Ing. Daniel Langr, Ph.D.

2022 - 2024

Many ongoing and future experiments utilize medium-mass atomic nuclei as exquisite laboratories for exploring the fundamental symmetries of nature and searching for signals of new physics beyond the Standard Model. While large particle colliders probe the high-energy frontier, sensitive studies of low-energy nuclear processes provide a complementary approach. We will exploit a novel combination of physics, mathematics, and advanced computational methods to build a novel quantum many-body framework for solving the structure of medium- mass nuclei and their processes from first principles with unprecedented accuracy and scope. This will allow us to provide a number of key nuclear theory predictions needed for the interpretation of ongoing and future experimental efforts to detect dark matter and to search for violations of predictions of the Standard model in electroweak nuclear processes.

2014

The proposed project is based on previous research results in the field of parallel and distributed computing systems. It is aimed at massively parallel computing, GPU computing, cluster computing, and global grid computing systems. More specifically, the project is going to focus 1) on the reseach of the architecture of the nondedicated cluster architecture with the focus on the algorithms for distributed task scheduling in such clusters, 2) on the design of efficient algorithms for crystal structure determination based on powder diffraction method on massively parallel GPU clusters, 3) on the parallelization of immune-system-inspired algorithms on GPU clusters, 4) on the design of efficient algorithms for data acquisition and visualization of very large sparse matrices mapped row-wise/column-wise on processors of massively parallel multiprocessor systems, and 5) on the reseach of heuristic algorithms for resource allocation in worldwide computing grids.

2012

This project consists of several research subprojects that investigate various interesting parallel programming aspects. The first research project concerns with sparse matrix storage formats. It is focused on aspects of quad-tree abstract data type for sparse matrix representation. Preliminary results indicate high potential of quad-tree format, but it is necessary to implement more functions and measure the performance thoroughly. The next part of the project is algorithms for visualization of very large sparse matrices. The recently developed algorithm is independent of partitioning of a matrix among individual processors of a parallel computer. The price for this independence is that the memory requirements are proportional to the size of the image, which limits the detail investigation of the matrix structure. The goal is to develop memory efficient algorithms for visualization of particular types of sparse matrices that will lead to the possibility of creation of much bigger re

2011 - 2012

The project goal is to design and create a framework for storing large sparse matrices onto disk subsystems and their loading back. The framework will be included in SA-NCSM (symmetry adapted no-core shell model) solver.

2016 - 2018

One of the most important and open problems in contemporary physics is the precise description of inter-nucleon interaction, which is derived from the underlying principles of Quantum Chromodynamics, and application of this interaction for first-principle computations of nuclear structure and reactions. In this research effort, a group of nuclear physicists and computer scientists will develop of novel approaches and methods for modeling of light and medium-mass nuclei and their properties, and will implement these methods in form of highly parallel algorithms for modern supercomputing architectures. This will facilitate first-principle modeling of nuclear structure and will provide a reliable structural information important for studying nuclear reactions, modeling of neutrinos interactions with atomic nuclei, or testing physics beyond the Standard Model.

2010 - 2012

The main goal of this project is to create an experimental computing grid for scientific computations mainly focused on linear algebra. This grid will execute the special version of libraries (BLAS/LAPACK) for the numeric linear algebra. When the user wants to execute some computations, the heuristic in the client part estimates if it will be faster to do a local computation or send input data to the grid and wait for reply (output data).

2012 - 2014

Algorithms for solving so called "Grand challenge problems" lead to huge data sets, typically organized as sparse matrices. This project addresses the research of effective and scalable algorithms and data structures for input/output operations on very large sparse matrices that due to their size must be stored and processed on massively parallel computers with tens or hundreds of thousands of processors. Such matrices consist of trillions of nonzero entries. The project focuses on research of new binary file formats for storing such matrices, on research of data structures and scalable algorithms for effective loading such matrices into massively parallel solvers, and on research of memory-effective formats for representation of such matrices in computer memory. Finally, the project also aims at research of effective and scalable algorithms for visualization of very large sparse matrices on massively parallel computers. Together with theoretical parts, the project involves verification of proposed algorithms and data structures on real massively parallel computers.

2011

Visualization of sparse matrices is mostly performed on desktop systems by programs such as Matlab or Mathematica. This approach cannot be used in case of large matrices that are spread across many nodes of a massively parallel supercomputer. The goal of this project is to develop efficient visualization techniques for large sparse matrices that could be easily integrated into an existing codes based on the MPI parallel programming model.