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Parallel Computing E191-04

  • A Research Unit of The Institute of Computer Engineering

Parallel Computing deals with the efficient utilization of parallel processing resources for the solution of computational problems. This sounds dry, but since all modern, general purpose computing devices are in some way or the other parallel computers, parallel compting is ubiquitous and inevitable.

Research Unit Parallel Computing

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Parallel Computing deals with the efficient utilization of parallel processing resources for the solution of computational problems. This sounds dry, but since all modern, general purpose computing devices are in some way or the other parallel computers, parallel compting is ubiquitous and inevitable.

Since not all computational problems are easily amenable to being solved in parallel, it is fascinating and challenging, and abounds with issues and problems that must be resolved better. Parallel Computing at TU Wien focuses on efficient utilization and modeling of real, existing architectures and systems (shared-memory multi-cores, distributed memory systems, hybrid and accelerated systems), on algorithms, interfaces, libraries and, to some extent, applications; and with idealized models of parallel computuations to explore the limits of parallelization.

The research area has specific expertise and interest in message-passing parallel computing, interfaces like MPI, benchmarking of parallel algorithms, scheduling, shared-memory algorithms and data structures, and parallel algorithms. All these topics are dealt with extensively in lectures offered by the research division.

The research Unit Parallel Computing is part of the Institute of Computer Engineering.

Sascha Hunold
Sascha Hunold S. Hunold

Associate Professor
Assoc.Prof. Dr.

Jesper Larsson Träff
Jesper Larsson Träff J. Träff

Head of Research Unit
Univ.Prof. Dr. / MSc PhD

Majid Salimibeni
Majid Salimibeni M. Salimibeni

PostDoc Researcher
PhD

Elia Nitsche
Elia Nitsche E. Nitsche

IT Services

Alina Marie Sarkissian
Alina Marie Sarkissian A. Sarkissian

Office Services
MA

2025W

2026S

 

2026

2025

2024

2023

2022

2021

  • A Doubly-pipelined, Dual-root Reduction-to-all Algorithm and Implementation / Träff, J. L. (2021). A Doubly-pipelined, Dual-root Reduction-to-all Algorithm and Implementation. arXiv. https://doi.org/10.48550/arXiv.2109.12626
  • A more pragmatic implementation of the lock-free, ordered, linked list / Träff, J. L., & Pöter, M. (2021). A more pragmatic implementation of the lock-free, ordered, linked list. In J. Lee & E. Petrank (Eds.), Proceedings of the 26th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. ACM. https://doi.org/10.1145/3437801.3441579
  • MicroBench Maker: Reproduce, Reuse, Improve / Hunold, S., Ajanohoun, J. I., & Carpen-Amarie, A. (2021). MicroBench Maker: Reproduce, Reuse, Improve. In 2021 International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS). 12th IEEE International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS 2021) in conjunction with SC 2021, St. Louis, Missouri, United States of America (the). IEEE. https://doi.org/10.1109/pmbs54543.2021.00013
    Project: Autotune (2021–2025)
  • Teaching Complex Scheduling Algorithms / Hunold, S., & Przybylski, B. (2021). Teaching Complex Scheduling Algorithms. In 2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). 11th NSF/TCPP Workshop on Parallel and Distributed Computing Education (EduPar 2021) in conjunction with 35th IEEE IPDPS 2021 - Online Conference, Portland, Oregon, USA, United States of America (the). IEEE. https://doi.org/10.1109/ipdpsw52791.2021.00058
  • MPI collective communication through a single set of interfaces: A case for orthogonality / Träff, J. L., Hunold, S., Mercier, G., & Holmes, D. J. (2021). MPI collective communication through a single set of interfaces: A case for orthogonality. Parallel Computing: Systems & Applications, 107(102826), 102826. https://doi.org/10.1016/j.parco.2021.102826
    Project: Process Mapping (2019–2024)

