Thilo Kielmann

dr. ing.

19992018
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Grid Mathematics
Scheduling Engineering & Materials Science
Application programming interfaces (API) Engineering & Materials Science
Data storage equipment Engineering & Materials Science
High Performance Mathematics
Network protocols Engineering & Materials Science
Middleware Engineering & Materials Science
Cluster computing Engineering & Materials Science

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Research Output 1999 2018

MemEFS: A network-aware elastic in-memory runtime distributed file system

Uta, A., Danner, O., van der Weegen, C., Oprescu, A. M., Sandu, A., Costache, S. & Kielmann, T., May 2018, In : Future Generation Computer Systems. 82, May, p. 631-646 16 p.

Research output: Contribution to JournalArticleAcademicpeer-review

Data storage equipment
Elasticity
Astronomy
Bioinformatics
Bandwidth

Optimizing the execution of many-task computing applications using in-memory distributed file systems

Uta, A., 2017, 144 p.

Research output: PhD ThesisPhD Thesis - Research VU, graduation VUAcademic

Open Access
File

Overcoming data locality: An in-memory runtime file system with symmetrical data distribution

Uta, A., Sandu, A. & Kielmann, T., 2016, In : Future Generation Computer Systems. 54, p. 144-158

Research output: Contribution to JournalArticleAcademicpeer-review

Data storage equipment
Scalability
Hash functions
Ethernet
Bandwidth

Towards resource disaggregation - Memory scavenging for scientific workloads

Uta, A., Oprescu, A. M. & Kielmann, T., 6 Dec 2016, Proceedings - 2016 IEEE International Conference on Cluster Computing, CLUSTER 2016. Institute of Electrical and Electronics Engineers, Inc., p. 100-109 10 p. 7776483

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Scavenging
Data storage equipment
Bandwidth
Experiments

E-BaTS: Energy-aware Scheduling for Bag-of-Task Applications in HPC Clusters

Filip, A., Oprescu, A. M., Costache, S. V. & Kielmann, T., 2015, In : Parallel Processing Letters. 25, 3, 32 p.

Research output: Contribution to JournalArticleAcademicpeer-review

Cluster computing
Cluster Computing
Energy utilization
High Performance
Scheduling