TY - GEN
T1 - Kernels for scalable data analysis in science
T2 - 4th IEEE International Conference on Big Data, Big Data 2016
AU - Sukumar, Sreenivas R.
AU - Kannan, Ramakrishnan
AU - Lim, Seung Hwan
AU - Matheson, Michael A.
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016
Y1 - 2016
N2 - In this paper, we pose and address some of the unique challenges in the analysis of scientific Big Data on supercomputing platforms. Our approach identifies, implements and scales numerical kernels that are critical to the instantiation of theory-inspired analytic workflows on modern computing architectures. We present the benefits of scalable kernels towards constructing algorithms such as principal component analysis and non-negative matrix factorization on an image-analysis use case at the Oak Ridge Leadership Computing Facility (OLCF). Based on experience with the use-case, we conclude that piecing scalable analytic kernels into user-defined analytic workflows are a flexible, modular and agile way to enable architecture-portable productivity for the data-intensive sciences.
AB - In this paper, we pose and address some of the unique challenges in the analysis of scientific Big Data on supercomputing platforms. Our approach identifies, implements and scales numerical kernels that are critical to the instantiation of theory-inspired analytic workflows on modern computing architectures. We present the benefits of scalable kernels towards constructing algorithms such as principal component analysis and non-negative matrix factorization on an image-analysis use case at the Oak Ridge Leadership Computing Facility (OLCF). Based on experience with the use-case, we conclude that piecing scalable analytic kernels into user-defined analytic workflows are a flexible, modular and agile way to enable architecture-portable productivity for the data-intensive sciences.
KW - Big Data
KW - analytical motifs
KW - data analysis kernels
KW - high performance data analytics
KW - mini-apps
UR - http://www.scopus.com/inward/record.url?scp=85015161333&partnerID=8YFLogxK
U2 - 10.1109/BigData.2016.7840703
DO - 10.1109/BigData.2016.7840703
M3 - Conference contribution
AN - SCOPUS:85015161333
T3 - Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016
SP - 1026
EP - 1031
BT - Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016
A2 - Ak, Ronay
A2 - Karypis, George
A2 - Xia, Yinglong
A2 - Hu, Xiaohua Tony
A2 - Yu, Philip S.
A2 - Joshi, James
A2 - Ungar, Lyle
A2 - Liu, Ling
A2 - Sato, Aki-Hiro
A2 - Suzumura, Toyotaro
A2 - Rachuri, Sudarsan
A2 - Govindaraju, Rama
A2 - Xu, Weijia
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 5 December 2016 through 8 December 2016
ER -