Pascal Costanza

Halvade: scalable sequence analysis with MapReduce.

Motivation: Post-sequencing DNA analysis typically consists of read mapping followed by variant calling. Especially for whole genome sequencing, this computational step is very time-consuming, even when using multithreading on a multi-core machine. Results: We present Halvade, a framework that enables sequencing pipelines to be executed in parallel on a multi-node and/or multi-core compute infrastructure in a highly efficient manner. As an example, a DNA sequencing analysis pipeline for variant calling has been implemented according to the GATK Best Practices recommendations, supporting both whole genome and whole exome sequencing. Using a 15-node computer cluster with 360 CPU cores in total, Halvade processes the NA12878 dataset (human, 100 bp paired-end reads, 50× coverage) in <3 h with very high parallel efficiency. Even on a single, multi-core machine, Halvade attains a significant speedup compared with running the individual tools with multithreading. Availability and implementation: Halvade is written in Java and uses the Hadoop MapReduce 2.0 API. It supports a wide range of distributions of Hadoop, including Cloudera and Amazon EMR. Its source is available at http://bioinformatics.intec.ugent.be/halvade under GPL license. Contact: jan.fostier@intec.ugent.be Supplementary information: Supplementary data are available at Bioinformatics online.

Resolving Load Balancing Issues in BWA on NUMA Multicore Architectures

Running BWA in multithreaded mode on a multi-socket server results in poor scaling behaviour. This is because the current parallelisation strategy does not take into account the load imbalance that is inherent to the properties of the data being aligned, e.g. varying read lengths and numbers of mutations. Additional load imbalance is also caused by the BWA code not anticipating certain hardware characteristics of multi-socket multicores, such as the non-uniform memory access time of the different cores. We show that rewriting the parallel section using Cilk removes the load imbalance, resulting in a factor two performance improvement over the original BWA.

elPrep: High-Performance Preparation of Sequence Alignment/Map Files for Variant Calling.

elPrep is a high-performance tool for preparing sequence alignment/map files for variant calling in sequencing pipelines. It can be used as a replacement for SAMtools and Picard for preparation steps such as filtering, sorting, marking duplicates, reordering contigs, and so on, while producing identical results. What sets elPrep apart is its software architecture that allows executing preparation pipelines by making only a single pass through the data, no matter how many preparation steps are used in the pipeline. elPrep is designed as a multithreaded application that runs entirely in memory, avoids repeated file I/O, and merges the computation of several preparation steps to significantly speed up the execution time. For example, for a preparation pipeline of five steps on a whole-exome BAM file (NA12878), we reduce the execution time from about 1:40 hours, when using a combination of SAMtools and Picard, to about 15 minutes when using elPrep, while utilising the same server resources, here 48 threads and 23GB of RAM. For the same pipeline on whole-genome data (NA12878), elPrep reduces the runtime from 24 hours to less than 5 hours. As a typical clinical study may contain sequencing data for hundreds of patients, elPrep can remove several hundreds of hours of computing time, and thus substantially reduce analysis time and cost.

Halvade-RNA : parallel variant calling from transcriptomic data using MapReduce

Given the current cost-effectiveness of next-generation sequencing, the amount of DNA-seq and RNA-seq data generated is ever increasing. One of the primary objectives of NGS experiments is calling genetic variants. While highly accurate, most variant calling pipelines are not optimized to run efficiently on large data sets. However, as variant calling in genomic data has become common practice, several methods have been proposed to reduce runtime for DNA-seq analysis through the use of parallel computing. Determining the effectively expressed variants from transcriptomics (RNA-seq) data has only recently become possible, and as such does not yet benefit from efficiently parallelized workflows. We introduce Halvade-RNA, a parallel, multi-node RNA-seq variant calling pipeline based on the GATK Best Practices recommendations. Halvade-RNA makes use of the MapReduce programming model to create and manage parallel data streams on which multiple instances of existing tools such as STAR and GATK operate concurrently. Whereas the single-threaded processing of a typical RNA-seq sample requires ∼28h, Halvade-RNA reduces this runtime to ∼2h using a small cluster with two 20-core machines. Even on a single, multi-core workstation, Halvade-RNA can significantly reduce runtime compared to using multi-threading, thus providing for a more cost-effective processing of RNA-seq data. Halvade-RNA is written in Java and uses the Hadoop MapReduce 2.0 API. It supports a wide range of distributions of Hadoop, including Cloudera and Amazon EMR.

