Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Aditya Devarakonda"'
Autor:
Tyler Beauchamp, Aditya Devarakonda, Varsha Chiruvella, Jessica Hatch, Patrick Lorenz, Lisa Renee Hilton
Publikováno v:
HPHR Journal.
Background Application of the fundamental principles of nutrition and exercise in clinical management can improve the outcomes of highly fatal diseases. The purpose of this study is to assess whether a knowledge gap in preventative measures of nutrit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a44cf1a9b26e2abf8e2cded9b4790694
https://doi.org/10.54111/0001/kkk3
https://doi.org/10.54111/0001/kkk3
Autor:
James Demmel, Zachary Blanco, Maryam Mehri Dehnavi, Mert Gurbuzbalaban, Aditya Devarakonda, Saeed Soori
Publikováno v:
ICPP
Proximal Newton methods are iterative algorithms that solve l1-regularized least squares problems. Distributed-memory implementation of these methods have become popular since they enable the analysis of large-scale machine learning problems. However
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::71d07217f70cd13838b95dea6eaff7bf
https://doi.org/10.1145/3225058.3225131
https://doi.org/10.1145/3225058.3225131
Publikováno v:
IPDPS
Parallel computing has played an important role in speeding up convex optimization methods for big data analytics and large-scale machine learning (ML). However, the scalability of these optimization methods is inhibited by the cost of communicating
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::623f7dad62ef6f2ce73ec7e6e013d2e3
http://arxiv.org/abs/1712.06047
http://arxiv.org/abs/1712.06047
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
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
https://doi.org/10.1109/bigdata.2016.7840606
Publikováno v:
Computing in Science & Engineering. 15:10-18
Clouds are rapidly joining high-performance computing (HPC) systems, clusters, and grids as viable platforms for scientific exploration and discovery. As a result, understanding application formulations and usage modes that are meaningful in such a h
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::19d3fc8fee37af9dc1f96f63714878c2
https://doi.org/10.1109/mcse.2013.49
https://doi.org/10.1109/mcse.2013.49
Primal and dual block coordinate descent methods are iterative methods for solving regularized and unregularized optimization problems. Distributed-memory parallel implementations of these methods have become popular in analyzing large machine learni
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c2c0a40b53204f399ba34eb39368f1ab
Autor:
James Demmel, Jay Alameda, Razvan Carbunescu, Susan Mehringer, Steven I. Gordon, Aditya Devarakonda
Publikováno v:
XSEDE
As parallel computing grows and becomes an essential part of computer science, tools must be developed to help grade assignments for large courses, especially with the prevalence of Massive Open Online Courses (MOOCs) increasing in recent years. This
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::287fdb15683c3ddad4215bdf708e4cb9
https://doi.org/10.1145/2616498.2616571
https://doi.org/10.1145/2616498.2616571
Autor:
Yanbin Liu, Ivan Rodero, S. Masoud Sadjadi, Aditya Devarakonda, Javier Delgado, David Villegas, Norman Bobroff, Manish Parashar, Liana Fong
Publikováno v:
Journal of Computer and System Sciences. (5):1330-1344
We show how a layered Cloud service model of software (SaaS), platform (PaaS), and infrastructure (IaaS) leverages multiple independent Clouds by creating a federation among the providers. The layered architecture leads naturally to a design in which
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::efe3dbb2e289bc5ecacfa7e29e96f812