2020

2019

2018

  • Brief Announcement: Stamp-it, a more Thread-efficient, Concurrent Memory Reclamation Scheme in the C++ Memory Model / Pöter, M., & Träff, J. L. (2018). Brief Announcement: Stamp-it, a more Thread-efficient, Concurrent Memory Reclamation Scheme in the C++ Memory Model. In Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures. 30th ACM Symposium on Parallelism in Algorithms and Architectures (SPAA 2018), Vienna, Austria, Austria. ACM. https://doi.org/10.1145/3210377.3210661
  • On Optimal trees for Irregular Gather and Scatter Collectives / Träff, J. L. (2018). On Optimal trees for Irregular Gather and Scatter Collectives. Invited Talk at the Uppsala University (Sept-2018), Uppsala, Sweden. http://hdl.handle.net/20.500.12708/86726
  • Autotuning MPI Collectives using Performance Guidelines / Hunold, S., & Carpen-Amarie, A. (2018). Autotuning MPI Collectives using Performance Guidelines. In Proceedings of the International Conference on High Performance Computing in Asia-Pacific Region. International Conference on High Performance Computing in Asia-Pacific Region (HPC Asia 2018), Tokyo, Japan. ACM. https://doi.org/10.1145/3149457.3149461
  • Implementation of Multioperations in Thick Control Flow Processors / Forsell, M., Roivainen, J., Leppänen, V., & Träff, J. L. (2018). Implementation of Multioperations in Thick Control Flow Processors. In 2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). 20th Workshop on Advances in Parallel and Distributed Computational Models (APDCM 2018) in conjunction with IPDPS 2018, Vancouver, Canada. IEEE. https://doi.org/10.1109/ipdpsw.2018.00121
  • Algorithm Selection of MPI Collectives Using Machine Learning Techniques / Hunold, S., & Carpen-Amarie, A. (2018). Algorithm Selection of MPI Collectives Using Machine Learning Techniques. In 2018 IEEE/ACM Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS). 9th IEEE International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS 2018) in conjunction with SC 2018, Dallas, United States of America (the). IEEE. https://doi.org/10.1109/pmbs.2018.8641622
  • Full-Duplex Inter-Group All-to-All Broadcast Algorithms with Optimal Bandwidth / Kang, Q., Träff, J. L., Al-Bahrani, R., Agrawal, A., Choudhary, A., & Liao, W. (2018). Full-Duplex Inter-Group All-to-All Broadcast Algorithms with Optimal Bandwidth. In Proceedings of the 25th European MPI Users’ Group Meeting. 25th European MPI Users’ Group Meeting (EuroMPI 2018), Barcelona, Spain. ACM. https://doi.org/10.1145/3236367.3236374
  • Hierarchical Clock Synchronization in MPI / Hunold, S., & Carpen-Amarie, A. (2018). Hierarchical Clock Synchronization in MPI. In 2018 IEEE International Conference on Cluster Computing (CLUSTER). IEEE International Conference on Cluster Computing, CLUSTER 2018, Belfast, United Kingdom of Great Britain and Northern Ireland (the). IEEE. https://doi.org/10.1109/cluster.2018.00050
  • Stamp-it: A more Thread-efficient, Concurrent Memory Reclamation Scheme in the C++ Memory Model / Pöter, M., & Träff, J. L. (2018). Stamp-it: A more Thread-efficient, Concurrent Memory Reclamation Scheme in the C++ Memory Model. arXiv. https://doi.org/10.48550/arXiv.1805.08639
  • Memory Models for C/C++ Programmers / Pöter, M., & Träff, J. L. (2018). Memory Models for C/C++ Programmers. arXiv. https://doi.org/10.48550/arXiv.1803.04432
  • Parallel Quicksort without Pairwise Element Exchange / Träff, J. L. (2018). Parallel Quicksort without Pairwise Element Exchange. arXiv. https://doi.org/10.48550/arXiv.1804.07494
  • <i>Stamp-it</i> , amortized constant-time memory reclamation in comparison to five other schemes / Pöter, M., & Träff, J. L. (2018). Stamp-it            , amortized constant-time memory reclamation in comparison to five other schemes. In Proceedings of the 23rd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. 23rd Symposium on Principles and Practice of Parallel Programming (PPoPP 2018), Vienna, Austria, Austria. ACM. https://doi.org/10.1145/3178487.3178532
  • Practical, distributed, low overhead algorithms for irregular gather and scatter collectives / Träff, J. L. (2018). Practical, distributed, low overhead algorithms for irregular gather and scatter collectives. Parallel Computing: Systems & Applications, 75, 100–117. https://doi.org/10.1016/j.parco.2018.04.003
    Project: MPI (2013–2018)
  • Supporting concurrent memory access in TCF processor architectures / Forsell, M., Roivainen, J., Leppänen, V., & Träff, J. L. (2018). Supporting concurrent memory access in TCF processor architectures. Microprocessors and Microsystems, 63, 226–236. https://doi.org/10.1016/j.micpro.2018.09.013

2017

  • Euro-Par 2016: Parallel Processing Workshops : Euro-Par 2016 International Workshops, Grenoble, France, August 24-26, 2016, Revised Selected Papers / Desprez, F., Dutot, P.-F., Kaklamanis, C., Marchal, L., Molitorisz, K., Ricci, L., Scarano, V., Vega-Rodriguez, M. A., Varbanescu, A. L., Hunold, S., Scott, S. L., Lankes, S., & Weidendorfer, J. (Eds.). (2017). Euro-Par 2016: Parallel Processing Workshops : Euro-Par 2016 International Workshops, Grenoble, France, August 24-26, 2016, Revised Selected Papers. Springer Nature Switzerland AG 2021. https://doi.org/10.1007/978-3-319-58943-5
  • Introduction to REPPAR Workshop / Hunold, S., Legrand, A., & Nussbaum, L. (2017). Introduction to REPPAR Workshop. In 2017 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). 2017 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), Lake Buena Vista, United States of America (the). IEEE. https://doi.org/10.1109/ipdpsw.2017.221
  • Exploiting Common Neighborhoods to Optimize MPI Neighborhood Collectives / Mirsadeghi, S. H., Träff, J. L., Balaji, P., & Afsahi, A. (2017). Exploiting Common Neighborhoods to Optimize MPI Neighborhood Collectives. In 2017 IEEE 24th International Conference on High Performance Computing (HiPC). 24th IEEE International Conference on High Performance Computing (HiPC 2017), Jaipur, India. IEEE. https://doi.org/10.1109/hipc.2017.00047
  • Predicting the Energy-Consumption of MPI Applications at Scale Using Only a Single Node / Heinrich, F. C., Cornebize, T., Degomme, A., Legrand, A., Carpen-Amarie, A., Hunold, S., Orgerie, A.-C., & Quinson, M. (2017). Predicting the Energy-Consumption of MPI Applications at Scale Using Only a Single Node. In 2017 IEEE International Conference on Cluster Computing (CLUSTER). IEEE International Conference on Cluster Computing (CLUSTER 2017), Honolulu, Hawaii, United States of America (the). IEEE. https://doi.org/10.1109/cluster.2017.66
  • Practical, linear-time, fully distributed algorithms for irregular gather and scatter / Träff, J. L. (2017). Practical, linear-time, fully distributed algorithms for irregular gather and scatter. In Proceedings of the 24th European MPI Users’ Group Meeting on - EuroMPI ’17. 24th European MPI Users’ Group Meeting (EuroMPI/USA 2017), Chicago, IL, United States of America (the). ACM. https://doi.org/10.1145/3127024.3127025
    Project: MPI (2013–2018)
  • Micro-benchmarking MPI Neighborhood Collective Operations / Lübbe, F. D. (2017). Micro-benchmarking MPI Neighborhood Collective Operations. In F. F. Rivera, T. F. Pena, & J. C. Cabaleiro (Eds.), Euro-Par 2017: Parallel Processing 23rd International Conference on Parallel and Distributed Computing, Santiago de Compostela, Spain, August 28 – September 1, 2017, Proceedings (pp. 65–78). Springer. https://doi.org/10.1007/978-3-319-64203-1_5
    Project: MPI (2013–2018)
  • Better Process Mapping and Sparse Quadratic Assignment / Schulz, C., & Träff, J. L. (2017). Better Process Mapping and Sparse Quadratic Assignment. In C. S. Iliopoulos, S. P. Pissis, S. J. Puglisi, & R. Raman (Eds.), 16th International Symposium on Experimental Algorithms, SEA 2017 (pp. 4:1-4:15). Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH. https://doi.org/10.4230/LIPIcs.SEA.2017.4
  • Supporting concurrent memory access in TCF-aware processor architectures / Forsell, M., Roivainen, J., Leppänen, V., & Träff, J. L. (2017). Supporting concurrent memory access in TCF-aware processor architectures. In J. Nurmi, M. Vesterbacka, J. J. Wikner, A. Alvandpour, M. Nielsen-Lönn, & I. R. Nielsen (Eds.), 2017 IEEE Nordic Circuits and Systems Conference (NORCAS): NORCHIP and International Symposium of System-on-Chip (SoC). IEEE. https://doi.org/10.1109/norchip.2017.8124962
