Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis
High performance matrix inversion based on LU factorization for multicore architectures
Proceedings of the 2011 ACM international workshop on Many task computing on grids and supercomputers
From serial loops to parallel execution on distributed systems
Euro-Par'12 Proceedings of the 18th international conference on Parallel Processing
High-performance bidiagonal reduction using tile algorithms on homogeneous multicore architectures
ACM Transactions on Mathematical Software (TOMS)
Hierarchical QR factorization algorithms for multi-core clusters
Parallel Computing
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We present a method for developing dense linear algebra algorithms that seamlessly scales to thousands of cores. It can be done with our project called DPLASMA (Distributed PLASMA) that uses a novel generic distributed Direct Acyclic Graph Engine (DAGuE). The engine has been designed for high performance computing and thus it enables scaling of tile algorithms, originating in PLASMA, on large distributed memory systems. The underlying DAGuE framework has many appealing features when considering distributed-memory platforms with heterogeneous multicore nodes: DAG representation that is independent of the problem-size, automatic extraction of the communication from the dependencies, overlapping of communication and computation, task prioritization, and architecture-aware scheduling and management of tasks. The originality of this engine lies in its capacity to translate a sequential code with nested-loops into a concise and synthetic format which can then be interpreted and executed in a distributed environment. We present three common dense linear algebra algorithms from PLASMA~(Parallel Linear Algebra for Scalable Multi-core Architectures), namely: Cholesky, LU, and QR factorizations, to investigate their data driven expression and execution in a distributed system. We demonstrate through experimental results on the Cray XT5 Kraken system that our DAG-based approach has the potential to achieve sizable fraction of peak performance which is characteristic of the state-of-the-art distributed numerical software on current and emerging architectures.