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Adjunct Professor

Bertil Schmidt

Adjunct Professor
PhD (Computer Science), Loughborough University, United Kingdom
Cluster:
Infocomm Technology
Email
LinkedIn
Website
Google Scholar

SIT Appointments

  • Adjunct Professor
    – Present

Education

  • PhD (Computer Science)
    Loughborough University , United Kingdom
  • Dipl.-Informatiker (M.Sc.)
    Universitiy of Kiel , Germany

Achievements

  • Best Paper Award IEEE HiPC 2020
  • Best Paper Finalist SC'20
  • Best Paper Award, IEEE ASAP 2015
  • CUDA Teaching Center Award
    –
  • CUDA Research Center (CRC) Award
    –
  • Best Paper Award, IEEE ASAP 2009
  • NVIDIA Professor Partnership Award
    –

Professional Memberships

  • Associate Editor, Frontiers in High Performance Computing
    – Present
  • Associate Editor, Journal of Parallel and Distributed Computing (JPDC)
    – Present
  • Editorial Board Member, Journal on Computational Science (Elsevier)
    – Present
  • IEEE Senior Member
    – Present

Research Interests

  • GPU Computing
  • High Performance Computing
  • Bioinformatics
  • Algorithm Acceleration
  • Concurrent Data Structures
  • Applied Machine Learning
  • Computational Science

Journal Papers

  • K Xu, J Zhang, X Duan, X Wan, N Huang, B Schmidt, W Liu, G Yang: Redesigning and Optimizing UCSF DOCK3.7 on Sunway TaihuLight,  IEEE Transactions on Parallel and Distributed Systems 33 (12), 2022, 4458-4471

  • Kallenborn, F., Cascitti, J., Schmidt, B. (2022). CARE 2.0: reducing false-positive sequencing error corrections using machine learning. BMC Bioinformatics, 23(1), 1-17.

  • Krüger, M., Wilson, J., Wietzoreck, M., Bandowe, B. A. M., Lammel, G., Schmidt, B., Poeschl, U., Berkemeier, T. (2022). Convolutional neural network prediction of molecular properties for aerosol chemistry and health effects. Natural Sciences, e20220016.

  • Bob, K., Teschner, D., Kemmer, T., Gomez-Zepeda, D., Tenzer, S., Schmidt, B., & Hildebrandt, A. (2022). Locality-sensitive hashing enables efficient and scalable signal classification in high-throughput mass spectrometry raw data. BMC Bioinformatics, 23(1), 1-16.

  • Meng, J., Zhuang, C., Chen, P., Wahib, M., Schmidt, B., Wang, X., Liu, W., Feng, S. (2022). Automatic Generation of High-Performance Convolution Kernels on ARM CPUs for Deep Learning. IEEE Transactions on Parallel and Distributed Systems, 33(11), 2885-2899.

  • Jünger, D., Kobus, R., Müller, A., Hundt, C., Xu, K., Liu, W., & Schmidt, B. (2022). General-purpose GPU hashing data structures and their application in accelerated genomics. Journal of Parallel and Distributed Computing, 163, 256-268.

  • Niebler, S., Miltenberger, A., Schmidt, B., & Spichtinger, P. (2022). Automated detection and classification of synoptic-scale fronts from atmospheric data grids. Weather and Climate Dynamics, 3(1), 113-137.

  • Zhang, H., Chang, Q., Yin, Z., Xu, X., Wei, Y., Schmidt, B., & Liu, W. (2022). RabbitV: fast detection of viruses and microorganisms in sequencing data on multi-core architectures. Bioinformatics, 38(10), 2932-2933.

  • Cascitti, J., Niebler, S., Müller, A., & Schmidt, B. (2022). RNACache: A scalable approach to rapid transcriptomic read mapping using locality sensitive hashing. Journal of Computational Science, 60, 101572.

  • Xu, K., Duan, X., Müller, A., Kobus, R., Schmidt, B., & Liu, W. (2022). FMapper: Scalable read mapper based on succinct hash index on SunWay TaihuLight. Journal of Parallel and Distributed Computing, 161, 72-82.

