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- Moritz Kreutzer, Georg Hager, Gerhard Wellein, Holger Fehske, Alan R. Bishop
- SIAM J. Scientific Computing
- 2014

Sparse matrix-vector multiplication (spMVM) is the most time-consuming kernel in many numerical algorithms and has been studied extensively on all modern processor and accelerator architectures. However, the optimal sparse matrix data storage format is highly hardware-specific, which could become an obstacle when using heterogeneous systems. Also, it is as… (More)

- Gerald Schubert, Holger Fehske, Georg Hager, Gerhard Wellein
- Parallel Processing Letters
- 2011

We evaluate optimized parallel sparse matrix-vector operations for several representative application areas on widespread multicore-based cluster configurations. First the single-socket baseline performance is analyzed and modeled with respect to basic architectural properties of standard multicore chips. Beyond the single node, the performance of parallel… (More)

- Eric Jeckelmann, Holger Benthien, H. Fehske, R. Schneider
- 2007

The dynamical density-matrix renormalization group (DDMRG) method is a numerical technique for calculating the zero-temperature dynamical properties in low-dimensional quantum many-body systems. For the one-dimensional Hubbard model and its extensions, DDMRG allows for accurate calculations of these properties for lattices with hundreds of sites and… (More)

- Gerhard Wellein, Georg Hager, Thomas Zeiser, Markus Wittmann, Holger Fehske
- 2009 33rd Annual IEEE International Computer…
- 2009

We present a pipelined wavefront parallelization approach for stencil-based computations. Within a fixed spatial domain successive wavefronts are executed by threads scheduled to a multicore processor chip with a shared outer level cache. By re-using data from cache in the successive wavefronts this multicore-aware parallelization strategy employs temporal… (More)

- Moritz Kreutzer, Georg Hager, Gerhard Wellein, Holger Fehske, Alan R. Bishop
- ArXiv
- 2013

Sparse matrix-vector multiplication (spMVM) is the most time-consuming kernel in many numerical algorithms and has been studied extensively on all modern processor and accelerator architectures. However, the optimal sparse matrix data storage format is highly hardware-specific, which could become an obstacle when using heterogeneous systems. Also, it is as… (More)

- Moritz Kreutzer, Georg Hager, Gerhard Wellein, Holger Fehske, Achim Basermann, Alan R. Bishop
- 2012 IEEE 26th International Parallel and…
- 2012

Sparse matrix-vector multiplication (spMVM) is the dominant operation in many sparse solvers. We investigate performance properties of spMVM with matrices of various sparsity patterns on the nVidia "Fermi" class of GPGPUs. A new "padded jagged diagonals storage" (pJDS) format is proposed which may substantially reduce the memory overhead intrinsic to the… (More)

- Andreas Alvermann, Holger Fehske
- J. Comput. Physics
- 2011

- Gerhard Wellein, Georg Hager, Achim Basermann, Holger Fehske
- VECPAR
- 2002

- Melven Röhrig-Zöllner, Jonas Thies, +6 authors Holger Fehske
- SIAM J. Scientific Computing
- 2015

- Gerald Schubert, Georg Hager, Holger Fehske, Gerhard Wellein
- 2011 IEEE International Symposium on Parallel and…
- 2011

We evaluate optimized parallel sparse matrix-vector operations for two representative application areas on widespread multicore-based cluster configurations. First the single-socket baseline performance is analyzed and modeled with respect to basic architectural properties of standard multicore chips. Going beyond the single node, parallel sparse… (More)