  • Is Gossip-inspired reduction competitive in high performance computing? / Wimmer, E. (2017). Is Gossip-inspired reduction competitive in high performance computing? International Workshop on Parallel Numerics (PARNUM 2017), Waischenfeld, Germany. http://hdl.handle.net/20.500.12708/86504
  • High Performance Expectations for MPI / Träff, J. L. (2017). High Performance Expectations for MPI. Friedrich-Alexander-Universität Erlangen-Nürnberg, Prof. Dr. Gerhard Wellein, Erlangen, Germany. http://hdl.handle.net/20.500.12708/86505
  • The past 25 years of MPI / Träff, J. L. (2017). The past 25 years of MPI. Panel at ISC High Performance Conference 2017 - The HPC Event, Intel booth, Frankfurt, Germany. http://hdl.handle.net/20.500.12708/86517
  • Autotuning MPI Collectives using Performance Guidelines / Hunold, S., & Carpen-Amarie, A. (2017). Autotuning MPI Collectives using Performance Guidelines. LIG - Bâtiment IMAG, St Martin d’Hères, France. http://hdl.handle.net/20.500.12708/86599
  • A new and five older Concurrent Memory Reclamation Schemes in Comparison (Stamp-it) / Pöter, M., & Träff, J. L. (2017). A new and five older Concurrent Memory Reclamation Schemes in Comparison (Stamp-it). arXiv. https://doi.org/10.48550/arXiv.1712.06134
  • On Optimal Trees for Irregular Gather and Scatter Collectives / Träff, J. L. (2017). On Optimal Trees for Irregular Gather and Scatter Collectives. arXiv. https://doi.org/10.48550/arXiv.1711.08731
  • Better Process Mapping and Sparse Quadratic Assignment / Schulz, C., & Träff, J. L. (2017). Better Process Mapping and Sparse Quadratic Assignment. arXiv. https://doi.org/10.48550/arXiv.1702.04164
  • Practical, Linear-time, Fully Distributed Algorithms for Irregular Gather and Scatter / Träff, J. L. (2017). Practical, Linear-time, Fully Distributed Algorithms for Irregular Gather and Scatter (1702.05967). arXiv. https://doi.org/10.48550/arXiv.1702.05967
    Project: MPI (2013–2018)
  • VieM v1.00 - Vienna Mapping and Sparse Quadratic Assignment User Guide / Schulz, C., & Träff, J. L. (2017). VieM v1.00 - Vienna Mapping and Sparse Quadratic Assignment User Guide. arXiv. https://doi.org/10.48550/arXiv.1703.05509
  • Tuning MPI Collectives by Verifying Performance Guidelines / Hunold, S., & Carpen-Amarie, A. (2017). Tuning MPI Collectives by Verifying Performance Guidelines. arXiv. https://doi.org/10.48550/arXiv.1707.09965
  • On expected and observed communication performance with MPI derived datatypes / Carpen-Amarie, A., Hunold, S., & Träff, J. L. (2017). On expected and observed communication performance with MPI derived datatypes. Parallel Computing: Systems & Applications, 69, 98–117. https://doi.org/10.1016/j.parco.2017.08.006
    Projects: EPiGRAM (2013–2016) / MPI (2013–2018)
  • Scheduling Independent Moldable Tasks on Multi-Cores with GPUs / Bleuse, R., Hunold, S., Kedad-Sidhoum, S., Monna, F., Mounie, G., & Trystram, D. (2017). Scheduling Independent Moldable Tasks on Multi-Cores with GPUs. IEEE Transactions on Parallel and Distributed Systems, 28(9), 2689–2702. https://doi.org/10.1109/tpds.2017.2675891
  • MPI Is 25 Years Old! / Lusk, E., & Träff, J. L. (2017). MPI Is 25 Years Old! HPCwire, MAY 1. http://hdl.handle.net/20.500.12708/146783
  • Fast Processing of MPI Derived Datatypes? / Träff, J. L. (2017). Fast Processing of MPI Derived Datatypes? Mini Workshop Algorithms Engineering, Uni Wien, Vienna, Austria, Austria. http://hdl.handle.net/20.500.12708/86518
  • High Performance Expectations for MPI / Träff, J. L. (2017). High Performance Expectations for MPI. In G. Baumgartner & J. Courian (Eds.), AHPC 2017, Austrian HPC Meeting 2017 (p. 33). FSP Scientific Computing, University of Innsbruck. http://hdl.handle.net/20.500.12708/56920

