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Analysis of Dynamically Scheduled Tile Algorithms for Dense Linear Algebra on Multicore Architectures
"... Abstract—The objective of this paper is to analyze the dynamic scheduling of dense linear algebra algorithms on sharedmemory, multicore architectures. Current numerical libraries, e.g., LAPACK, show clear limitations on such emerging systems mainly due to their coarse granularity tasks. Thus, many ..."
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Cited by 6 (4 self)
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Abstract—The objective of this paper is to analyze the dynamic scheduling of dense linear algebra algorithms on sharedmemory, multicore architectures. Current numerical libraries, e.g., LAPACK, show clear limitations on such emerging systems mainly due to their coarse granularity tasks. Thus, many
A class of parallel tiled linear algebra algorithms for multicore architectures
"... Abstract. As multicore systems continue to gain ground in the High Performance Computing world, linear algebra algorithms have to be reformulated or new algorithms have to be developed in order to take advantage of the architectural features on these new processors. Fine grain parallelism becomes a ..."
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Cited by 163 (57 self)
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Abstract. As multicore systems continue to gain ground in the High Performance Computing world, linear algebra algorithms have to be reformulated or new algorithms have to be developed in order to take advantage of the architectural features on these new processors. Fine grain parallelism becomes a
Energy Footprint of Advanced Dense Numerical Linear Algebra using Tile Algorithms on Multicore Architecture
"... Abstract—We propose to study the impact on the energy footprint of two advanced algorithmic strategies in the context of high performance dense linear algebra libraries: (1) mixed precision algorithms with iterative refinement allow to run at the peak performance of single precision floatingpoint a ..."
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Cited by 5 (1 self)
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algorithms, which will eventually supersede the block algorithms from LAPACK, both strategies further excel in performance in the presence of a dynamic task scheduler while targeting multicore architecture. Energy consumption measurements are reported along with parallel performance numbers on a dual
Scheduling Twosided Transformations using Tile Algorithms on Multicore Architectures
"... The objective of this paper is to describe, in the context of multicore architectures, three different scheduler implementations for the twosided linear algebra transformations, in particular the Hessenberg and Bidiagonal reductions which are the first steps for the standard eigenvalue problems and ..."
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Cited by 6 (2 self)
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popularity as a paradigm for programming multicore architectures. Buttari et al. [2007] introduced the concept of tile algorithms in which parallelism is no longer hidden inside Basic Linear Algebra Subprograms but is brought to the fore to yield much better performance. Along with efficient scheduling
Scheduling Twosided Transformations using AlgorithmsbyTiles on Multicore Architectures
"... The objective of this paper is to describe, in the context of multicore architectures, different scheduler implementations for the twosided linear algebra transformations, in particular the Hessenberg and Bidiagonal reductions which are the first steps for the standard eigenvalue problems and the ..."
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Cited by 3 (0 self)
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The objective of this paper is to describe, in the context of multicore architectures, different scheduler implementations for the twosided linear algebra transformations, in particular the Hessenberg and Bidiagonal reductions which are the first steps for the standard eigenvalue problems
Dynamic Task Scheduling for Linear Algebra Algorithms on DistributedMemory Multicore Systems
"... Multicore systems have increasingly gained importance in both sharedmemory and distributedmemory environments. This paper presents a dynamic task scheduling approach to executing dense linear algebra algorithms on multicore systems (either shared or distributedmemory). We use a taskbased librar ..."
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Cited by 35 (10 self)
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Multicore systems have increasingly gained importance in both sharedmemory and distributedmemory environments. This paper presents a dynamic task scheduling approach to executing dense linear algebra algorithms on multicore systems (either shared or distributedmemory). We use a task
Parallel tiled QR factorization for multicore architectures
, 2007
"... As multicore systems continue to gain ground in the High Performance Computing world, linear algebra algorithms have to be reformulated or new algorithms have to be developed in order to take advantage of the architectural features on these new processors. Fine grain parallelism becomes a major requ ..."
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Cited by 79 (40 self)
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As multicore systems continue to gain ground in the High Performance Computing world, linear algebra algorithms have to be reformulated or new algorithms have to be developed in order to take advantage of the architectural features on these new processors. Fine grain parallelism becomes a major
Tall and Skinny QR Matrix Factorization Using Tile Algorithms on Multicore Architectures
"... Abstract. To exploit the potential of multicore architectures, recent dense linear algebra libraries have used tile algorithms, which consist in scheduling a Directed Acyclic Graph (DAG) of tasks of fine granularity where nodes represent tasks, either panel factorization or update of a blockcolumn, ..."
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Cited by 1 (1 self)
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Abstract. To exploit the potential of multicore architectures, recent dense linear algebra libraries have used tile algorithms, which consist in scheduling a Directed Acyclic Graph (DAG) of tasks of fine granularity where nodes represent tasks, either panel factorization or update of a block
Parallel TwoStage Hessenberg Reduction using Tile Algorithms for Multicore Architectures
"... Abstract. This paper describes a parallel Hessenberg reduction in the context of multicore architectures using tile algorithms. The Hessenberg reduction is very often used as a preprocessing step in solving dense linear algebra problems, such as the standard eigenvalue problem. Although expensive, ..."
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Cited by 1 (0 self)
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Abstract. This paper describes a parallel Hessenberg reduction in the context of multicore architectures using tile algorithms. The Hessenberg reduction is very often used as a preprocessing step in solving dense linear algebra problems, such as the standard eigenvalue problem. Although expensive
Results 1  10
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