TY - CHAP
T1 - Bringing high performance computing to big data algorithms
AU - Anzt, H.
AU - Dongarra, J.
AU - Gates, M.
AU - Kurzak, J.
AU - Luszczek, P.
AU - Tomov, S.
AU - Yamazaki, I.
N1 - Publisher Copyright:
© Springer International Publishing AG 2017. All rights reserved.
PY - 2017/2/25
Y1 - 2017/2/25
N2 - Many ideas of High Performance Computing are applicable to Big Data problems. The more so now, that hybrid, GPU computing gains traction in mainstream computing applications. This work discusses the differences between the High Performance Computing software stack and the Big Data software stack and then focuses on two popular computing workloads, the Alternating Least Squares algorithm and the Singular Value Decomposition, and shows how their performance can be maximized using hybrid computing techniques.
AB - Many ideas of High Performance Computing are applicable to Big Data problems. The more so now, that hybrid, GPU computing gains traction in mainstream computing applications. This work discusses the differences between the High Performance Computing software stack and the Big Data software stack and then focuses on two popular computing workloads, the Alternating Least Squares algorithm and the Singular Value Decomposition, and shows how their performance can be maximized using hybrid computing techniques.
UR - http://www.scopus.com/inward/record.url?scp=85019958670&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-49340-4_23
DO - 10.1007/978-3-319-49340-4_23
M3 - Chapter
AN - SCOPUS:85019958670
SN - 9783319493398
SP - 777
EP - 806
BT - Handbook of Big Data Technologies
PB - Springer International Publishing
ER -