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Dive into the research topics where Daniel Langr is active.

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Featured researches published by Daniel Langr.


IEEE Transactions on Parallel and Distributed Systems | 2016

Evaluation Criteria for Sparse Matrix Storage Formats

Daniel Langr; Pavel Tvrdík

When authors present new storage formats for sparse matrices, they usually focus mainly on a single evaluation criterion, which is the performance of sparse matrix-vector multiplication (SpMV) in FLOPS. Though such an evaluation is essential, it does not allow to directly compare the presented format with its competitors. Moreover, in case that matrices are within an HPC application constructed in different formats, this criterion alone is not sufficient for the key decision whether or not to convert them into the presented format for the SpMV-based application phase. We establish ten evaluation criteria for sparse matrix storage formats, discuss their advantages and disadvantages, and provide general suggestions for format authors/evaluators to make their work more valuable for the HPC community.


Physical Review Letters | 2013

Collective modes in light nuclei from first principles.

T. Dytrych; Kristina D. Launey; J. P. Draayer; Pieter Maris; James P. Vary; Erik Saule; Masha Sosonkina; Daniel Langr; M. A. Caprio

Results for ab initio no-core shell model calculations in a symmetry-adapted SU(3)-based coupling scheme demonstrate that collective modes in light nuclei emerge from first principles. The low-lying states of 6Li, 8Be, and 6He are shown to exhibit orderly patterns that favor spatial configurations with strong quadrupole deformation and complementary low intrinsic spin values, a picture that is consistent with the nuclear symplectic model. The results also suggest a pragmatic path forward to accommodate deformation-driven collective features in ab initio analyses when they dominate the nuclear landscape.


cluster computing and the grid | 2005

Clondike: Linux cluster of non-dedicated workstations

Martin Kacer; Daniel Langr; Pavel Tvrdík

Clusters of workstations are a promising platform for high-performance computing. Most of such clusters are built of dedicated workstations that cannot be used for other purposes. Even though these clusters offer good price/performance ratio, their costs are not negligible. On the other hand, there exist many idle workstations connected via computer networks. The idea of exploiting such idle resources is obvious. In this paper, we describe an architecture of clusters made of non-dedicated idle workstations. These clusters aim at providing a single-system-image Linux environment. The main innovative feature is that a cluster administration is separate from administration of individual workstations. We discuss issues of security, availability, performance, and administration of such clusters. We also describe a pilot implementation and give promising experimental results.


symbolic and numeric algorithms for scientific computing | 2012

Minimal Quadtree Format for Compression of Sparse Matrices Storage

Ivan imecek; Daniel Langr; Pavel Tvrdík

Computations with sparse matrices are widespread in scientific projects. Commonly used storage formats (such as COO or CSR) are not suitable for I/O file operations with sparse matrices due to their high space complexities. Memory-efficient formats are still under development. In this paper, we present a new storage format called the Minimal quadtree (MQ) as well as algorithms for converting matrices from common storage formats to the MQ format. We compare the space complexity of common storage formats and of the MQ format and prove that the idea of using the quadtree as the data structure for sparse matrices is viable.


ieee international conference on high performance computing data and analytics | 2012

Space-efficient Sparse Matrix Storage Formats for Massively Parallel Systems

Ivan Å imecek; Daniel Langr; Pavel Tvrdík

In this paper, we propose and evaluate new storage formats for sparse matrices that minimize the space complexity of information about matrix structure. The motivation of our work are applications with very large sparse matrices that due to their size must be processed on massively parallel computer systems consisting of tens or hundreds of thousands of processor cores and that must be stored in a distributed file system using parallel I/O. The parallel I/O is typically the main performance bottleneck and reading or writing such matrices from/to distributed file system can take significant amount of time. We try to reduce this time by reducing the amount of data to be processed.


Physical Review C | 2015

Electron-scattering form factors for Li 6 in the ab initio symmetry-guided framework

T. Dytrych; A. C. Hayes; Kristina D. Launey; J. P. Draayer; Pieter Maris; James P. Vary; Daniel Langr; Tomáš Oberhuber

We present an ab initio symmetry-adapted no-core shell-model description for


Computer Physics Communications | 2016

Efficacy of the SU(3) scheme for ab initio large-scale calculations beyond the lightest nuclei

T. Dytrych; Pieter Maris; Kristina D. Launey; J. P. Draayer; James P. Vary; Daniel Langr; Erik Saule; M. A. Caprio; Masha Sosonkina

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symbolic and numeric algorithms for scientific computing | 2013

Space Efficient Formats for Structure of Sparse Matrices Based on Tree Structures

Ivan imecek; Daniel Langr; Pavel Tvrdík

Li. We study the structure of the ground state of


federated conference on computer science and information systems | 2016

Block iterators for sparse matrices

Daniel Langr; Ivan Šimeček; T. Dytrych

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ACM Transactions on Mathematical Software | 2014

Algorithm 947: Paraperm---Parallel Generation of Random Permutations with MPI

Daniel Langr; Pavel Tvrdík; T. Dytrych; J. P. Draayer

Li and the impact of the symmetry-guided space selection on the charge density components for this state in momentum space, including the effect of higher shells. We accomplish this by investigating the electron scattering charge form factor for momentum transfers up to

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T. Dytrych

Louisiana State University

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J. P. Draayer

Louisiana State University

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Ivan Šimeček

Czech Technical University in Prague

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Pavel Tvrdík

Czech Technical University in Prague

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A C Dreyfuss

Louisiana State University

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Tomáš Oberhuber

Czech Technical University in Prague

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C. Bahri

Louisiana State University

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