  • Gao, P., Duan, X., Schmidt, B., Zhang, W., Gan, L., Fu, H., Liu, W.& Yang, G. (2022). Optimization of reactive force field simulation: Refactor, parallelization, and vectorization for interactions. IEEE Transactions on Parallel and Distributed Systems, 33(2), 359-373.

  • F. Kallenborn, A. Hildebrandt, B. Schmidt: CARE: context-aware sequencing read error correction. Bioinformatics 37(7): 889-895 (2021)

  • B Schmidt, A Hildebrandt: Deep learning in next-generation sequencing. Drug Discovery Today 26 (1), 173-180

  • Zekun Yin, Hao Zhang, Meiyang Liu, Wen Zhang, Honglei Song, Haidong Lan, Yanjie Wei, Beifang Niu, Bertil Schmidt, Weiguo Liu: RabbitQC: high-speed scalable quality control for sequencing data. Bioinformatics 37(4): 573-574 (2021)

  • Zekun Yin, Xiaoming Xu, Jinxiao Zhang, Yanjie Wei, Bertil Schmidt, Weiguo Liu:
    RabbitMash: accelerating hash-based genome analysis on modern multi-core architectures. Bioinformatics 37(6): 873-875 (2021

  • Robin Kobus, José Manuel Abuín, André Müller, Sören Lukas Hellmann, Juan Carlos Pichel, Tomás F. Pena, Andreas Hildebrandt, Thomas Hankeln, Bertil Schmidt:
    A big data approach to metagenomics for all-food-sequencing. BMC Bioinformatics. 21(1): 102 (2020)

  • Winther, H. B., Gutberlet, M., Hundt, C., Kaireit, T. F., Alsady, T. M., Schmidt, B., Vogel‐Claussen, J. (2020). Deep semantic lung segmentation for tracking potential pulmonary perfusion biomarkers in chronic obstructive pulmonary disease (COPD): The multi‐ethnic study of atherosclerosis COPD study. Journal of Magnetic Resonance Imaging, 51(2), 571-579.

  • P Gao, X Duan, T Zhang, M Zhang, B Schmidt, X Zhang, H Sun, W Zhang, W. Liu: Millimeter-scale and billion-atom reactive force field simulation on Sunway Taihulight. IEEE Transactions on Parallel and Distributed Systems 31 (12), 2954-2967, 2020

  • JM Abuín, N Lopes, L Ferreira, TF Pena, B Schmidt: Big data in metagenomics: Apache spark vs MPI. Plos ONE 15 (10), e023974, 2020

  • Jiang, P., Luo, J., Wang, Y., Deng, P., Schmidt, B., Tang, X., Wong, L. & Zhao, L. (2019). kmcEx: memory-frugal and retrieval-efficient encoding of counted k-mers. Bioinformatics, 35(23), 4871-4878.

  • Lan, H., Meng, J., Hundt, C., Schmidt, B., Deng, M., Wang, X., Liu, W. & Feng, S. (2019). FeatherCNN: Fast inference computation with TensorGEMM on ARM architectures. IEEE Transactions on Parallel and Distributed Systems, 31(3), 580-594.

Conferences

  • Müller, A., Schmidt, B., Membarth, R., Leißa, R., & Hack, S. (2022). AnySeq/GPU: A Novel Approach for Faster Sequence Alignment on GPUs. ACM International Conference on Supercomputing (ICS 2022)

  • Henkys, V., Schmidt, B., & Berger, N. (2022). Online Event Selection for Mu3e using GPUs. ISPDC 2022

  • Weißenberger, A., & Schmidt, B. (2021, December). Accelerating JPEG Decompression on GPUs. IEEE 28th International Conference on High Performance Computing, Data, and Analytics (HiPC 2021) (pp. 121-130). IEEE.

