SEAD virtual archive: Building a federation of institutional repositories for long-term data preservation in sustainability science B Plale, RH McDonald, K Chandrasekar, I Kouper, S Konkiel, ... | 31 | 2013 |
Fine-grained energy efficiency using per-core dvfs with an adaptive runtime system B Acun, K Chandrasekar, LV Kale 2019 Tenth International Green and Sustainable Computing Conference (IGSC), 1-8, 2019 | 14 | 2019 |
A memory heterogeneity-aware runtime system for bandwidth-sensitive HPC applications K Chandrasekar, X Ni, LV Kale 2017 IEEE International Parallel and Distributed Processing Symposium …, 2017 | 11 | 2017 |
The Charm++ parallel programming system L Kalé, B Acun, S Bak, A Becker, M Bhandarkar, N Bhat, A Bhatele, ... Aug, 2019 | 10 | 2019 |
Scalable GW software for quasiparticle properties using OpenAtom M Kim, S Mandal, E Mikida, K Chandrasekar, E Bohm, N Jain, Q Li, ... Computer Physics Communications 244, 427-441, 2019 | 9 | 2019 |
Effectiveness of hybrid workflow systems for computational science B Plale, EC Withana, C Herath, K Chandrasekar, Y Luo Procedia Computer Science 9, 508-517, 2012 | 9 | 2012 |
POSTER: automated load balancer selection based on application characteristics H Menon, K Chandrasekar, LV Kale Proceedings of the 22nd ACM SIGPLAN Symposium on Principles and Practice of …, 2017 | 8 | 2017 |
Task characterization-driven scheduling of multiple applications in a task-based runtime K Chandrasekar, B Seshasayee, A Gavrilovska, K Schwan Proceedings of the First International Workshop on Extreme Scale Programming …, 2015 | 8 | 2015 |
Middleware alternatives for storm surge predictions in Windows Azure K Chandrasekar, M Pathirage, S Wijeratne, C Mattocks, B Plale Proceedings of the 3rd workshop on Scientific Cloud Computing, 3-12, 2012 | 7 | 2012 |
Fault tolerance in OpenSPARC multicore architecture using core virtualization K Chandrasekar, R Ananthachari, S Seshadri, R Parthasarathi International Conference on High Performance Computing, 2008 | 5 | 2008 |
Improving data reuse in co-located applications with progress-driven scheduling K Chandrasekar, B Seshasayee, A Gavrilovska, K Schwan RESPA Workshop, co-located with SC 15, 2015 | 4 | 2015 |
Runtime Techniques for Programming with Fast and Slow Memory X Ni, N Jain, K Chandrasekar, LV Kale 2017 IEEE International Conference on Cluster Computing (CLUSTER), 147-151, 2017 | 3 | 2017 |
Strengths and weaknesses of sub-workflow interoperability B Plale, EC Withana, C Herath, K Chandrasekar, Y Luo, F Terkhorn Tech. Rep. TR700, 2011 | 2 | 2011 |
The SEAD datanet prototype: Data preservation services for sustainability science B Plale, RH McDonald, K Chandrasekar, I Kouper, R Light, SR Konkiel, ... Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries …, 2013 | 1 | 2013 |
escience workflows 9 years out: Converging on a vision B Plale, G Fox, S Kowalczyk, K Chandrasekar Tech. rep., Pervasive Technology Institute, Indiana University, Bloomington …, 2011 | 1 | 2011 |
OpenAtom: massively-parallel simulations for molecular and electronic dynamics M Kim, S Mandal, E Mikida, K Chandrasekar, Q Li, E Bohm, N Jain, L Kale, ... APS March Meeting Abstracts 2019, C22. 001, 2019 | | 2019 |
Imaginary time, shredded propagator method for large-scale GW calculations M Kim, S Mandal, E Mikida, K Chandrasekar, E Bohm, N Jain, Q Li, L Kale, ... APS March Meeting Abstracts 2018, A29. 005, 2018 | | 2018 |
Reduced order polarizability method for large scale GW calculations M Kim, S Mandal, E Mikida, K Chandrasekar, E Bohm, N Jain, L Kale, ... APS March Meeting Abstracts 2017, E7. 011, 2017 | | 2017 |
Executing Storm Surge Ensembles on PAAS Cloud A Chakraborty, M Pathirage, I Suriarachchi, K Chandrasekar, C Mattocks, ... Cloud Computing for Data-Intensive Applications, 257-276, 2014 | | 2014 |
Meta-Balancer: Automating Load Balancing Decisions H Menon, K Chandrasekar, LV Kale | | |