I am your standard off-the-shelf HPC guy, that is currently roaming the embedded space at Bosch Chassis Systems Control.
I got my Ph.D. for my research on programming models for future HPC systems. My research has been centered around easing parallel programming mostly with a focus on programming models and high performance computing. I am currently a software architect in the area of driver assistance and automated driving.
Patent: DE102021208819A1
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Patent: US20230226970A1
Status: pending; also registered as DE102022200486 and CN116466617A
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Patent: US20210273788A1
Status: active; also registered as DE102020202594 and CN113326486A
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Patent: DE102019220220A1
Status: pending
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Patent: DE102019217416A1
Status: pending
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Patent: US20210397353A1
Status: pending; also registered as DE102020207616A1 and CN113823341A
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High Performance Computing
pp. 331–342
DOI: 10.1007/978-3-030-59851-8_22
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Operating Systems for Supercomputers and High Performance Computing
DOI: 10.1007/978-981-13-6624-6_20
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Proceedings of the 10th Workshop on Programming Languages and Operating Systems
PLOS’19
pp. 8–15
DOI: 10.1145/3365137.3365395
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Concurrency and Computation: Practice and Experience
Volume 30
Patent: 9
DOI: 10.1002/cpe.4412
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2018 IEEE 32nd International Parallel and Distributed Processing Symposium workshops: IPDPSW 2018
pp. 1279-1280
DOI: 10.1109/IPDPSW.2018.00198
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2017 IEEE International Conference on Cluster Computing (CLUSTER)
pp. 400–409
DOI: 10.1109/CLUSTER.2017.59
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Co-Scheduling of HPC Applications
pp. 114–141
DOI: 10.3233/978-1-61499-730-6-114
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Co-Scheduling of HPC Applications
pp. 69–94
DOI: 10.3233/978-1-61499-730-6-69
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Proceedings of the Joined Workshops COSH 2017 and VisorHPC 2017
pp. 27–32
DOI: 10.14459/2017md1344415
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Architecture of Computing Systems - ARCS 2017: 30th International Conference, Vienna, Austria, April 3–6, 2017
pp. 73–84
DOI: 10.1007/978-3-319-54999-6_6
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Euro-Par 2016: Parallel Processing Workshops, Revised Selected Papers
pp. 721–733
DOI: 10.1007/978-3-319-58943-5_58
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Job Scheduling Strategies for Parallel Processing: 19th and 20th International Workshops
pp. 141–157
DOI: 10.1007/978-3-319-61756-5_8
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2016 International Conference on High Performance Computing Simulation (HPCS)
pp. 318-325
DOI: 10.1109/HPCSim.2016.7568352
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Proceedings of the 6th International Workshop on Runtime and Operating Systems for Supercomputers
ROSS ’16
pp. 4:1–4:8
DOI: 10.1145/2931088.2931093
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Procedia Computer Science
Volume 80
pp. 1439–1449
International Conference on Computational Science 2016, ICCS 2016, 6-8 Jun 2016, San Diego, California, USA
DOI: 10.1016/j.procs.2016.05.464
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Proceedings of the 2016 IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum
DOI: 10.1109/IPDPSW.2016.149
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Proceedings of the 1st COSH Workshop on Co-Scheduling of HPC Applications
pp. 6
DOI: 10.14459/2016md1320971
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Proceedings of the 1st COSH Workshop on Co-Scheduling of HPC Applications
pp. 19
DOI: 10.14459/2016md1286951
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44th International Conference on Parallel Processing Workshops (ICPPW)
pp. 277-285
DOI: 10.1109/ICPPW.2015.38
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Parallel Processing and Applied Mathematics
Volume 8385
of the series Lecture Notes in Computer Science
Lecture Notes in Computer Science
pp. 75-85
DOI: 10.1007/978-3-642-55195-6_7
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Proceedings of the 2014 IEEE International Parallel & Distributed Processing Symposium Workshops
IPDPSW ’14
pp. 717–726
DOI: 10.1109/IPDPSW.2014.83
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Ph.D. thesis, Universität Kassel
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Applied Parallel and Scientific Computing
Volume 7134
of the series Lecture Notes in Computer Science
Lecture Notes in Computer Science
pp. 291-301
DOI: 10.1007/978-3-642-28145-7_29
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Proceedings of the 2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops & PhD Forum
IPDPSW ’12
pp. 762–769
DOI: 10.1109/IPDPSW.2012.94
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Euro-Par 2010 Parallel Processing Workshops
Volume 6586
of the series Lecture Notes in Computer Science
Lecture Notes in Computer Science
pp. 373-380
DOI: 10.1007/978-3-642-21878-1_46
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Proceedings of the 2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and PhD Forum
IPDPSW ’11
pp. 693–699
DOI: 10.1109/IPDPS.2011.193
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The Open Astronomy Journal
Volume 4
pp. 154–161
DOI: 10.2174/1874381101004010154
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24th IEEE International Symposium on Parallel and Distributed Processing,
IPDPS 2010, Atlanta, Georgia, USA, 19-23 April 2010 - Workshop Proceedings
pp. 1–8
DOI: 10.1109/IPDPSW.2010.5470823
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Parallel Processing and Applied Mathematics
Volume 6067
of the series Lecture Notes in Computer Science
Lecture Notes in Computer Science
pp. 486-495
DOI: 10.1007/978-3-642-14390-8_51
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International Conference on High Performance Computing Simulation
pp. 44-51
DOI: 10.1109/HPCSIM.2009.5192780
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Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
IPDPS ’09
pp. 1–8
DOI: 10.1109/IPDPS.2009.5160937
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AIP Conference Proceedings
Volume 1168
pp. 1065-1067
Patent: 1
DOI: 10.1063/1.3241237
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Most modern algorithms require a lot of specific data during its development phase. This data is intended to be collected in the field by cars that are driven by end-customers and upload it to online storage for further processing.
Under NDA.
HermitCore is a novel unikernel operating system targeting a scalable and predictable runtime behavior for HPC and cloud environments. It extends the multi-kernel approach and combines it with unikernel features.
Adopt the existing multipurpose camera for cars to work well with Daimler trucks and other similar comercial vehicles. It provides semi-automated driving (Level 2) functions like lane keeping and object and pedestrian detection.
PSAM is an experimental PGAS implementation designed to provide allow fine-grained synchronization and efficient remote memory caching mechanism.
FaST deals with the temporal and spatial placement of processes on high performance computers of the future. We develop autopin+, a tool to optimize the local placement within a node.
GASPI is a Partitioned Global Address Space (PGAS) API for C, C++ and Fortran. Its communication mechanism is based on one-sided RDMA transfers with a notification mechanism.
CuPP is a framework that allows easy integration of CUDA into C++ applications. It can automatically serialize and deserialize data structures and transfer them between the CPU and GPU.
Certificate. You can also take a look at the solution in my GitHub repositories.
Certificate. You can also take a look at the solution in my GitHub repositories.
Programming Models for Next Generation HPC Systems.
Case Studies on GPU Usage and Data Structure Design.
A Framework for Easy CUDA Integration in C++ Applications.