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Massively Parallel Processing of Whole Genome Sequence Data: An In-Depth Performance Study

Summary: Gesall: a massively parallel platform for genome analysis using Wrapper Technology and GDPT to wrap programs unmodified. Genomics workloads show super-linear and sublinear speedups, highlighting data-management questions at the genomics frontier. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5486
Venue
SIGMOD
Year
2017
Pagerank
5.093636e-05
Overall Rank
11,994 | 17.72%
DOI
10.1145/3035918.3064048

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Authors

BibTeX Citation

@inproceedings{roy_sigmod17,
        title = {{Massively Parallel Processing of Whole Genome Sequence Data: An In-Depth Performance Study}},
        author = {Roy, Abhishek and Diao, Yanlei and Evani, Uday and Abhyankar, Avinash and Howarth, Clinton and Le Priol, Rémi and Bloom, Toby},
        series = {{SIGMOD} '17},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3035918.3064048},
        url = {https://dl.acm.org/doi/10.1145/3035918.3064048},
        year = {2017}
}

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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
2,265 A Platform for Scalable One-Pass Analytics using MapReduce 2011 SIGMOD 8.8398946e-05
2,459 WHAM: A High-throughput Sequence Alignment Method 2011 SIGMOD 8.550768e-05
2,949 GenBase: A Complex Analytics Genomics Benchmark 2014 SIGMOD 7.9281519e-05
8,429 Building Highly-Optimized, Low-Latency Pipelines for Genomic Data Analysis 2015 CIDR 5.4263087e-05
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