Halvade*

Halvade* is a MapReduce implementation of the best-practice DNA sequencing pipeline as recommended by Broad Institute.

Parallel Efficiency Reaches 91 Percent1

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.

Halvade* is 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. Halvade is implemented in Java and uses the Apache Hadoop* MapReduce 2.0 API. For example, it supports the Cloudera Hadoop* Distribution as well as Amazon EMR*.

Performance Results

Using a 15-node computer cluster with 360 CPU cores in total, Halvade processes the NA12878 dataset (human, 100 bp paired-end reads, 50x coverage) in less than 3 hours with high parallel efficiency1.

The speed-up curve shows that the more Hadoop tasks, the better the performance, with almost linear scaling. Here, each task uses six physical Intel® Xeon® CPU cores, which amounts to 12 hardware threads per Hadoop task. The efficiency curve shows the same result: With 360 cores in total, parallel efficiency is at 91.1 percent, indicating that available resources are effectively used.

Without Halvade, the same pipeline would run for an estimated 288 hours (ca. 12 days) on a single node. Even with multithreading enabled within the tools that support it, a runtime of 120 hours (ca. 5 days) was measured. With Halvade, the runtime is reduced to 3 hours on a 15-node Intel® Xeon® CPU cluster running Cloudera Hadoop* Distribution. Using only a single node, the whole pipeline runs in 48 hours (ca. 2 days).

Download the code ›

Reproduce these results with this optimization recipe ›

Publications

Dries Decap, Joke Reumers, Charlotte Herzeel, Pascal Costanza, and Jan Fostier. “Halvade: scalable sequence analysis with MapReduce.” Bioinformatics (2015) 31 (15): 2482-2488 first published online March 26, 2015.

Read the Halvade analysis article ›

Configuration Table

System Overview

 

Nodes

15 nodes, with 64 GB RAM each

Processor

In total: 30 Intel® Xeon® E5-2695 v2 CPUs @ 2.40 GHz each

Cores

In total: 360 physical cores (720 threads)

RAM

In total: 960 GB RAM

Apache Hadoop* Distribution

Cloudera version 5.0.1b

Tasks per Node

4 tasks per node, each task using 6 physical cores (12 threads)

제품 및 성능 정보

1

벤치마크 결과는 “스펙터”와 “멜트다운”으로 알려진 공격에 대응하기 위한 목적의 최신 소프트웨어 패치 및 펌웨어 업데이트를 적용하기 이전에 얻어진 것입니다. 이러한 업데이트를 적용할 경우 이와 같은 결과가 귀하의 장치 또는 시스템에는 해당하지 않을 수 있습니다.

성능 테스트에 사용된 소프트웨어 및 워크로드는 인텔® 마이크로프로세서에만 적합하도록 최적화되었을 수 있습니다. SYSmark* 및 MobileMark*와 같은 성능 테스트는 특정 컴퓨터 시스템, 구성 요소, 소프트웨어, 운영 및 기능을 사용해서 수행합니다. 해당 요소에 변경이 생기면 결과가 달라질 수 있습니다. 구매를 고려 중인 제품을 제대로 평가하려면 다른 제품과 결합하여 사용할 경우 해당 제품의 성능을 포함한 기타 정보 및 성능 테스트를 참고해야 합니다. 자세한 내용은 http://www.intel.co.kr/benchmarks를 참조하십시오.