DecGPU: distributed error correction on massively parallel graphics processing units using CUDA and MPI.

ABSTRACT:

Background

Next-generation sequencing technologies have led to the high-throughput production of sequence data (reads) at low cost. However, these reads are significantly shorter and more error-prone than conventional Sanger shotgun reads. This poses a challenge for the de novo assembly in terms of assembly quality and scalability for large-scale short read datasets.

Results

We present DecGPU, the first parallel and distributed error correction algorithm for high-throughput short reads (HTSRs) using a hybrid combination of CUDA and MPI parallel programming models. DecGPU provides CPU-based and GPU-based versions, where the CPU-based version employs coarse-grained and fine-grained parallelism using the MPI and OpenMP parallel programming models, and the GPU-based version take

SUBMITTER: Liu Y 

PROVIDER: S-EPMC3072957 | biostudies-literature | 2011 Mar

REPOSITORIES: biostudies-literature