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Berryman","Aaron Taudt","Johannes Zeman","Axel Arnold" ], "authors": [ {"first" : "Kai", "last" : "Kratzer"}, {"first" : "Joshua T.", "last" : "Berryman"}, {"first" : "Aaron", "last" : "Taudt"}, {"first" : "Johannes", "last" : "Zeman"}, {"first" : "Axel", "last" : "Arnold"} ], "volume": "185","number": "7","pages": "1875--1885", "doi" : "10.1016/j.cpc.2014.03.013", "bibtexKey": "Kratzer_2014" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2cb291e05a1a3bbb91a404b35dfb148a0/tpollinger", "tags" : [ "{HPX}","myown","Performance","{CUDA}","Portability","hpc","Task-based","{GPU}","Kokkos" ], "intraHash" : "cb291e05a1a3bbb91a404b35dfb148a0", "interHash" : "23aeada47e005c4caa88b8ee189c767a", "label" : "Beyond Fork-Join: Integration of Performance Portable Kokkos Kernels with HPX", "user" : "tpollinger", "description" : "", "date" : "2022-03-24 16:12:08", "changeDate" : "2022-03-24 15:12:36", "count" : 3, "pub-type": "inproceedings", "booktitle": "2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)", "year": "2021", "url": "", "author": [ "Gregor Daiß","Mikael Simberg","Auriane Reverdell","John Biddiscombe","Theresa Pollinger","Hartmut Kaiser","Dirk Pflüger" ], "authors": [ {"first" : "Gregor", "last" : "Daiß"}, {"first" : "Mikael", "last" : "Simberg"}, {"first" : "Auriane", "last" : "Reverdell"}, {"first" : "John", "last" : "Biddiscombe"}, {"first" : "Theresa", "last" : "Pollinger"}, {"first" : "Hartmut", "last" : "Kaiser"}, {"first" : "Dirk", "last" : "Pflüger"} ], "pages": "377--386","abstract": "Between a widening range of GPU vendors and the trend of having more GPUs per compute node in supercomputers such as Summit, Perlmutter, Frontier and Aurora, developing performant yet portable distributed HPC applications becomes ever more challenging. Leveraging existing solutions like Kokkos for platform-independent code and HPX for distributing the application in a task-based fashion can alleviate these challenges. However, using such frameworks in the same application requires them to work together seamlessly. In this work we present an HPX Kokkos integration that works both ways: we can integrate CPU and GPU Kokkos kernels as HPX tasks and inversely use HPX worker threads to work on Kokkos kernels. Using HPX futures makes launching and synchronizing Kokkos kernels from multiple threads easy, allowing us to move away from the more traditional fork-join model. To evaluate our integrations we ported existing Vc and CUDA kernels within an existing HPX application, Octo-Tiger, to use Kokkos instead. We achieve comparable, or better, performance than with previous Vc and CUDA kernels, showing both the viability of our HPX Kokkos integration, as well as future-proofing Octo-Tiger for a wider range of potential machines. Furthermore, we introduce event polling for synchronizing CUDA kernels (or Kokkos kernels on the respective backend) achieving speedups over the previous solution using callbacks.", "eventtitle" : "2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)", "shorttitle" : "Beyond Fork-Join", "doi" : "10.1109/IPDPSW52791.2021.00066", "bibtexKey": "dais_beyond_2021" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2865b04cce31a607aa787e4051fded357/tpollinger", "tags" : [ "parallelism","myown","simulation","processing","combination_technique","hpc","sparse_grids" ], "intraHash" : "865b04cce31a607aa787e4051fded357", "interHash" : "2716385e3707408ae1db87d8be44ecc7", "label" : "Distributing Higher-Dimensional Simulations Across Compute Systems: A Widely Distributed Combination Technique", "user" : "tpollinger", "description" : "", "date" : "2022-03-24 15:53:08", "changeDate" : "2022-03-31 13:46:17", "count" : 3, "pub-type": "inproceedings", "booktitle": "2021 IEEE/ACM International Workshop on Hierarchical Parallelism for Exascale Computing (HiPar)", "year": "2021", "url": "https://ieeexplore.ieee.org/abstract/document/9654243", "author": [ "Theresa Pollinger","Marcel Hurler","Michael Obersteiner","Dirk Pflüger" ], "authors": [ {"first" : "Theresa", "last" : "Pollinger"}, {"first" : "Marcel", "last" : "Hurler"}, {"first" : "Michael", "last" : "Obersteiner"}, {"first" : "Dirk", "last" : "Pflüger"} ], "pages": "1--9","abstract": "The