Zobrazeno 1 - 10
of 32
pro vyhledávání: '"Jatin Chhugani"'
Autor:
Mikhail Smelyanskiy, Jatin Chhugani, Changkyu Kim, Nadathur Satish, Hideki Saito, Pradeep Dubey, Milind B. Girkar, Rakesh Krishnaiyer
Publikováno v:
ISCA
Current processor trends of integrating more cores with wider SIMD units, along with a deeper and complex memory hierarchy, have made it increasingly more challenging to extract performance from applications. It is believed by some that traditional a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::21ea640efe4a7c1c713fb1eaa82e114d
https://doi.org/10.1145/2742910
https://doi.org/10.1145/2742910
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
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
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
https://doi.org/10.1109/ipdpsw.2016.114
Autor:
Venkatraman Govindaraju, Karthikeyan Sankaralingam, Nadathur Satish, Chen-Han Ho, Changkyu Kim, Jatin Chhugani, Tony Nowatzki
Publikováno v:
IEEE Micro. 32:38-51
The DySER (Dynamically Specializing Execution Resources) architecture supports both functionality specialization and parallelism specialization. By dynamically specializing frequently executing regions and applying parallelism mechanisms, DySER provi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9d4e6ce081c8b181fca709d7c8a0a6f1
https://doi.org/10.1109/mm.2012.51
https://doi.org/10.1109/mm.2012.51
Autor:
Victor W. Lee, Eric Sedlar, Pradeep Dubey, Changkyu Kim, Jatin Chhugani, Tim Kaldewey, Nadathur Satish, Anthony D. Nguyen, Scott A. Brandt
Publikováno v:
ACM Transactions on Database Systems. 36:1-34
In-memory tree structured index search is a fundamental database operation. Modern processors provide tremendous computing power by integrating multiple cores, each with wide vector units. There has been much work to exploit modern processor architec
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::26056f6baccd3fbd9de4e490f10ee363
https://doi.org/10.1145/2043652.2043655
https://doi.org/10.1145/2043652.2043655
Autor:
Changkyu Kim, Martin Grund, Hasso Plattner, David Schwalb, Jatin Chhugani, Alexander Zeier, Nadathur Satish, Pradeep Dubey, Jens Krueger
Publikováno v:
Proceedings of the VLDB Endowment. 5:61-72
Read-optimized columnar databases use differential updates to handle writes by maintaining a separate write-optimized delta partition which is periodically merged with the read-optimized and compressed main partition. This merge process introduces si
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7e61b267442334623006d67af14148f5
https://doi.org/10.14778/2047485.2047491
https://doi.org/10.14778/2047485.2047491
Publikováno v:
Proceedings of the VLDB Endowment. 4:795-806
Concurrency control on B + trees is primarily achieved with latches, but serialization and contention can hinder scalability. As core counts on current processors increase, it is imperative to develop scalable latch-free techniques for concurrency co
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::90755e2fb325debc0501dd778b5506c1
https://doi.org/10.14778/3402707.3402719
https://doi.org/10.14778/3402707.3402719
Autor:
Jatin Chhugani, Anthony D. Nguyen, Victor W. Lee, Tim Kaldewey, Eric Sedlar, Andrea Di Blas, Nadathur Satish, Changkyu Kim, Pradeep Dubey
Publikováno v:
Proceedings of the VLDB Endowment. 2:1378-1389
Join is an important database operation. As computer architectures evolve, the best join algorithm may change hand. This paper re-examines two popular join algorithms -- hash join and sort-merge join -- to determine if the latest computer architectur
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4e07d5b53a46524fbac30894c033f750
https://doi.org/10.14778/1687553.1687564
https://doi.org/10.14778/1687553.1687564
Autor:
Jatin Chhugani, Anthony-Trung D. Nguyen, Changkyu Kim, Daehyun Kim, Sanjeev Kumar, Pradeep Dubey, Christian Bienia, Youngmin Kim
Publikováno v:
Computer. 41:46-53
Unlike online games, metaverses present a single seamless, persistent world where users can transparently roam around without predefined objectives. An analysis of Second Life illustrates the demands such applications place on clients, servers, and t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::141fed4f8160e6530d0134c4450ef343
https://doi.org/10.1109/mc.2008.398
https://doi.org/10.1109/mc.2008.398
Autor:
Daehyun Kim, Christopher J. Hughes, Mikhail Smelyanskiy, Yen-Kuang Chen, Anthony-Trung D. Nguyen, Pradeep Dubey, Victor W. Lee, Jatin Chhugani, Sanjeev Kumar
Publikováno v:
Proceedings of the IEEE. 96:790-807
This paper examines the growing need for a general-purpose ldquoanalytics enginerdquo that can enable next-generation processing platforms to effectively model events, objects, and concepts based on end-user input, and accessible datasets, along with
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3fcb032354f754d1ebfa14f2a959cceb
https://doi.org/10.1109/jproc.2008.917729
https://doi.org/10.1109/jproc.2008.917729