Výsledky vyhledávání - "Jey Kottalam"

Alchemist: An Apache Spark ⇔ MPI interface

Autor: Michael F. Ringenburg, Kristyn Maschhoff, Alex Gittens, Kai Rothauge, Michael W. Mahoney, L. Gerhardt, Shusen Wang, Prabhat, Jey Kottalam

Publikováno v: Concurrency and Computation: Practice and Experience.

The Apache Spark framework for distributed computation is popular in the data analytics community due to its ease of use, but its MapReduce-style programming model can incur significant overheads when performing computations that do not map directly

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fafdee7667a2dd1e0b2dd5b57d6d4ee6
https://doi.org/10.1002/cpe.5026

Zobrazit plný text záznamu

Accelerating Large-Scale Data Analysis by Offloading to High-Performance Computing Libraries using Alchemist

Autor: Kai Rothauge, Michael W. Mahoney, Jey Kottalam, L. Gerhardt, Michael F. Ringenburg, Alex Gittens, Kristyn Maschhoff, Prabhat, Shusen Wang

Publikováno v: KDD

Apache Spark is a popular system aimed at the analysis of large data sets, but recent studies have shown that certain computations---in particular, many linear algebra computations that are the basis for solving common machine learning problems---are

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3707e0b9de495c56b6008d091d266704
http://arxiv.org/abs/1805.11800

Zobrazit plný text záznamu

Matrix factorizations at scale: A comparison of scientific data analytics in spark and C+MPI using three case studies

Autor: Evan Racah, Michael W. Mahoney, Jatin Chhugani, Jim Harrell, Jey Kottalam, Jialin Liu, Shane Canon, Pramod Sharma, Michael F. Ringenburg, James Demmel, Jiyan Yang, Prabhat, Aditya Devarakonda, Kristyn Maschhoff, Alex Gittens, L. Gerhardt, Venkat Krishnamurthy

Publikováno v: IEEE BigData
Gittens, Alex; Devarakonda, Aditya; Racah, Evan; Ringenburg, Michael; Gerhardt, Lisa; Kottalam, Jey; et al.(2016). Matrix Factorizations at Scale: a Comparison of Scientific Data Analytics in Spark and C+MPI Using Three Case Studies:. Lawrence Berkeley National Laboratory: Lawrence Berkeley National Laboratory. Retrieved from: http://www.escholarship.org/uc/item/13q8t1hg
Gittens, Alex; Devarakonda, Aditya; Racah, Evan; Ringenburg, Michael; Gerhardt, Lisa; Kottaalam, Jey; et al.(2016). Matrix Factorizations at Scale: a Comparison of Scientific Data Analytics in Spark and C+MPI Using Three Case Studies:. Lawrence Berkeley National Laboratory: Lawrence Berkeley National Laboratory. Retrieved from: http://www.escholarship.org/uc/item/2h15p99d

We explore the trade-offs of performing linear algebra using Apache Spark, compared to traditional C and MPI implementations on HPC platforms. Spark is designed for data analytics on cluster computing platforms with access to local disks and is optim

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7993a6588578f3c2f032af663634d3fa
https://doi.org/10.1109/bigdata.2016.7840606

Zobrazit plný text záznamu

A Multi-Platform Evaluation of the Randomized CX Low-Rank Matrix Factorization in Spark

Autor: Venkat Krishnamurthy, Evan Racah, Michael W. Mahoney, Norman G. Lewis, Prabhat, Jiyan Yang, Jatin Chhugani, Curt R. Fischer, Jey Kottalam, Benjamin P. Bowen, Mohitdeep Singh, Yushu Yao, Michael F. Ringenburg, Oliver Ruebel, Alex Gittens

Publikováno v: IPDPS Workshops
Gittens, Alex; Kottalam, Jey; Yang, Jiyan; Ringenburg, Michael, F.; Chhugani, Jatin; Racah, Evan; et al.(2016). A multi-platform evaluation of the randomized CX low-rank matrix factorization in Spark:. Lawrence Berkeley National Laboratory: Lawrence Berkeley National Laboratory. Retrieved from: http://www.escholarship.org/uc/item/6jh4m35v

We investigate the performance and scalability of the randomized CX low-rank matrix factorization and demonstrate its applicability through the analysis of a 1TB mass spectrometry imaging (MSI) dataset, using Apache Spark on an Amazon EC2 cluster, a

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::27791ae8905040f20ad2fd4d9c2aa794
https://doi.org/10.1109/ipdpsw.2016.114

Zobrazit plný text záznamu

Rethinking Data-Intensive Science Using Scalable Analytics Systems

Autor: Uri Laserson, Zhao Zhang, Frank Austin Nothaft, Anthony D. Joseph, Michael D. Linderman, Michael J. Franklin, Carl Yeksigian, Arun Ahuja, David A. Patterson, Jey Kottalam, Timothy Danford, Jeff Hammerbacher, Matt Massie

Publikováno v: SIGMOD Conference

"Next generation" data acquisition technologies are allowing scientists to collect exponentially more data at a lower cost. These trends are broadly impacting many scientific fields, including genomics, astronomy, and neuroscience. We can attack the

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::52095a75ce1926af947c63a0bc3c085a
https://doi.org/10.1145/2723372.2742787

Zobrazit plný text záznamu

MLI: An API for Distributed Machine Learning

Autor: Michael J. Franklin, Ameet Talwalkar, Joseph E. Gonzalez, Virginia Smith, Tim Kraska, Xinghao Pan, Jey Kottalam, Michael I. Jordan, Evan R. Sparks

Publikováno v: ICDM

MLI is an Application Programming Interface designed to address the challenges of building Machine Learn- ing algorithms in a distributed setting based on data-centric computing. Its primary goal is to simplify the development of high-performance, sc

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fb6e16c723703903e4dd7efa929fb7a5
https://doi.org/10.1109/icdm.2013.158

Zobrazit plný text záznamu

Vyhledávací nástroje:

Upřesnit hledání