDF)   
Lukas Machlica, Jan Vanek, Zbynek Zajıc
Gaussian Mixture Models (GMMs) are widely used among scientists e.g. in statistics toolkits and data mining procedures. In order to estimate parameters of a GMM the Maximum Likelihood (ML) training is often utilized, more precisely the Expectation-Maximization (EM) algorithm. Nowadays, a lot of tasks works with huge datasets, what makes the estimation process time consuming […]
Wen-Mei Hwu
Histogramming is a technique by which input datasets are mined to extract features and patterns. Histograms have wide range of uses in computer vision, machine learning, database processing, quality control for manufacturing, and many applications benefit from advance knowledge about the distribution of data. Computing a histogram is, essentially, the antithesis of parallel processing. Without […]
View View   Download Download (PDF)   
Yuan Wen, Zheng Wang, Michael F.P. O'Boyle
Heterogeneous systems consisting of multiple CPUs and GPUs are increasingly attractive as platforms for high performance computing. Such platforms are usually programmed using OpenCL which provides program portability by allowing the same program to execute on different types of device. As such systems become more mainstream, they will move from application dedicated devices to platforms […]
View View   Download Download (PDF)   
Robin Kumar, Amandeep Kaur Cheema
Machine learning, a branch of artificial intelligence, concerns the construction and study of systems that can learn from data. Neural network is the well-known branch of machine learning & it has been used extensively by researchers for prediction of data and the prediction accuracy depends upon fine tuning of particular financial data. In this paper […]
View View   Download Download (PDF)   
Di Zhao
High-accuracy optimization is the key component of time-sensitive applications in computer sciences such as machine learning, and we develop single-GPU Iterative Discrete Approximation Monte Carlo Optimization (IDA-MCS) and multi-GPU IDA-MCS in our previous research. However, because of the memory capability constrain of GPUs in a workstation, single-GPU IDA-MCS and multi-GPU IDA-MCS may be in low […]
View View   Download Download (PDF)   
Page 1 of 912345...Last »

* * *

* * *

Like us on Facebook

HGPU group

171 people like HGPU on Facebook

Follow us on Twitter

HGPU group

1282 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: 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.

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

HGPU group © 2010-2014 hgpu.org

All rights belong to the respective authors

Contact us: