1,225 research outputs found
Parallelization Strategies for Density Matrix Renormalization Group Algorithms on Shared-Memory Systems
Shared-memory parallelization (SMP) strategies for density matrix
renormalization group (DMRG) algorithms enable the treatment of complex systems
in solid state physics. We present two different approaches by which
parallelization of the standard DMRG algorithm can be accomplished in an
efficient way. The methods are illustrated with DMRG calculations of the
two-dimensional Hubbard model and the one-dimensional Holstein-Hubbard model on
contemporary SMP architectures. The parallelized code shows good scalability up
to at least eight processors and allows us to solve problems which exceed the
capability of sequential DMRG calculations.Comment: 18 pages, 9 figure
Coordinated Caching for High Performance Calibration using Z -> µµ Events of the CMS Experiment
Calibration of the detectors is a prerequisite for almost all physics analyses conducted as part of the LHC experiment. As such, both speed and precision are critical. As part of this thesis, a high performance analysis infrastructure using coordinated caching has been developed. This has been used to conduct the first calibration of jets using Z -> µµ events recorded during the second LHC run at the CMS experiment
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Automation of Determination of Optimal Intra-Compute Node Parallelism
Maximizing the productivity of modern multicore and manycore chips requires optimizing parallelism at the compute node level. This is, however, a complex multi-step process. It is an iterative method requiring determining optimal degrees of parallel scalability and optimizing memory access behavior. Further, there are multiple cases to be considered, programs which use only MPI or OpenMP and hybrid (MPI +OpenMP) programs. This paper presents a set of three coordinated workflows for determining the optimal parallelism at the program level for MPI programs and at the loop level for hybrid (MPI+OpenMP) cases. The paper also details mostly automated implementations of these workflows using the PerfExpert infrastructure. Finally the paper presents case studies demonstrating both the applicability and the effectiveness of optimizing parallelism at the compute node level. The results shown in the paper will provide valuable information to further advance in the full automation of the workflows. The software implementing the parallelism scalability optimization is open source and available for download.Texas Advanced Computing Center (TACC)Computer Science
Enhancing Energy Production with Exascale HPC Methods
High Performance Computing (HPC) resources have become the key actor for achieving more ambitious challenges in many disciplines. In this step beyond, an explosion on the available parallelism and the use of special purpose
processors are crucial. With such a goal, the HPC4E project applies new exascale HPC techniques to energy industry simulations, customizing them if necessary, and going beyond the state-of-the-art in the required HPC exascale
simulations for different energy sources. In this paper, a general overview of these methods is presented as well as some specific preliminary results.The research leading to these results has received funding from the European Union's Horizon 2020 Programme (2014-2020) under the HPC4E Project (www.hpc4e.eu), grant agreement n° 689772, the Spanish Ministry of
Economy and Competitiveness under the CODEC2 project (TIN2015-63562-R), and
from the Brazilian Ministry of Science, Technology and Innovation through Rede
Nacional de Pesquisa (RNP). Computer time on Endeavour cluster is provided by the
Intel Corporation, which enabled us to obtain the presented experimental results in
uncertainty quantification in seismic imagingPostprint (author's final draft
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