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Adapting concurrency throttling and voltage–frequency scaling for dense eigensolvers
Springer Verlag (2015)We analyze power dissipation and energy consumption during the execution of high-performance dense linear algebra kernels on multi-core processors. On top of this analysis, we propose and evaluate several strategies to ... -
Balancing task- and data-level parallelism to improve performance and energy consumption of matrix computations on the Intel Xeon Phi
Elsevier (2015-08)The emergence of new manycore architectures, such as the Intel Xeon Phi, poses new challenges in how to adapt existing libraries and applications to this type of systems. In particular, the exploitation of manycore ... -
DVFS-control techniques for dense linear algebra operations on multi-core processors
Springer (2012-11)This paper analyzes the impact on power consumption of two DVFS-control strategies when applied to the execution of dense linear algebra operations on multi-core processors. The strategies considered here, prototyped as ... -
DVFS-Technique for Dense Linear Algebra Operations on Multi-Core Processors
Departament d' Enginyeria i Ciència dels Computadors, Universitat Jaume I (2011-05)This paper addresses the efficient explotation of task-level parallelism, present in many dense linear algebra operations, from the point of view of both computational performance and energy consumption. In particular, ... -
Energy Balance between Voltage-Frequency Scaling and Resilience for Linear Algebra Routines on Low-Power Multicore Architectures
Elsevier (2017)Near Threshold Voltage (NTV) computing has been recently proposed as a technique to save energy, at the cost of incurring higher error rates including, among others, Silent Data Corruption (SDC). In this paper, we evaluate ... -
Energy Balance between Voltage-Frequency Scaling and Resilience for Linear Algebra Routines on Low-Power Multicore Architectures
Elsevier (2018)Near Threshold Voltage (NTV) computing has been recently proposed as a technique to save energy, at the cost of incurring higher error rates including, among others, Silent Data Corruption (SDC). In this paper, we evaluate ... -
Energy-efficient execution of dense linear algebra algorithms on multi-core processors
Springer Verlag (2013-09)This paper addresses the efficient exploitation of task-level parallelism, present in many dense linear algebra operations, from the point of view of both computational performance and energy consumption. The strategies ... -
Enhancing performance and energy consumption of runtime schedulers for dense linear algebra
Wiley (2014-06)The road towards Exascale Computing requires a holistic effort to address three different challenges simultaneously: high performance, energy efficiency, and programmability. The use of runtime task schedulers to orchestrate ... -
Modeling power and energy of the task-parallel Cholesky factorization on multicore processors
Springer Berlin Heidelberg (2014-05)In this paper we introduce a model for the total energy consumption of the Cholesky factorization on a multicore processor. Our model assumes a task-parallel execution of the factorization process, with concurrency leveraged ... -
Solving “Large” Dense Matrix Problems on Multi-Core Processors and GPUs
Departament d' Enginyeria i Ciència dels Computadors, Universitat Jaume I (2009-01)Few realize that, for large matrices, many dense matrix computations achieve nearly the same performance when the matrices are stored on disk as when they are stored in a very large main memory. Similarly, few realize ... -
Static scheduling of the LU factorization with look-ahead on asymmetric multicore processors
Elsevier (2018)We analyze the benefits of look-ahead in the parallel execution of the LU factorization with partial pivoting (LUpp) in two distinct “asymmetric” multicore scenarios. The first one corresponds to an actual hardware-asymmetric ... -
The libflame library for dense matrix computations
IEEE Computer Society (2009-11)Researchers from the Formal Linear Algebra Method Environment (Flame) project have developed new methodologies for analyzing, designing, and implementing linear algebra libraries. These solutions, which have culminated in ... -
Two-sided orthogonal reductions to condensed forms on asymmetric multicore processors
Elsevier (2018)We investigate how to leverage the heterogeneous resources of an Asymmetric Multicore Processor (AMP) in order to deliver high performance in the reduction to condensed forms for the solution of dense eigenvalue and ... -
Using desktop computers to solve large-scale dense linear algebra problems
Springer Science+Business Media (2011-11)We provide experimental evidence that current desktop computers feature enough computational power to solve large-scale dense linear algebra problems. While the high computational cost of the numerical methods for solving ...