10 citations · 25 across the 9 of their papers we have counts for
13 papers
Performance Modeling and Prediction for Dense Linear Algebra
Elmar Peise
This dissertation introduces measurement-based performance modeling and prediction techniques for dense linear algebra algorithms. As a core principle, these techniques avoid execu…
Large Scale Parallel Computations in R through Elemental
Rodrigo Canales, Elmar Peise, Paolo Bientinesi
Even though in recent years the scale of statistical analysis problems has increased tremendously, many statistical software tools are still limited to single-node computations. Ho…
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…
High-performance generation of the Hamiltonian and Overlap matrices in FLAPW methods
Edoardo Di Napoli, Elmar Peise, Markus Hrywniak +1
One of the greatest efforts of computational scientists is to translate the mathematical model describing a class of physical phenomena into large and complex codes. Many of these…
The ELAPS Framework: Experimental Linear Algebra Performance Studies
Elmar Peise, Paolo Bientinesi
Optimal use of computing resources requires extensive coding, tuning and benchmarking. To boost developer productivity in these time consuming tasks, we introduce the Experimental…
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…