Mixed Materials
Communication-Efficient Cluster Scalable Genomics Data Processing Using Apache Arrow Flight
| Τίτλος: | Communication-Efficient Cluster Scalable Genomics Data Processing Using Apache Arrow Flight |
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| Συγγραφείς: | Ahmad, T. (author), Ma, Chengxin (author), Al-Ars, Z. (author), Hofstee, H.P. (author) |
| Στοιχεία εκδότη: | IEEE 2022 |
| Τύπος εγγράφου: | Mixed materials |
| Περίληψη: | Current cluster scaled genomics data processing solutions rely on big data frameworks like Apache Spark, Hadoop and HDFS for data scheduling, processing and storage. These frameworks come with additional computation and memory overheads by default. It has been observed that scaling genomics dataset processing beyond 32 nodes is not efficient on such frameworks.To overcome the inefficiencies of big data frameworks for processing genomics data on clusters, we introduce a low-overhead and highly scalable solution on a SLURM based HPC batch system. This solution uses Apache Arrow as in-memory columnar data format to store genomics data efficiently and Arrow Flight as a network protocol to move and schedule this data across the HPC nodes with low communication overhead.As a use case, we use NGS short reads DNA sequencing data for pre-processing and variant calling applications. This solution outperforms existing Apache Spark based big data solutions in term of both computation time (2x) and lower communication overhead (more than 20-60% depending on cluster size). Our solution has similar performance to MPI-based HPC solutions, with the added advantage of easy programmability and transparent big data scalability. The whole solution is Python and shell script based, which makes it flexible to update and integrate alternative variant callers. Our solution is publicly available on GitHub at https://github.com/abs-tudelft/time-to-fly-high/tree/main/genomics Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. Computer Engineering |
| Όροι ευρετηρίου: | Genomics, Whole Genome/Exome Sequencing, Big Data, Apache Arrow, In-Memory, Plasma Object Store, Parallel Processing, conference paper |
| DOI: | 10.1109.ISPDC55340.2022.00028 |
| Σύνδεσμος: | Proceedings of the 2022 21st International Symposium on Parallel and Distributed Computing (ISPDC)--978-1-6654-8803-7 2022 21st International Symposium on Parallel and Distributed Computing (ISPDC)--ae4772da-eecb-4dc2-9630-599dce54b37c |
| Διαθεσιμότητα: | Open access content. Open access content © 2022 T. Ahmad, Chengxin Ma, Z. Al-Ars, H.P. Hofstee |
| Σημείωση: | English |
| Other Numbers: | NLTUD oai:tudelft.nl:uuid:867e7f25-6b2c-4769-8018-8f5ad2d2ac38 doi:10.1109/ISPDC55340.2022.00028 1390839363 |
| Πηγή συνεισφοράς: | DELFT UNIV OF TECHNOL From OAIster®, provided by the OCLC Cooperative. |
| Αριθμός Καταχώρησης: | edsoai.on1390839363 |
| Βάση Δεδομένων: | OAIster |
| DOI: | 10.1109.ISPDC55340.2022.00028 |
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