2016

  • The art of benchmarking MPI libraries / Hunold, S., Carpen-Amarie, A., & Träff, J. L. (2016). The art of benchmarking MPI libraries. In I. Reichl, C. Blaas-Schenner, & J. Zabloudil (Eds.), Austrian HPC Meeting 2016 - AHPC 2016 (p. 45). Vienna Scientific Cluster (VSC). http://hdl.handle.net/20.500.12708/56921
  • The Art of MPI Benchmarking / Hunold, S. (2016). The Art of MPI Benchmarking. 45th SPEEDUP Workshop on High-Performance Computing, Basel, Switzerland. http://hdl.handle.net/20.500.12708/86310
  • Brief Announcement: Benchmarking Concurrent Priority Queues: / Gruber, J., Träff, J. L., & Wimmer, M. (2016). Brief Announcement: Benchmarking Concurrent Priority Queues: In SPAA ’16: Proceedings of the 28th ACM Symposium on Parallelism in Algorithms and Architectures (pp. 361–362). ACM. https://doi.org/10.1145/2935764.2935803
  • Polynomial-Time Construction of Optimal MPI Derived Datatype Trees / Ganian, R., Kalany, M., Szeider, S., & Träff, J. L. (2016). Polynomial-Time Construction of Optimal MPI Derived Datatype Trees. In 2016 IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE 30th International Parallel and Distributed Processing Symposium (IPDPS 2016), Chicago, United States of America (the). IEEE Computer Society. https://doi.org/10.1109/ipdps.2016.13
    Project: EPiGRAM (2013–2016)
  • On the Expected and Observed Communication Performance with MPI Derived Datatypes / Carpen-Amarie, A., Hunold, S., & Träff, J. L. (2016). On the Expected and Observed Communication Performance with MPI Derived Datatypes. In D. Holmes, A. Collis, J. L. Träff, & L. Smith (Eds.), Proceedings of the 23rd European MPI Users’ Group Meeting. ACM. https://doi.org/10.1145/2966884.2966905
    Projects: EPiGRAM (2013–2016) / MPI (2013–2018)
  • A Library for Advanced Datatype Programming / Träff, J. L. (2016). A Library for Advanced Datatype Programming. In D. Holmes, A. Collis, J. L. Träff, & L. Smith (Eds.), Proceedings of the 23rd European MPI Users’ Group Meeting. ACM. https://doi.org/10.1145/2966884.2966904
    Project: EPiGRAM (2013–2016)
  • High Performance Parallel Summed-Area Table Kernels for Multi-core and Many-core Systems / Papatriantafyllou, A., & Sacharidis, D. (2016). High Performance Parallel Summed-Area Table Kernels for Multi-core and Many-core Systems. In P.-F. Dutot & D. Trystram (Eds.), Euro-Par 2016: Parallel Processing (pp. 306–318). Springer International Publishing. https://doi.org/10.1007/978-3-319-43659-3_23
  • Automatic Verification of Self-consistent MPI Performance Guidelines / Hunold, S., Carpen-Amarie, A., Lübbe, F. D., & Träff, J. L. (2016). Automatic Verification of Self-consistent MPI Performance Guidelines. In P.-F. Dutot & D. Trystram (Eds.), Euro-Par 2016: Parallel Processing (pp. 433–446). Springer International Publishing. https://doi.org/10.1007/978-3-319-43659-3_32
    Projects: MPI (2013–2018) / ReproPC (2013–2016)
