Ioan Raicu, Ph.D.
Affiliations: | 2009 | University of Chicago, Chicago, IL |
Area:
Computer ScienceGoogle:
"Ioan Raicu"Parents
Sign in to add mentor| Ian T. Foster | grad student | 2009 | Chicago | |
| (Many-task computing: Bridging the gap between high-throughput computing and high-performance computing.) | ||||
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Publications
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| Sadooghi I, Martin JH, Li T, et al. (2017) Understanding the Performance and Potential of Cloud Computing for Scientific Applications Ieee Transactions On Cloud Computing. 5: 358-371 |
| Orhean AI, Pop F, Raicu I. (2017) New scheduling approach using reinforcement learning for heterogeneous distributed systems Journal of Parallel and Distributed Computing. 117: 292-302 |
| Valero-Lara P, Nookala P, Pelayo FL, et al. (2016) Many-task computing on many-core architectures Scalable Computing. 17: 33-46 |
| Zhao D, Qiao K, Yin J, et al. (2016) Dynamic Virtual Chunks: On Supporting Efficient Accesses to Compressed Scientific Data Ieee Transactions On Services Computing. 9: 96-109 |
| Zhao D, Liu N, Kimpe D, et al. (2016) Towards Exploring Data-Intensive Scientific Applications at Extreme Scales through Systems and Simulations Ieee Transactions On Parallel and Distributed Systems. 27: 1824-1837 |
| Wang K, Kulkarni A, Lang M, et al. (2016) Exploring the design tradeoffs for extreme-scale high-performance computing system software Ieee Transactions On Parallel and Distributed Systems. 27: 1070-1084 |
| Sadooghi I, Wang K, Patel D, et al. (2016) FaBRiQ: Leveraging Distributed Hash Tables towards Distributed Publish-Subscribe Message Queues Proceedings - 2015 2nd Ieee/Acm International Symposium On Big Data Computing, Bdc 2015. 11-20 |
| Zhao D, Wang K, Qiao K, et al. (2016) Toward high-performance key-value stores through GPU encoding and locality-aware encoding Journal of Parallel and Distributed Computing. 96: 27-37 |
| Zhao D, Qiao K, Zhou Z, et al. (2016) Exploiting multi-cores for efficient interchange of large messages in distributed systems Concurrency Computation |
| Wang K, Qiao K, Sadooghi I, et al. (2016) Load-balanced and locality-aware scheduling for data-intensive workloads at extreme scales Concurrency Computation. 28: 70-94 |