12593
Anas Abu-Doleh, Kamer Kaya, Mohamed Abouelhoda, Umit V. Catalyurek
The revolution in high-throughput sequencing technologies accelerated the discovery and extraction of various genomic sequences. However, the massive size of the generated datasets raise several computational problems. For example, aligning the sequences or finding the similar regions in them, which is one of the crucial steps in many bioinformatics pipelines, is a time consuming task. […]
View View   Download Download (PDF)   
Gustavo Encarnacao
Since the discovery of Deoxyribonucleic Acid (DNA) significant technological advances were made, leading to very large amounts of data gathered for analysis. The tools for this analysis however have advanced at a slower pace and have become one of the limiting factors of new discoveries in this field of research. Recently, from the 3D game […]
View View   Download Download (PDF)   
Gang Liao, Qi Sun, Longfei Ma, Zhihui Qin
In this paper, a contrastive evaluation of massive parallel implementations of suffix tree and suffix array to accelerate genome sequence matching are proposed based on Intel Core i7 3770K quad-core and NVIDIA GeForce GTX680 GPU(kepler architecture). Due to the more regular execution flow of the indexed binary search algorithm, the more efficient use of the […]
View View   Download Download (PDF)   
Binay Kumar Pandey, Rajdeep Niyogi, Ankush Mittal
In present time weighted suffix tree is consider as a one of the most important existing data structure used for analyzing molecular weighted sequence. Although a static partitioning based parallel algorithm existed for the construction of weighted suffix tree, but for very long weighted DNA sequences it takes significant amount of time. However, in our […]
View View   Download Download (PDF)   
Gustavo Encarnacao, Nuno Sebastiao, Nuno Roma
A comparative analysis of high-performance implementations of two state of the art index structures that are of particular interest in the field of bioinformatics applications to accelerate the alignment of DNA sequences is presented. The two indexes are based on suffix trees and suffix arrays and were implemented in two different platforms: a quad-core CPU […]
View View   Download Download (PDF)   
Weidong Sun, Zongmin Ma
Suffix array is a simpler and compact alternative to the suffix tree, lexicographic name construction is the fundamental building block in suffix array construction process. This paper depicts the design issues of first data parallel implementation of the lexicographic name construction algorithm on a commodity multiprocessor GPU using the Compute Unified Device Architecture (CUDA) platform, […]
View View   Download Download (PDF)   
Abdullah Gharaibeh, Matei Ripeanu
GPUs offer drastically different performance characteristics compared to traditional multicore architectures. To explore the tradeoffs exposed by this difference, we refactor MUMmer, a widely-used, highly-engineered bioinformatics application which has both CPU- and GPU-based implementations. We synthesize our experience as three high-level guidelines to design efficient GPU-based applications. First, minimizing the communication overheads is as important […]
Michael C. Schatz, Cole Trapnell
We present a string-matching program that runs on the GPU. Our program, Cmatch, achieves a speedup of as much as 35x on a recent GPU over the equivalent CPU-bound version. String matching has a long history in computational biology with roots in finding similar proteins and gene sequences in a database of known sequences. The […]
Cole Trapnell, Michael C. Schatz
MUMmerGPU uses highly-parallel commodity graphics processing units (GPU) to accelerate the data-intensive computation of aligning next generation DNA sequence data to a reference sequence for use in diverse applications such as disease genotyping and personal genomics. MUMmerGPU 2.0 features a new stackless depth-first-search print kernel and is 13× faster than the serial CPU version of […]

* * *

* * *

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: