A GPU-accelerated graph layout plugin will be released soon August 5, 2011
Posted by Jun in Bioinformatics, Cytoscape, GPU.Tags: graph network force-directed layout GPU CUDA Cytoscape plugin visualization AllegroViva AllegroLayout protein interaction
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We are pleased to announce that a new GPU-accelerated plugin for Cytoscape named AllegroLayout will be released this month.
The AllegroLayout plugin automatically draws a graph in an aesthetically pleasing way and provides two kinds of force-based layout algorithms. It also provides both of GPU and CPU implementations for each algorithm. The GPU implementation with the latest graphics card is about 200 times faster than the CPU implementation, which exploits single core of the latest CPU and is implemented in JAVA.
Force-based layout algorithm can achieve high-quality results that have more or less uniform edge length and uniform node distribution and also reflect inherent symmetry. It simulates a graph as a physical system by assigning forces as if the edges were springs and the nodes were electrically charged particles and finds an equilibrium state as a final result. Its main drawback is high running time because it has to calculate the forces of all pairs of nodes. However, the AllegroLayout plugin solves the main problem by exploiting massive-parallel GPU computing architecture and makes it possible to perform the layout algorithm with a large graph in several seconds.
The AllegroLayout plugin will support various kinds of O/S platforms such as Windows, MAC OS X, and Linux and any CUDA-capable GPU that is included in most NVIDIA graphics hardware.
More detailed information will be available soon on our website.
we look forward to your continued interest.
Please enjoy the video introducing the plugin and you can enjoy it most in full screen mode and HD format with sound.
AllegroMCODE Analysis Performance for a Huge Network February 25, 2011
Posted by allegroviva in Bioinformatics, Cytoscape, GPU.Tags: allegromcode, cytoscape, gpu, mcode, network
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We have evaluated the performance of AllegroMCODE with the multi-threaded JAVA implementation and our GPU Server.
The GPU finds clusters 881 time faster than the latest Intel core i7 (quad cores) system as followed below.
Please note that we also wanted to evaluate the MCODE plugin, but all of Cytoscape v2.7, 2.8, and 2.8.1 cause an Java exception while processing the test network by using MCODE.
| System Specification | Intel Core i7 2.67GHz
6G DDR3 (using 4 cores) |
NVIDIA GTX580
1.5G GDDR5 |
| Algorithm Option | Java | GPU Server |
| Analysis Options | Default Parameters | |
| Test Network Data | Node: 325,729 Edge: 1,469,679 |
|
| Processing Time | 1 hour 22 min 15 sec | 5.6 seconds |
| GPU Speedup | 881 times faster | |
If you would like to evaluate by yourself, you can get the test network file from: click
Please check more information about the AllegroMCODE plugin from: http://allegroviva.com/allegromcode
Release of the AllegroMCODE plugin February 15, 2011
Posted by allegroviva in Bioinformatics, Cytoscape, GPU.Tags: allegromcode, cytoscape, gpu, mcode, network
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We are pleased to announce the first relase of the AllegroMCODE plugin. The AllegroMCODE plugin is a Cytoscape plugin that find clusters, or highly interconnected groups of nodes in a large complex network such as a protein interaction network and a social network.
This plugin generates the same clustering results as the well-known MCODE plugin, but it can process several hundred times faster than the MCODE plugin for large complex networks by exploiting our high performance GPU computing architecture. You can also enjoy the processing speedup without any special graphics hardware since it interfaces with our free GPU computing server.
Your analysis task of large complex network will be faster than ever by using the AllegroMCODE plugin. For example, it takes 0.52 seconds to find clusters from a large PPI netwrok which has 31,215 nodes and 317,706 edges by using the AllegroMCODE plugin with our free GPU computing server.
Compatibility: Cytoscape 2.6, 2.7, and 2.8
Availability: through the Cytoscape plugin manager under the “Analysis” category.
You can get more information from the AllegroMCODE page.
http://www.allegroviva.com/allegromcode
Screenshot AllegroMCODE
A GPU-accelerated bioinformatics application for large-scale protein interaction networks February 15, 2011
Posted by allegroviva in Bioinformatics, Conference & Expo, Cytoscape, GPU, Paper.Tags: allegromcode, cytoscape, gpu, mcode, protein network
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Two researchers, Jun Sung Yoon from the AllegroViva Corporation in California and Won-Hyong Chung from the Korea Research Institute of Bioscience & Biotechnology in Daejeon, Korea, have presented a paper that describes an attempt to exploit GPUs for biological network analysis.
2011 Asia Pacific Bioinformatics Conference in Incheon, Korea.
Click here to download the poster.
Biophysical Society 54th Annual Meeting February 6, 2010
Posted by Jun in Conference & Expo.add a comment
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San Francisco, CA
FEB 20-24, 2010
Website: https://www.biophysics.org/Default.aspx?alias=www.biophysics.org/2010meeting
BioIT World Conference & Expo 2010 February 6, 2010
Posted by Jun in Bioinformatics, Conference & Expo.add a comment
April 20-22, 2010
Boston, MA
Siemens uses graphics chips to create better 3-D views of babies in wombs (video) February 6, 2010
Posted by Jun in GPU, Products.add a comment
Siemens is using Nvidia’s CUDA graphics technology to create three-dimensional ultrasounds of babies in utero and other medical applications.
It takes advantage of CUDA, which is a programming environment which makes use of the computing power of a graphics chip for non-graphics purposes. With CUDA, Siemens can take the images and view them in stereoscopic 3-D. It can take images of a heart beating so that doctors can figure out if there is an abnormality in how the heart beats.
Click the following link to read the full article.
List of Free Software for Microscopy January 28, 2010
Posted by Jun in Bioinformatics, Molecular Biology.add a comment
The following document has many free softwares for microscopy.
Tesla Bio Workbench Enables Scientists to Achieve New Breakthroughs January 17, 2010
Posted by Jun in Bioinformatics, GPU.add a comment
NVIDIA today announced the Tesla Bio Workbench, which enables scientists to push the boundaries of biological research by turning a standard PC into a “computational laboratory” capable of running complex bioscience codes in fields such as drug discovery and DNA sequencing more than 10-20 times faster through the use NVIDIA® Tesla™ GPUs.
The Tesla Bio Workbench consists of:
- A range of GPU-optimized bioscience applications for molecular dynamics- and quantum chemistry- based research, including: AMBER, GROMACS, LAMMPS, NAMD, TeraChem, VMD, and bioinformatics applications like CUDASW++ (Smith-Waterman), GPU-HMMER, and MUMmerGPU.
- A community site for downloading the applications, checking out the latest benchmarks, reading academic papers and tutorials, joining discussion forums with the application developers themselves and more.
- Details on the Tesla GPU-based workstations and clusters available worldwide for easy deployment of these applications.
High-Content Analysis, 2010 January 11, 2010
Posted by allegroviva in Event.add a comment
High-Content Analysis, Jan 11-15, 2010
COVERAGE INCLUDES
COMPOUND/siRNA SCREENING • PATHWAY ANALYSIS • DATA MANAGEMENT
IMAGE ANALYSIS • HCA FOR STEM CELLS • LIVE-CELL IMAGING
FLOW CYTOMETRY • NEURONAL SCREENING
NEW BIOLOGICAL MODELS fOR HCA • NOVEL PROBES aND BIOSENSORS
Conference-at-a-Glance [PDF]