  • Kobus, R., Müller, A., Jünger, D., Hundt, C., & Schmidt, B. (2021, August). MetaCache-GPU: ultra-fast metagenomic classification. In 50th ACM International Conference on Parallel Processing (ICPP 2021), ACM

  • Cascitti, J., Niebler, S., Müller, A., & Schmidt, B. (2021, June). RNACache: Fast Mapping of RNA-Seq Reads to Transcriptomes Using MinHashing. In International Conference on Computational Science (pp. 367-381). Springer, (ICCS 2021)

  • Jünger, D., Kobus, R., Müller, A., Hundt, C., Xu, K., Liu, W., & Schmidt, B. (2020, December). Warpcore: a library for fast hash tables on gpus. IEEE 27th International Conference on High Performance Computing, Data, and Analytics (HiPC 2020) (pp. 11-20). IEEE.

  • Duan, X., Gao, P., Zhang, M., Zhang, T., Meng, H., Li, Y., Schmidt, B., Liu, W. & Yang, G. (2020, November). Cell-list based molecular dynamics on many-core processors: a case study on sunway TaihuLight supercomputer. In SC20: International Conference for High Performance Computing, Networking, Storage and Analysis (pp. 1-12). IEEE.

  • Schmidt, B., & Hundt, C. (2020, August). cuDTW++: Ultra-Fast Dynamic Time Warping on CUDA-Enabled GPUs. In European Conference on Parallel Processing (pp. 597-612). (Euro-Par 2020) Springer.

  • Hieronymus, M., Schmidt, B., & Böser, S. (2020, June). Reconstruction of Low Energy Neutrino Events with GPUs at IceCube. In International Conference on Computational Science (pp. 118-131). Springer, (ICCS 2020)

  • Müller, A., Schmidt, B., Hildebrandt, A., Membarth, R., Leißa, R., Kruse, M., & Hack, S. (2020, May). AnySeq: a high performance sequence alignment library based on partial evaluation. In 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS 2020) (pp. 1030-1040). IEEE.

  • Duan, X., Gao, P., Zhang, M., Zhang, T., Meng, H., Li, Y., Schmidt, B. & Liu, W. (2020, February). Neighbor-list-free molecular dynamics on sunway taihulight supercomputer. In Proceedings of the 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP 2020).

  • Weißenberger, A., & Schmidt, B. (2019, August). Massively parallel ANS decoding on GPUs. In Proceedings of the 48th International Conference on Parallel Processing (pp. 1-10), ACM (ICPP 2019)

  • Yin, Z., Zhang, T., Müller, A., Liu, H., Wei, Y., Schmidt, B., & Liu, W. (2019, August). Efficient parallel sort on AVX-512-based multi-core and many-core architectures. In 2019 IEEE 21st international conference on high performance computing and communications; (HPCC) (pp. 168-176). IEEE.

  • Kobus, R., Jünger, D., Hundt, C., & Schmidt, B. (2019, August). Gossip: Efficient communication primitives for multi-gpu systems. In Proceedings of the 48th International Conference on Parallel Processing (pp. 1-10), ACM (ICPP 2019)

  • Büren, F., Jünger, D., Kobus, R., Hundt, C., & Schmidt, B. (2019, June). Suffix Array Construction on Multi-GPU Systems. In Proceedings of the 28th International Symposium on High-Performance Parallel and Distributed Computing (pp. 183-194) (ACM HPDC 2019)

  • Leißa, R., Boesche, K., Hack, S., Pérard-Gayot, A., Membarth, R., Slusallek, P., Müller, A. & Schmidt, B. (2018). AnyDSL: A partial evaluation framework for programming high-performance libraries. Proceedings of the ACM on Programming Languages, 2(OOPSLA 2018), 1-30.

  • Weißenberger, A., & Schmidt, B. (2018, August). Massively parallel Huffman decoding on GPUs. In Proceedings of the 47th International Conference on Parallel Processing (pp. 1-10), ACM (ICPP 2018)

  • Jünger, D., Hundt, C., & Schmidt, B. (2018, May). WarpDrive: Massively parallel hashing on multi-GPU nodes. In 2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS 2018) (pp. 441-450). IEEE

Books

  • B. Schmidt, J. González-Domínguez, C. Hundt, M. Schlarb: Parallel Programming: Concepts and Practice, Morgan Kaufmann (Elsevier), 2018

  • B. Schmidt: Bioinformatics: High Performance Parallel Computer Architectures, Taylor & Francis/CRC Press, 2010