  • Clock Synchronization Algorithms and SimGrid / Hunold, S. (2016). Clock Synchronization Algorithms and SimGrid. SimGrid User Days, Fréjus, France. http://hdl.handle.net/20.500.12708/86260
  • Tutorial: Effective MPI Programming: concepts, advanced features, do's and dont's / Träff, J. L. (2016). Tutorial: Effective MPI Programming: concepts, advanced features, do’s and dont’s. Tutorial on MPI at the 22nd International European Conference on Parallel and Distributed Computing (Euro-Par 2016), Grenoble, France. http://hdl.handle.net/20.500.12708/86292
  • On The Power of Structured Data in MPI / Träff, J. L. (2016). On The Power of Structured Data in MPI. Guest Lecture of the course: Parallel and High Performance Computing, LMU Munich, München, Germany. http://hdl.handle.net/20.500.12708/86357
    Project: MPI (2013–2018)
  • Polynomial-Time Construction of Optimal MPI Derived Datatype Trees / Träff, J. L. (2016). Polynomial-Time Construction of Optimal MPI Derived Datatype Trees. Leibniz-Rechenzentrum (LRZ), Garching bei München, Germany. http://hdl.handle.net/20.500.12708/86364
  • Message-Combining Algorithms for Isomorphic, Sparse Collective Communication / Träff, J. L., Carpen-Amarie, A., Hunold, S., & Rougier, A. (2016). Message-Combining Algorithms for Isomorphic, Sparse Collective Communication. arXiv. https://doi.org/10.48550/arXiv.1606.07676
  • Benchmarking Concurrent Priority Queues: Performance of k-LSM and Related Data Structures / Gruber, J., Träff, J. L., & Wimmer, M. (2016). Benchmarking Concurrent Priority Queues: Performance of k-LSM and Related Data Structures. arXiv. https://doi.org/10.48550/arXiv.1603.05047
  • PGMPI: Automatically Verifying Self-Consistent MPI Performance Guidelines / Hunold, S., Carpen-Amarie, A., Lübbe, F. D., & Träff, J. L. (2016). PGMPI: Automatically Verifying Self-Consistent MPI Performance Guidelines. arXiv. https://doi.org/10.48550/arXiv.1606.00215
    Projects: MPI (2013–2018) / ReproPC (2013–2016)
  • MPI Derived Datatypes: Performance Expectations and Status Quo / Carpen-Amarie, A., Hunold, S., & Träff, J. L. (2016). MPI Derived Datatypes: Performance Expectations and Status Quo. arXiv. https://doi.org/10.48550/arXiv.1607.00178
    Projects: EPiGRAM (2013–2016) / MPI (2013–2018)
  • The EPiGRAM Project: Preparing Parallel Programming Models for Exascale / Markidis, S., Peng, I. B., Larsson Träff, J., Rougier, A., Bartsch, V., Machado, R., Rahn, M., Hart, A., Holmes, D., Bull, M., & Laure, E. (2016). The EPiGRAM Project: Preparing Parallel Programming Models for Exascale. In M. Taufer, B. Mohr, & J. M. Kunkel (Eds.), High Performance Computing : ISC High Performance 2016 International Workshops, ExaComm, E-MuCoCoS, HPC-IODC, IXPUG, IWOPH, P^3MA, VHPC, WOPSSS, Frankfurt, Germany, June 19–23, 2016, Revised Selected Papers (pp. 56–68). Springer International Publishing. https://doi.org/10.1007/978-3-319-46079-6_5
