Data-intensive computing

Résumé

Data-intensive computing is a class of parallel computing applications which use a data parallel approach to process large volumes of data typically terabytes or petabytes in size and typically referred to as big data. Computing applications which devote most of their execution time to computational requirements are deemed compute-intensive, whereas computing applications which require large volumes of data and devote most of their processing time to I/O and manipulation of data are deemed data-intensive. Introduction The rapid growth of the Internet and World Wide Web led to vast amounts of information available online. In addition, business and government organizations create large amounts of both structured and unstructured information which needs to be processed, analyzed, and linked. Vinton Cerf described this as an “information avalanche” and stated “we must harness the Internet’s energy before the information it has unleashed buries us”. An IDC white paper sponsor

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https://en.wikipedia.org/wiki/Data-intensive_computing

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Tools for parallel I/O and compute intensive applications

EPFL1999

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Interactivity on the Grid: Limitations and Opportunities

Marco Meoni

Grid computing has the intrinsic disadvantages of a batch system: the undetermined delay between the time a job is submitted and the time it is completed. Our approach to this problem is iGrid, a framework that proposes to change the Grid from a batch system to a more interactive distributed platform. Agents are deployed on nodes and wait to be contacted by an interactive job to start the computation, thus bypassing the regular Grid scheduler. A CPU fairshare mechanism allows users to get their fair iGrid machine share over a long period. Our prototype implementation scales to all 400 Grid nodes we were granted executing on top a software for data intensive parallel computing.

Acm Order Department, P O Box 64145, Baltimore, Md 21264 Usa2011

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Ultrafast homomorphic encryption models enable secure outsourcing of genotype imputation

Jean-Philippe Léonard Bossuat, David Jules Froelicher, Jean-Pierre Hubaux, Yiping Ma, Juan Ramón Troncoso-Pastoriza

Genotype imputation is a fundamental step in genomic data analysis, where missing variant genotypes are predicted using the existing genotypes of nearby "tag"variants. Although researchers can outsource genotype imputation, privacy concerns may prohibit genetic data sharing with an untrusted imputation service. Here, we developed secure genotype imputation using efficient homomorphic encryption (HE) techniques. In HE-based methods, the genotype data are secure while it is in transit, at rest, and in analysis. It can only be decrypted by the owner. We compared secure imputation with three state-of-the-art non-secure methods and found that HE-based methods provide genetic data security with comparable accuracy for common variants. HE-based methods have time and memory requirements that are comparable or lower than those for the non-secure methods. Our results provide evidence that HE-based methods can practically perform resource-intensive computations for high-throughput genetic data analysis. The source code is freely available for download at https://github.com/K-miran/secure-imputation.

CELL PRESS2021

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