Vadim Demchik
General principles of pseudorandom numbers production for Monte Carlo simulations on GPUs are discussed by creating an OpenCL open-source library of pseudorandom number generators PRNGCL. The library contains implementation of a number of the most popular uniform generators. The most popular pseudorandom number generators for Monte Carlo simulations and libraries for GPUs are reviewed. Some […]
Lena Oden, Benjamin Klenk, Holger Froning
GPUs are widely used in high performance computing, due to their high computational power and high performance per Watt. Still, one of the main bottlenecks of GPU-accelerated cluster computing is the data transfer between distributed GPUs. This not only affects performance, but also power consumption. The most common way to utilize a GPU cluster is […]
View View   Download Download (PDF)   
Kamran Karimi
OpenCL, along with CUDA, is one of the main tools used to program GPGPUs. However, it allows running the same code on multi-core CPUs too, making it a rival for the long-established OpenMP. In this paper we compare OpenCL and OpenMP when developing and running compute-heavy code on a CPU. Both ease of programming and […]
View View   Download Download (PDF)   
Yuki Tsujita, Toshio Endo
Recently large scale scientific computation on heterogeneous supercomputers equipped with accelerators is receiving attraction. However, traditional static job execution methods and memory management methods are insufficient in order to harness heterogeneous computing resources including memory efficiently, since they introduce larger data movement costs and lower resource usage. This paper takes the Cholesky decomposition computation, which […]
View View   Download Download (PDF)   
Vasvi Kakkad
Advances in technology have given rise to applications that are deployed on wireless sensor networks (WSNs), the cloud, and the Internet of things. There are many emerging applications, some of which include sensor-based monitoring, web traffic processing, and network monitoring. These applications collect large amount of data as an unbounded sequence of events and process […]
View View   Download Download (PDF)   
Krzysztof Kaczmarski, Pawel Rzazewski, Albert Wolant
Motion planning is an important and well-studied field of robotics. A typical approach to finding a route is to construct a cell graph representing a scene and then to find a path in such a graph. In this paper we present and analyze parallel algorithms for constructing the cell graph on a SIMD-like GPU processor. […]
View View   Download Download (PDF)   
Christian Andreetta, Vivien Begot, Jost Berthold, Martin Elsman, Troels Henriksen, Maj-Britt Nordfang, Cosmin E. Oancea
Commodity many-core hardware is now mainstream, driven in particular by the evolution of general purpose graphics programming units (GPGPUs), but parallel programming models are lagging behind in effectively exploiting the available application parallelism. There are two principal reasons. First, real-world applications often exhibit a rich composition of nested parallelism, whose statical extraction requires a set […]
View View   Download Download (PDF)   
Qiankun Dong, Tao Li, Shuai Zhang, Xiaofan Jiao, Jiabing Leng
GPUs are now widely used as high performance general purpose computing devices. More and more applications have achieved large speedups with one or more GPUs, and the number of GPU programs is growing fast. In certain situations, the high level CUDA C code of kernels is not available, but low level PTX code can be […]
View View   Download Download (PDF)   
Luna Backes
Computer vision (CV) is widely expected to be the next "Big Thing" in mobile computing. For example, Google has recently announced their project "Tango", a 5-inch Android phone containing highly customized hardware and software designed to track the full 3-dimensional motion of the device as you hold it while simultaneously creating a map of the […]
View View   Download Download (PDF)   
Anuj Kalia, Dong Zhou, Michael Kaminsky, David G. Andersen
Numerous recent research efforts have explored the use of Graphics Processing Units (GPUs) as accelerators for software-based routing and packet handling applications, typically demonstrating throughput several times higher than using legacy code on the CPU alone. In this paper, we explore a new hypothesis about such designs: For many such applications, the benefits arise less […]
View View   Download Download (PDF)   
Kyuyeon Hwang, Wonyong Sung
Recurrent neural networks (RNNs) have shown outstanding performance on processing sequence data. However, they suffer from long training time, which demands parallel implementations of the training procedure. Parallelization of the training algorithms for RNNs are very challenging because internal recurrent paths form dependencies between two different time frames. In this paper, we first propose a […]
View View   Download Download (PDF)   
Weifeng Liu, Brian Vinter
Sparse matrix-vector multiplication (SpMV) is a fundamental building block for numerous applications. In this paper, we propose CSR5 (Compressed Sparse Row 5), a new storage format, which offers high-throughput SpMV on various platforms including CPUs, GPUs and Xeon Phi. First, the CSR5 format is insensitive to the sparsity structure of the input matrix. Thus the […]
Page 1 of 50412345...102030...Last »

* * *

* * *

Like us on Facebook

HGPU group

229 people like HGPU on Facebook

Follow us on Twitter

HGPU group

1424 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 12.3
  • 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.

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: