DAGuE

DAGuE: A generic distributed DAG engine for High Performance Computing. The frenetic development of the current architectures places a strain on the current state-of-the-art programming environments. Harnessing the full potential of such architectures is a tremendous task for the whole scientific computing community. We present DAGuE a generic framework for architecture aware scheduling and management of micro-tasks on distributed many-core heterogeneous architectures. Applications we consider can be expressed as a Direct Acyclic Graph of tasks with labeled edges designating data dependencies. DAGs are represented in a compact, problem-size independent format that can be queried on-demand to discover data dependencies, in a totally distributed fashion. DAGuE assigns computation threads to the cores, overlaps communications and computations and uses a dynamic, fully-distributed scheduler based on cache awareness, data-locality and task priority. We demonstrate the efficiency of our approach, using several micro-benchmarks to analyze the performance of different components of the framework, and a linear algebra factorization as a use case.


References in zbMATH (referenced in 10 articles , 1 standard article )

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  1. Dongarra, Jack; Gates, Mark; Haidar, Azzam; Kurzak, Jakub; Luszczek, Piotr; Wu, Panruo; Yamazaki, Ichitaro; Yarkhan, Asim; Abalenkovs, Maksims; Bagherpour, Negin; Hammarling, Sven; Šístek, Jakub; Stevens, David; Zounon, Mawussi; Relton, Samuel D.: PLASMA: Parallel linear algebra software for multicore using OpenMP (2019)
  2. Dongarra, Jack; Gates, Mark; Haidar, Azzam; Kurzak, Jakub; Luszczek, Piotr; Tomov, Stanimire; Yamazaki, Ichitaro: The singular value decomposition: anatomy of optimizing an algorithm for extreme scale (2018)
  3. Jeannot, Emmanuel; Fournier, Yvan; Lorendeau, Benjamin: Experimenting task-based runtimes on a legacy computational fluid dynamics code with unstructured meshes (2018)
  4. Elmar Peise; Paolo Bientinesi: Algorithm 979: Recursive Algorithms for Dense Linear Algebra - The ReLAPACK Collection (2017) not zbMATH
  5. Agullo, Emmanuel; Buttari, Alfredo; Guermouche, Abdou; Lopez, Florent: Implementing multifrontal sparse solvers for multicore architectures with sequential task flow runtime systems (2016)
  6. Ghysels, Pieter; Li, Xiaoye S.; Rouet, François-Henry; Williams, Samuel; Napov, Artem: An efficient multicore implementation of a novel HSS-structured multifrontal solver using randomized sampling (2016)
  7. Yu, Chenhan D.; Wang, Weichung: Performance models and workload distribution algorithms for optimizing a hybrid CPU-GPU multifrontal solver (2014)
  8. Bosilca, George; Bouteiller, Aurelien; Danalis, Anthony; Herault, Thomas; Lemarinier, Pierre; Dongarra, Jack: DAGuE: A generic distributed DAG engine for high performance computing (2012) ioport
  9. Saule, Erik; Baş, Erdeniz Ö.; Çatalyürek, Ümit V.: Load-balancing spatially located computations using rectangular partitions (2012)
  10. Agullo, Emmanuel; Giraud, Luc; Guermouche, Abdou; Roman, Jean: Parallel hierarchical hybrid linear solvers for emerging computing platforms (2011)