Search

Application codes in a variety of areas are being updated for performance on the latest architectures. In this paper we examine an application, which comes from magnetic fusion for performance acceleration with a particular emphasis on methods that are applicable for many/multicore and future architectural designs. We take an important magnetic fusion particle code that already includes several levels of parallelism including hybrid MPI combined with OpenMP. We study how to include new advanced hybrid models, which extend the applicability of OpenMP tasks and exploit multi-threaded MPI support to overlap communication and computation. Experiments carried out on Cray XT4 and XT5 machines resulting in a speed-up of up to 35% of the investigated GTS particle shifter kernel show the benefits and applicability of this approach. [ABSTRACT FROM AUTHOR]

Summary: Hermitian (symmetric) eigenvalue solvers are the core constituents of electronic structure, quantum‐chemistry, and other HPC applications such as Quantum ESPRESSO, VASP, CP2K, and NWChem to name a few. Our understanding of the performance of symmetric eigenvalue algorithms on various hardware is clearly important to the quantum chemistry or condensed matter physics community but in fact goes beyond that community. For instance, big data analytics is increasingly utilizing eigenvalues solvers, in the study of randomized singular value decomposition (SVD) or principal component analysis (PCA). Noise, vibration, and harshness (NVH) is another field where fast and efficient eigenvalue solvers are required. Most eigenvalue solver packages feature numerous different parameters which can be tuned for performance, eg, the number of nodes, number of total ranks, the decomposition of the matrix, etc. In this paper, we investigate the performance of different packages as well as the influence of these knobs on the solver performance. [ABSTRACT FROM AUTHOR]