Updated on 2025/10/26

写真a

 
MAGNUSSEN, Birk Martin
 
Affiliation
Research Organization, Green Computing Systems Research Organization
Job title
Junior Researcher(Assistant Professor)
 

Papers

  • Using Machine Learning for Optical Spectroscopy Data Analysis

    Birk Martin Magnussen

       2024.11  [Refereed]

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    Zugleich: Dissertation, Universität Kassel, 2024

    DOI

  • S-MSRRS5000: A Simulated Dataset Highlighting the Challenges of Data Obtained From Multiple Spatially Resolved Reflection Spectroscopy

    Birk Martin Magnussen, Maik Jessulat, Claudius Stern, Bernhard Sick

       2024.04

    DOI

  • Optical Detection of the Body Mass Index and Related Parameters Using Multiple Spatially Resolved Reflection Spectroscopy

    Birk Martin Magnussen, Frank Möckel, Maik Jessulat, Claudius Stern, Bernhard Sick

    2024 12th International Conference on Bioinformatics and Computational Biology (ICBCB)     132 - 136  2024.03  [Refereed]

    DOI

  • Adaptive Shapley: Using Explainable AI with Large Datasets to Quantify the Impact of Arbitrary Error Sources

    Birk Martin Magnussen, Maik Jessulat, Claudius Stern, Bernhard Sick

    2024 9th International Conference on Big Data Analytics (ICBDA)     305 - 310  2024.03  [Refereed]

    DOI

  • Continuous Feature Networks: A Novel Method to Process Irregularly and Inconsistently Sampled Data With Position-Dependent Features

    Birk Martin Magnussen, Claudius Stern, Bernhard Sick

    International Journal On Advances in Intelligent Systems   16 ( 3\&4 ) 43 - 50  2023.12  [Refereed]  [Invited]

    DOI

  • Leveraging Repeated Unlabelled Noisy Measurements to Augment Supervised Learning

    Birk Martin Magnussen, Claudius Stern, Bernhard Sick

    Proceedings of the 2023 6th International Conference on Computational Intelligence and Intelligent Systems     1 - 6  2023.11  [Refereed]

    DOI

  • Intra-Model Smoothing Using Depth Aware Multi-Sample Anti-Aliasing for Deferred Rendering Pipelines

    Birk Martin Magnussen

    Computer Graphics & Visual Computing (CGVC) 2023    2023.09  [Refereed]  [International journal]

    DOI

  • Utilizing Continuous Kernels for Processing Irregularly and Inconsistently Sampled Data With Position-Dependent Features

    Birk Martin Magnussen, Claudius Stern, Bernhard Sick

    Proceedings of The Nineteenth International Conference on Autonomic and Autonomous Systems     49 - 53  2023.03  [Refereed]

    DOI

  • Performance Evaluation of OSCAR Multi-target Automatic Parallelizing Compiler on Intel, AMD, Arm and RISC-V Multicores

    Birk Martin Magnussen, Tohma Kawasumi, Hiroki Mikami, Keiji Kimura, Hironori Kasahara

    Languages and Compilers for Parallel Computing     50 - 64  2022  [Refereed]

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    With an increasing number of shared memory multicore processor architectures, there is a requirement for supporting multiple architectures in automatic parallelizing compilers. The OSCAR (Optimally Scheduled Advanced Multiprocessor) automatic parallelizing compiler is able to parallelize many different sequential programs, such as scientific applications, embedded real-time applications, multimedia applications, and more. OSCAR compiler’s features include coarse-grain task parallelization with earliest execution condition analysis, analyzing both data and control dependencies, data locality optimizations over different loop nests with data dependencies, and the ability to generate parallelized code using the OSCAR API 2.1. The OSCAR API 2.1 is compatible with OpenMP for SMP multicores, with additional directives for power control and supporting heterogeneous multicores. This allows for a C or Fortran compiler with OpenMP support to generate parallel machine code for the target multicore. Additionally, using the OSCAR API analyzer allows a sequential-only compiler without OpenMP support to generate machine code for each core separately, which is then linked to one parallel application. Overall, only little configuration changes to the OSCAR compiler are needed to run and optimize OSCAR compiler-generated code on a specific platform. This paper evaluates the performance of OSCAR compiler-generated code on different modern SMP multicore processors, including Intel and AMD x86 processors, an Arm processor, and a RISC-V processor using scientific and multimedia benchmarks in C and Fortran. The results show promising speedups on all platforms, such as a speedup of 7.16 for the swim program of the SPEC2000 benchmarks on an 8-core Intel x86 processor, a speedup of 9.50 for the CG program of the NAS parallel benchmarks on 8 cores of an AMD x86 Processor, a speedup of 3.70 for the BT program of the NAS parallel benchmarks on a 4-core RISC-V processor, and a speedup of 2.64 for the equake program of the SPEC2000 benchmarks on 4 cores of an Arm processor.

    DOI

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