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ORCIDElmar Peise
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2010 – 2019
- 2019
[j5]Elmar Peise, Paolo Bientinesi:
The ELAPS framework: Experimental Linear Algebra Performance Studies. Int. J. High Perform. Comput. Appl. 33(2) (2019)- 2017
- [b1]Elmar Peise:
Performance modeling and prediction for dense linear algebra. RWTH Aachen University, Germany, 2017 - [j4]Edoardo Di Napoli
, Elmar Peise, Markus Hrywniak, Paolo Bientinesi:
High-performance generation of the Hamiltonian and Overlap matrices in FLAPW methods. Comput. Phys. Commun. 211: 61-72 (2017) - [j3]Elmar Peise, Paolo Bientinesi:
Algorithm 979: Recursive Algorithms for Dense Linear Algebra - The ReLAPACK Collection. ACM Trans. Math. Softw. 44(2): 16:1-16:19 (2017) - [i14]Elmar Peise:
Performance Modeling and Prediction for Dense Linear Algebra. CoRR abs/1706.01341 (2017) - 2016
- [i13]Edoardo Di Napoli, Elmar Peise, Markus Hrywniak, Paolo Bientinesi:
High-performance generation of the Hamiltonian and Overlap matrices in FLAPW methods. CoRR abs/1602.06589 (2016) - [i12]Elmar Peise, Paolo Bientinesi:
Recursive Algorithms for Dense Linear Algebra: The ReLAPACK Collection. CoRR abs/1602.06763 (2016) - [i11]Rodrigo Canales, Elmar Peise, Paolo Bientinesi:
Large Scale Parallel Computations in R through Elemental. CoRR abs/1610.07310 (2016) - 2015
- [j2]Elmar Peise, Diego Fabregat-Traver, Paolo Bientinesi:
High performance solutions for big-data GWAS. Parallel Comput. 42: 75-87 (2015) - [i10]Elmar Peise, Paolo Bientinesi:
The ELAPS Framework: Experimental Linear Algebra Performance Studies. CoRR abs/1504.08035 (2015) - 2014
- [c4]Elmar Peise, Diego Fabregat-Traver, Paolo Bientinesi:
On the Performance Prediction of BLAS-based Tensor Contractions. PMBS@SC 2014: 193-212 - [c3]Elmar Peise, Paolo Bientinesi:
A Study on the Influence of Caching: Sequences of Dense Linear Algebra Kernels. VECPAR 2014: 245-258 - [i9]Elmar Peise, Paolo Bientinesi:
A Study on the Influence of Caching: Sequences of Dense Linear Algebra Kernels. CoRR abs/1402.5897 (2014) - [i8]Elmar Peise, Diego Fabregat-Traver, Paolo Bientinesi:
High Performance Solutions for Big-data GWAS. CoRR abs/1403.6426 (2014) - [i7]Elmar Peise, Paolo Bientinesi:
Cache-aware Performance Modeling and Prediction for Dense Linear Algebra. CoRR abs/1409.8602 (2014) - [i6]Elmar Peise, Diego Fabregat-Traver, Paolo Bientinesi:
On the Performance Prediction of BLAS-based Tensor Contractions. CoRR abs/1409.8608 (2014) - 2013
- [j1]Matthias Petschow, Elmar Peise, Paolo Bientinesi:
High-Performance Solvers for Dense Hermitian Eigenproblems. SIAM J. Sci. Comput. 35(1) (2013) - [c2]Elmar Peise, Diego Fabregat-Traver, Yurii S. Aulchenko, Paolo Bientinesi:
Algorithms for large-scale whole genome association analysis. EuroMPI 2013: 229-234 - [i5]Elmar Peise, Diego Fabregat-Traver, Yurii S. Aulchenko, Paolo Bientinesi:
Algorithms for Large-scale Whole Genome Association Analysis. CoRR abs/1304.2272 (2013) - 2012
- [c1]Elmar Peise, Paolo Bientinesi:
Performance Modeling for Dense Linear Algebra. SC Companion 2012: 406-416 - [i4]Matthias Petschow, Elmar Peise, Paolo Bientinesi:
High-Performance Solvers for Dense Hermitian Eigenproblems. CoRR abs/1205.2107 (2012) - [i3]Elmar Peise:
Hierarchical Performance Modeling for Ranking Dense Linear Algebra Algorithms. CoRR abs/1207.5217 (2012) - [i2]Elmar Peise, Paolo Bientinesi:
Performance Modeling for Dense Linear Algebra. CoRR abs/1209.2364 (2012)
2000 – 2009
- 2009
- [i1]Viktor Mosenkis, Uwe Naumann, Elmar Peise:
Low-Memory Tour Reversal in Directed Graphs. Combinatorial Scientific Computing 2009
Coauthor Index
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