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Memory-Efficient All-Pair Suffix-Prefix Overlaps on GPU
Sayan Goswami
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Distributed de novo assembler for large-scale long-read datasets
Sayan Goswami, Kisung lee, Seung-Jong Park
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Towards Distributed Cyberinfrastructure for Smart Cities Using Big Data and Deep Learning Technologies
Our current efforts and ultimate vision to build distributed cyberinfrastructure which integrates big data and deep learning technologies with a variety of data for enhancing public safety and livability in cites
Shayan Shams, Sayan Goswami, Kisung lee, Seungwon Yang, Seung-Jong Park
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GPU-Accelerated Large-Scale Genome Assembly
A new GPU-accelerated genome assembler called LaSAGNA, which can assemble large-scale sequence datasets using a single GPU by building string graphs from approximate all-pair overlaps.
Sayan Goswami, Kisung lee, Shayan Shams, Seung-Jong Park
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Augmenting Amdahl's Second Law: A Theoretical Model to Build Cost-Effective Balanced HPC Infrastructure for Data-Driven Science
A new system model augmenting Amdahl’s second law for balanced system to optimize price-performance-ratio of HPC clusters.
Arghya Kusum Das,, Jaeki Hong,, Sayan Goswami, Richard Platania, Kisung lee, Wooseok Chang, Seung-Jong Park, Ling Liu
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Lazer: Distributed memory-efficient assembly of large-scale genomes
A scalable and memory-efficient distributed assembler that uses partitioned de Bruijn graphs to improve memory-to-disk swapping and reduce the network communication enabling the assembly of larger datasets on fewer nodes.
Sayan Goswami, Arghya Kusum Das,, Richard Płatania, Kisung lee, Seung-Jong Park
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