numerical solution of high-dimensional PDE problems is essential for many research questions, such as understanding relativistic astrophysics, quantum physics, or hot fusion plasmas. At the same time, it is haunted by the curse of dimensionality, rendering finely resolved simulations infeasible even on modern architectures. The Sparse Grid Combination Technique helps to break the curse of dimensionality for high-dimensional PDE problems to some extent. But even then, simulations are restricted by the size of HPC systems. A new implementation based on the open-source code DisCoTec allows to distribute existing solvers even across compute systems: The widely distributed combination technique enables simulations at scales that would otherwise be intractable.This paper introduces the extended algorithm and showcases a proof of concept for the remote communication set-up. The scaling properties for the single-system and two-system cases are presented, and the numerical correctness of the implementation is validated.The widely distributed combination technique is useful in cases where the memory and/or compute resources are not sufficient for a particular problem to fit on one single available system, but multiple systems are able to accommodate it.", "eventtitle" : "2021 IEEE/ACM International Workshop on Hierarchical Parallelism for Exascale Computing (HiPar)", "venue" : "SC21", "shorttitle" : "Distributing Higher-Dimensional Simulations Across Compute Systems", "doi" : "10.1109/HiPar54615.2021.00006", "bibtexKey": "pollingerdistributing" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/23c5a355745e5f75568ce69d0e41a8885/tpollinger", "tags" : [ "load_balancing","plasma_physics","combination_technique","HPC","sparse_grids" ], "intraHash" : "3c5a355745e5f75568ce69d0e41a8885", "interHash" : "65ce12fa9e227eac5229d89746aabe92", "label" : "A massively-parallel, fault-tolerant solver for high-dimensional PDEs", "user" : "tpollinger", "description" : "", "date" : "2020-07-27 14:54:55", "changeDate" : "2020-07-27 13:44:23", "count" : 3, "pub-type": "inproceedings", "booktitle": "European Conference on Parallel Processing", "year": "2016", "url": "", "author": [ "Mario Heene","Alfredo Parra Hinojosa","Hans-Joachim Bungartz","Dirk Pflüger" ], "authors": [ {"first" : "Mario", "last" : "Heene"}, {"first" : "Alfredo Parra", "last" : "Hinojosa"}, {"first" : "Hans-Joachim", "last" : "Bungartz"}, {"first" : "Dirk", "last" : "Pflüger"} ], "pages": "635--647", "bibtexKey": "heene2016massivelyparallel" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2a0136090b1fda342674ed39e03a08bec/tpollinger", "tags" : [ "plasma_physics","combination_technique","HPC","sparse_grids" ], "intraHash" : "a0136090b1fda342674ed39e03a08bec", "interHash" : "bdb1396717a0c726310b9f07a0ba6610", "label" : "A Massively Parallel Combination Technique for the Solution of High-Dimensional PDEs", "user" : "tpollinger", "description" : "", "date" : "2020-07-27 14:54:55", "changeDate" : "2020-07-27 13:30:40", "count" : 5, "pub-type": "phdthesis", "year": "2018", "url": "", "author": [ "Mario Heene" ], "authors": [ {"first" : "Mario", "last" : "Heene"} ], "bibtexKey": "heene2018massively" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/236ffce852a4026a7268d10072986677f/tpollinger", "tags" : [ "load_balancing","myown","machine_learning","plasma_physics","combination_technique","HPC","sparse_grids" ], "intraHash" : "36ffce852a4026a7268d10072986677f", "interHash" : "3e84370ab81750d0d4f980c6677091f5", "label" : "Learning-Based Load Balancing for Massively Parallel Simulations of Hot Fusion Plasmas", "user" : "tpollinger", "description" : "", "date" : "2020-07-27 14:42:05", "changeDate" : "2020-07-27 13:44:34", "count" : 2, "pub-type": "article", "journal": "Advances in Parallel Computing","publisher":"IOS Press", "year": "2020", "url": "http://doi.org/10.3233/APC200034", "author": [ "Theresa Pollinger","Dirk Pflüger" ], "authors": [ {"first" : "Theresa", "last" : "Pollinger"}, {"first" : "Dirk", "last" : "Pflüger"} ], "volume": "36","number": "Parallel Computing: Technology Trends","pages": "137\u2013146", "issn" : "0927-5452", "doi" : "10.3233/APC200034", "bibtexKey": "Pollinger2020" } ] }