10 citations · 25 across the 9 of their papers we have counts for
5 papers · 1 filter
Algorithm 979: Recursive Algorithms for Dense Linear Algebra -- The ReLAPACK Collection
Elmar Peise, Paolo Bientinesi
To exploit both memory locality and the full performance potential of highly tuned kernels, dense linear algebra libraries such as LAPACK commonly implement operations as blocked a…
On the Performance Prediction of BLAS-based Tensor Contractions
Elmar Peise, Diego Fabregat-Traver, Paolo Bientinesi
Tensor operations are surging as the computational building blocks for a variety of scientific simulations and the development of high-performance kernels for such operations is kn…
A Study on the Influence of Caching: Sequences of Dense Linear Algebra Kernels
Elmar Peise, Paolo Bientinesi
It is universally known that caching is critical to attain high- performance implementations: In many situations, data locality (in space and time) plays a bigger role than optimiz…
Performance Modeling for Dense Linear Algebra
Elmar Peise, Paolo Bientinesi
It is well known that the behavior of dense linear algebra algorithms is greatly influenced by factors like target architecture, underlying libraries and even problem size; because…
High-Performance Solvers for Dense Hermitian Eigenproblems
Matthias Petschow, Elmar Peise, Paolo Bientinesi
We introduce a new collection of solvers - subsequently called EleMRRR - for large-scale dense Hermitian eigenproblems. EleMRRR solves various types of problems: generalized, stand…