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

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 CPUbased 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 takes advantage of the CUDA and MPI parallel programming models and employs a hybrid CPU+GPU computing model to maximize the performance by overlapping the CPU and GPU computation. The distributed feature of our algor...

• Type: Journal
• Year: 2011
• Where: BMCBI
• Authors: Yongchao Liu, Bertil Schmidt, Douglas L. Maskell
• Keywords: Artificial Intelligence, BMCBI 2011, Error Correction, Error Correction Algorithm, Parallel Programming Models