    Project: EPiGRAM (2013–2016)
  • The art of benchmarking MPI libraries / Hunold, S. (2016). The art of benchmarking MPI libraries. Austrian HPC Meeting 2016 - AHPC16, Grundlsee, Austria. http://hdl.handle.net/20.500.12708/86269
  • Viewpoint: (Mis)Managing Parallel Computing Research through EU Project Funding / Träff, J. L. (2016). Viewpoint: (Mis)Managing Parallel Computing Research through EU Project Funding. Communications of the ACM, 59(12), 46–48. https://doi.org/10.1145/2948893
  • Governing energy consumption in Hadoop through CPU frequency scaling: An analysis / Ibrahim, S., Phan, T.-D., Carpen-Amarie, A., Chihoub, H.-E., Moise, D., & Antoniu, G. (2016). Governing energy consumption in Hadoop through CPU frequency scaling: An analysis. Future Generation Computer Systems: The International Journal of EScience, 54, 219–232. http://hdl.handle.net/20.500.12708/148922
  • Editorial: Special Issue: Euro-Par 2015 / Lengauer, C., Bougé, L., & Träff, J. L. (2016). Editorial: Special Issue: Euro-Par 2015. Concurrency and Computation: Practice and Experience, 28(12), 3445–3446. http://hdl.handle.net/20.500.12708/148865
  • The Art of MPI Benchmarking / Hunold, S. (2016). The Art of MPI Benchmarking. Lunchtime Seminar, Department of Computer Science, University of Innsbruck, Innsbruck, Austria, Austria. http://hdl.handle.net/20.500.12708/86282
  • Effective MPI Programming: Concepts, Advanced Features, Do's and Don'ts / Träff, J. L. (2016). Effective MPI Programming: Concepts, Advanced Features, Do’s and Don’ts. Vienna Scientific Cluster: VSC School Seminar, TU Wien, Vienna, Austria, Austria. http://hdl.handle.net/20.500.12708/86253
  • Proceedings of the 23rd European MPI Users' Group Meeting, EuroMPI 2016 / Holmes, D., Collis, A., Träff, J. L., & Smith, L. (Eds.). (2016). Proceedings of the 23rd European MPI Users’ Group Meeting, EuroMPI 2016. ACM. http://hdl.handle.net/20.500.12708/24173

2015

2014

2013

2012

2011

 

  • Sascha Hunold: Best Short Paper / PMBS@Supercomputing
    2022 / USA
  • Sascha Hunold: Best Paper Award IEEE CLUSTER 2020
    2020 / Japan
  • Jesper Larsson Träff: Innovation Radar: Innovation Title: PGAS-based MPI with interoperability; Innovation Category: Exploration; FP 7 project EPiGRAM
    2018 / Project
  • Sascha Hunold: Best Paper Award EuroMPI/Asia
    2014 / Japan
  • Jesper Larsson Träff: Best Paper Award: "Reproducible MPI Micro-Benchmarking Isn't As Easy As You Think", S. Hunold, A. Carpen-Amarie, J. Träff, 21st European MPI Users' Group Meeting, EuroMPI/ASIA 2014, Kyoto, Japan, September 9-12, 2014
    2014 / Program Chairs of EuroMPI/ASIA 2014 / Japan

Soon, this page will include additional information such as reference projects, conferences, events, and other research activities.

Until then, please visit Parallel Computing’s research profile in TISS .