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20202026
most citedThe Parallelism Motifs of Genomic Data Analysis

19 citations · 54 across the 9 of their papers we have counts for

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Showing 2020Show all

6 papers · 1 filter

cs.DC2020★ 14 cited

10 Years Later: Cloud Computing is Closing the Performance Gap

Giulia Guidi, Marquita Ellis, Aydin Buluc +2

Can cloud computing infrastructures provide HPC-competitive performance for scientific applications broadly? Despite prolific related literature, this question remains open. Answer…

cs.DC2020★ 1 cited

Parallel String Graph Construction and Transitive Reduction for De Novo Genome Assembly

Giulia Guidi, Oguz Selvitopi, Marquita Ellis +3

One of the most computationally intensive tasks in computational biology is de novo genome assembly, the decoding of the sequence of an unknown genome from redundant and erroneous…

cs.DC2020

Distributed Many-to-Many Protein Sequence Alignment using Sparse Matrices

Oguz Selvitopi, Saliya Ekanayake, Giulia Guidi +3

Identifying similar protein sequences is a core step in many computational biology pipelines such as detection of homologous protein sequences, generation of similarity protein gra…

q-bio.GN2020

LOGAN: High-Performance GPU-Based X-Drop Long-Read Alignment

Alberto Zeni, Giulia Guidi, Marquita Ellis +6

Pairwise sequence alignment is one of the most computationally intensive kernels in genomic data analysis, accounting for more than 90% of the runtime for key bioinformatics applic…

cs.DC2020★ 17 cited

diBELLA: Distributed Long Read to Long Read Alignment

Marquita Ellis, Giulia Guidi, Aydın Buluç +2

We present a parallel algorithm and scalable implementation for genome analysis, specifically the problem of finding overlaps and alignments for data from "third generation" long r…

cs.DC2020★ 19 cited

The Parallelism Motifs of Genomic Data Analysis

Katherine Yelick, Aydin Buluc, Muaaz Awan +11

Genomic data sets are growing dramatically as the cost of sequencing continues to decline and small sequencing devices become available. Enormous community databases store and shar…