CORAL: Verification-aware OpenCL based Read Mapper for Heterogeneous Systems.

Clicks: 579
ID: 53757
2019
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #1 of 27 articles by views in ieee/acm transactions on computational biology and bioinformatics

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Genomics has the potential to transform medicine from reactive to a personalized, predictive, preventive and participatory (P4) form. The computational costs of genomics have become a daunting challenge. Most modern computing systems are heterogeneous consisting of combinations of CPUs, GPUs and FPGAs. They require platform-specific software and languages to program making their simultaneous operation challenging. Existing read mappers and analysis tools in the WGS pipeline do not scale for such heterogeneity. Additionally, the computational cost of mapping reads is high due to expensive dynamic programming based verification, where optimized implementations are already available. Thus, improvement in filtration techniques is needed to reduce verification overhead. To address the aforementioned limitations with regards to the mapping element of the WGS pipeline, we propose a Cross-platfOrm Read mApper using opencL (CORAL). CORAL is capable of executing on heterogeneous devices/platforms simultaneously. It can reduce computational time by suitably distributing the workload without any additional programming effort. We showcase this on a quadcore Intel CPU along with two Nvidia GTX 590 GPUs, distributing the workload judiciously to achieve up to 2× speedup compared to when only CPUs are used. To reduce the verification overhead, CORAL dynamically adapts k-mer length during filtration. We demonstrate competitive timings against other mappers using real and simulated read. CORAL is available at: https://github.com/nclaes/CORAL.
Reference Key
maheshwari2019coralieeeacm Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Maheshwari, Sidharth;Gudur, Venkateshwarlu Yellaswamy;Shafik, Rishad;Wilson, Ian;Yakovlev, Alex;Acharyya, Amit;
Journal ieee/acm transactions on computational biology and bioinformatics
Year 2019
DOI
10.1109/TCBB.2019.2943856
URL
Keywords
meta-analysis systematic review loneliness adults effectiveness digital technology

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.