BarraCUDA is designed to take advantage of the parallelism of GPU to accelerate the alignment of millions of sequencing reads generated by NGS instruments. By doing this, we could, at least in part streamline the current bioinformatics pipeline such that the wider scientific community could benefit from the sequencing technology.
One of the goals of the special issue is to discover how the bioinformatics scientific community is trying to take advantage of a variety of modern high-end parallel and distributed architectures.
BGI, the world’s largest genomics institute, has slashed the time to analyze batches of DNA sequencing data from nearly four days to just six hours using a NVIDIA Tesla GPU-based server farm. The speed up is considered a critically important step in determining, in an affordable manner, the chemical building blocks that make up a DNA molecule.
The goal of this workshop is to provide a forum for discussion of latest research in developing high-performance computing solutions to data-intensive and compute-intensive problems arising from molecular biology and related life sciences areas.
Excellent overview just have been published in Soft Computing. Authors have sketched the threat offered to the software industry by the many times announced failure of Moore’s law, and how the also many times announced age of parallel computing may actually be upon us.
Microsoft announced the release of NCBI BLAST on Windows Azure. The new application enables a broader community of scientists to combine desktop resources with the power of cloud computing for critical biological research. At the SC10 conference Microsoft showcased Azure by demonstrating its use for 100 billion comparisons of protein sequences in a database manged by the National Center for Biotechnology Information (NCBI).
CUDA–MEME: Accelerating motif discovery in biological sequences using CUDA-enabled graphics processing units
In this paper, authors present a highly parallel formulation and implementation of the MEME motif discovery algorithm using the CUDA programming model. To achieve high efficiency, we introduce two parallelization approaches: sequence-level and substring-level parallelization. Furthermore, a hybrid computing framework is described to take advantage of both CPU and GPU compute resources.
The GPU implementation achieves up to 34 times speedup over the CPU implementation of tableau-based structure search with simulated annealing, making it one of the fastest available methods. To the best of our knowledge, this is the first application of a GPU to the protein structural search problem.
Computing research has become a vital cog in the machinery required to drive biological discovery. Computing has made possible significant achievements over the last decade, especially in the genomics sector. An emerging area is the investigation of hardware accelerators for speeding up the massive scale of computation needed in large-scale biocomputing applications. Various hardware platforms, such as FPGA, Graphics Processing Unit (GPU), the Cell Broadband Engine (CBE) and multi-core processors are being explored. In this paper, we present a survey of hardware accelerators for biocomputing by choosing a representative set of each.