Jan Laukemann
Jan Laukemann, M. Sc.
Short bio
Jan Laukemann is a PhD student at the University of Erlangen-Nürnberg (FAU) Erlangen and works for the National High Performance Computing Center (NHR@FAU). Previously he finished his Master’s at FAU and worked as a Research Scientist at Intel Parallel Computing Labs (Intel PCL). He focuses on application optimization and performance engineering for HPC systems and novel algorithms for scalable linear algebra, tensor decomposition and graph computations. His research interests primarily include x86 and non-x86 computer architectures, their performance behavior on the node level, and vectorization techniques. He is the main developer of the Open Source Architecture Code Analyzer (OSACA), a static in-core kernel analysis tool, and is part of the organization committee of the annual HPC-AI Advisory Council Student Cluster Competition at ISC High Performance.
Research fields
List of publications
2021
Execution-Cache-Memory modeling and performance tuning of sparse matrix-vector multiplication and Lattice quantum chromodynamics on A64FX
In: Concurrency and Computation-Practice & Experience (2021)
ISSN: 1532-0626
DOI: 10.1002/cpe.6512
URL: https://onlinelibrary.wiley.com/doi/full/10.1002/cpe.6512
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ALTO: Adaptive Linearized Storage of Sparse Tensors
ICS '21: 2021 International Conference on Supercomputing (Virtual Event, USA, 2021-06-14 - 2021-06-17)
DOI: 10.1145/3447818.3461703
URL: https://dl.acm.org/doi/10.1145/3447818.3461703
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2020
Performance Modeling of Streaming Kernels and Sparse Matrix-Vector Multiplication on A64FX
2020 IEEE/ACM Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems, PMBS 2020 (, 2020-11-12)
In: Proceedings of PMBS 2020: Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems 2020
DOI: 10.1109/PMBS51919.2020.00006
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2019
Automatic Throughput and Critical Path Analysis of x86 and ARM Assembly Kernels
10th IEEE/ACM Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems, PMBS 2019
DOI: 10.1109/PMBS49563.2019.00006
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