SparkFlow: towards high-performance data analytics for Spark-based genome analysis

Rosa Filgueira, Feras M. Awaysheh, Adam Carter, Darren J. White, Omar Rana

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

The recent advances in DNA sequencing technology triggered next-generation sequencing (NGS) research in full scale. Big Data (BD) is becoming the main driver in analyzing these large-scale bioinformatic data. However, this complicated process has become the system bottleneck, requiring an amalgamation of scalable approaches to deliver the needed performance and hide the deployment complexity. Utilizing cutting-edge scientific workflows can robustly address these challenges. This paper presents a Spark-based alignment workflow called SparkFlow for massive NGS analysis over singularity containers. SparkFlow is highly scalable, reproducible, and capable of parallelizing computation by utilizing data-level parallelism and load balancing techniques in HPC and Cloud environments. The proposed workflow capitalizes on benchmarking two state-of-art NGS workflows, i.e., BaseRecalibrator and ApplyBQSR. SparkFlow realizes the ability to accelerate large-scale cancer genomic analysis by scaling vertically (HyperThreading) and horizontally (provisions on-demand). Our result demonstrates a trade-off inevitably between the targeted applications and processor architecture. SparkFlow achieves a decisive improvement in NGS computation performance, throughput, and scalability while maintaining deployment complexity. The paper’s findings aim to pave the way for a wide range of revolutionary enhancements and future trends within the High-performance Data Analytics (HPDA) genome analysis realm.
Original languageEnglish
Title of host publication20252 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid)
Number of pages10
ISBN (Print)9781665499576
Publication statusPublished - 19 Jul 2022

Keywords / Materials (for Non-textual outputs)

  • Big data
  • Scientific workflow
  • HPC
  • Genome analysis
  • Apache Spark
  • High-performance data analytics


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