Efficient synchronization for stencil computations using dynamic task graphs

Abstract Executing stencil computations constitutes a significant portion of execution time for many numerical simulations running on high performance computing systems. Most parallel implementations of these stencil operations suffer from a substantial synchronization overhead. Furthermore, with the rapidly increasing number of cores these synchronization costs keep rising. This paper presents a novel approach for reducing the synchronization overhead of stencil computations by leveraging dynamic task graphs to avoid global barriers and minimizing spin-waiting, and exploiting basic properties of stencil operations to optimize the execution and memory management. Our experiments show a reduction in synchronization overhead by at least a factor four when compared to state-of-the-art stencil compilers like Pochoir and Patus.

Performance Analysis of a Parallel, Multi-node Pipeline for DNA Sequencing

Post-sequencing DNA analysis typically consists of read mapping followed by variant calling and is very time-consuming, even on a multi-core machine. Recently, we proposed Halvade, a parallel, multi-node implementation of a DNA sequencing pipeline according to the GATK Best Practices recommendations. The MapReduce programming model is used to distribute the workload among different workers. In this paper, we study the impact of different hardware configurations on the performance of Halvade. Benchmarks indicate that especially the lack of good multithreading capabilities in the existing tools (BWA, SAMtools, Picard, GATK) cause suboptimal scaling behavior. We demonstrate that it is possible to circumvent this bottleneck by using multiprocessing on high-memory machines rather than using multithreading. Using a 15-node cluster with 360 CPU cores in total, this results in a runtime of 1 h 31 min. Compared to a single-threaded runtime of \(\sim \)12 days, this corresponds to an overall parallel efficiency of 53 %.

Session details: Onward! workshop summaries

SPLASH and Onward! workshops are a great way to grow your knowledge and expand your professional network. They are highly interactive events that provide a creative and collaborative environment where attendees meet to discuss and solve challenging problems related to a variety of new emerging technologies and research areas. Over the past two decades, the OOPSLA workshops provided an incubator for exploring many of the ideas that went on to shape general software practice (for example, design patterns, UML, aspect-oriented programming, and agile methods). That tradition continues this year both within the new charter for SPLASH established last year, and incorporating the visionary focus of Onward! This year, SPLASH and Onward! offer twelve workshops that represent a diverse set of technology and research topics. Areas covered by the workshops include actors, agents, agile practices, cloud-based computing, decentralized control, embedded systems, evaluation and usability of programming languages and tools, foundations of object-oriented programming, free composition, legacy code, multicore programming, objectoriented and domain-specific modelling, programming languages and runtimes for internet clients, smartphone applications, and virtual machines. The summaries included in this companion provide an introduction to the goals and objectives of each workshop. We welcome you to these workshops with the hope that the discussions are productive and fruitful, and assist in fostering new collaborations that extend beyond the borders of the conference!

Session details: Workshop summaries

SPLASH and Onward! workshops are a great way to grow your knowledge and expand your professional network. They are highly interactive events that provide a creative and collaborative environment where attendees meet to discuss and solve challenging problems related to a variety of new emerging technologies and research areas. Over the past two decades, the OOPSLA workshops provided an incubator for exploring many of the ideas that went on to shape general software practice (for example, design patterns, UML, aspect-oriented programming, and agile methods). That tradition continues this year both within the new charter for SPLASH established last year, and incorporating the visionary focus of Onward! This year, SPLASH and Onward! offer twelve workshops that represent a diverse set of technology and research topics. Areas covered by the workshops include actors, agents, agile practices, cloud-based computing, decentralized control, embedded systems, evaluation and usability of programming languages and tools, foundations of object-oriented programming, free composition, legacy code, multicore programming, objectoriented and domain-specific modelling, programming languages and runtimes for internet clients, smartphone applications, and virtual machines. The summaries included in this companion provide an introduction to the goals and objectives of each workshop. We welcome you to these workshops with the hope that the discussions are productive and fruitful, and assist in fostering new collaborations that extend beyond the borders of the conference!