Chetan D. Pise, Shailendra W. Shende
Graphs play a very important role in the field of Science and Technology for finding the shortest distance between any two places. This Paper demonstrate the recent technology named as CUDA (Compute Unified Device Architecture) working for BFS Graph Algorithm. There are some Graph algorithms are fundamental to many disciplines and application areas. Large graphs […]
View View   Download Download (PDF)   
Yichao Cheng, Hong An, Zhitao Chen, Feng Li, Zhaohui Wang, Xia Jiang, Yi Peng
Graph is a widely used data structure and graph algorithms, such as breadth-first search (BFS), are regarded as key components in a great number of applications. Recent studies have attempted to accelerate graph algorithms on highly parallel graphics processing unit (GPU). Although many graph algorithms based on large graphs exhibit abundant parallelism, their performance on […]
View View   Download Download (PDF)   
Frederico L. Cabral, Carla Osthoff, Rafael Nardes, Daniel Ramos
Many problems in Computer Science can be modelled using graphs. Evaluating node centrality in complex networks, which can be considered equivalent to undirected graphs, provides an useful metric of the relative importance of each node inside the evaluated network. The knowledge on which the most central nodes are, has various applications, such as improving information […]
View View   Download Download (PDF)   
Guillaume Chapuis
The exponential growth in bioinformatics data generation and the stagnation of processor frequencies in modern processors stress the need for efficient implementations that fully exploit the parallel capabilities offered by modern computers. This thesis focuses on parallel algorithms and implementations for bioinformatics problems. Various types of parallelism are described and exploited. This thesis presents applications […]
View View   Download Download (PDF)   
Jianlong Zhong, Bingsheng He
Medusa is a parallel graph processing system on graphics processors (GPUs). The core design of Medusa is to enable developers to leverage the massive parallelism and other hardware features of GPUs by writing sequential C/C++ code for a small set of APIs. This simplifies the implementation of parallel graph processing on the GPU. The runtime […]
Adam McLaughlin, David A. Bader
Betweenness Centrality is a widely used graph analytic that has applications such as finding influential people in social networks, analyzing power grids, and studying protein interactions. However, its complexity makes its exact computation infeasible for large graphs of interest. Furthermore, networks tend to change over time, invalidating previously calculated results and encouraging new analyses regarding […]
View View   Download Download (PDF)   
A.E. Sarıyuce, E. Saule, K. Kaya, U.V. Çatalyurek
Centrality metrics have shown to be highly correlated with the importance and loads of the nodes in a network. Given the scale of today’s social networks, it is essential to use efficient algorithms and high performance computing techniques for their fast computation. In this work, we exploit hardware and software vectorization in combination with fine-grain […]
View View   Download Download (PDF)   
Oya Celiktutan, Christian Wolf, Bulent Sankur, Eric Lombardi
Graphs and hyper-graphs are frequently used to recognize complex and often non-rigid patterns in computer vision, either through graph matching or point-set matching with graphs. Most formulations resort to the minimization of a difficult energy function containing geometric or structural terms, frequently coupled with data attached terms involving appearance information. Traditional methods solve the minimization […]
View View   Download Download (PDF)   
Andrew Davidson, Sean Baxter, Michael Garland, John D. Owens
Finding the shortest paths from a single source to all other vertices is a fundamental method used in a variety of higher-level graph algorithms. We present three parallel-friendly and work-efficient methods to solve this Single-Source Shortest Paths (SSSP) problem: Workfront Sweep, Near-Far and Bucketing. These methods choose different approaches to balance the tradeoff between saving […]
Erich Elsen, Vishal Vaidyanathan
VertexAPI2 uses state-of-the-art GPU algorithms to implement the Gather-Apply-Scatter (GAS) abstraction for graph computations. VertexAPI2 provides up to an order of magnitude greater performance over the previous implementation and performance comparable to speed-of-light hand-coded algorithms in some cases, while retaining the simplicity of development of the GAS model. The current code also has a preliminary […]
Mehmet Deveci, Kamer Kaya, Bora Ucar, Umit V. Catalyurek
We design, develop, and evaluate an atomic- and lock-free GPU implementation of the push-relabel algorithm in the context of finding maximum cardinality matchings in bipartite graphs. The problem has applications on computer science, scientific computing, bioinformatics, and other areas. Although the GPU parallelization of the push-relabel technique has been investigated in the context of flow […]
View View   Download Download (PDF)   
Abdullah Gharaibeh, Elizeu Santos-Neto, Lauro Beltrao Costa, Matei Ripeanu
The increasing scale and wealth of inter-connected data, such as those accrued by social network applications, demand the design of new techniques and platforms to efficiently derive actionable knowledge from large-scale graphs. However, real-world graphs are famously difficult to process efficiently. Not only they have a large memory footprint, but also most graph algorithms entail […]
Page 1 of 812345...Last »

* * *

* * *

Like us on Facebook

HGPU group

129 people like HGPU on Facebook

Follow us on Twitter

HGPU group

1190 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: