doc. Ing. Ivan Šimeček, Ph.D.

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

2015

The Applicated Numerics and Cryptology research group studies various security components of information systems, especially at the research and development true random generators and physically unclonable functions, cryptoanalysis of block and stream ciphers, factorization of large numbers and efficient solving large systems of linear equations. The proposed project also addresses these thematic research areas.

2016

This project is aimed at several aspects of security of information systems inclusing identification of potential security threats. Main topics are following: cryptoanalysis of block and stream ciphers, efficient solving large systems of linear equations with an infinite precision, and the research and development true random generators and physically unclonable functions,

2020 - 2022

The proposed project is based on previous research results in the field of parallel and distributed algorithms for solution of some important engineer's problems. More specifically, the project is going to focus 1) on the methodology and algorithms for a systematic evaluation of sparse matrix and tensor storage formats that would allow a complex understanding of their properties, 2) on the application of machine learning in network security, 3) on the most advanced computer science methods in astronomy to allow preprocessing, storing, sharing, and analyzing of petabytes of data flowing continuously from astronomical instruments, 4) on the research of algorithms for multithreaded memoization system focused on their effective implementation and parallelization.

2017 - 2019

The proposed project is based on previous research results in the field of parallel and distributed algorithms for solution of some important engineer's problems. More specifically, the project is going to focus 1) on the most advanced computer science methods in astronomy to allow preprocessing, storing, sharing, and analysing of petabytes of data flowing continuously from astronomical instruments, 2) on the methodology and algorithms for a systematic evaluation of sparse matrix storage formats that would allow a complex understanding of their properties, 3) on the research of algorithms for solving the convex hull problem in 2 and 3 dimensions, focused on their effective implementation and parallelization.

2023 - 2025

The proposed project is based on previous research results in the field of parallel and distributed algorithms for solution of some important engineer's problems. More specifically, the project is going to focus 1) on the most advanced computer science methods in astronomy to allow preprocessing, storing, sharing, and analyzing of petabytes of data flowing continuously from astronomical instruments, 2) on the methodology and algorithms for a systematic evaluation of sparse matrix and tensor storage formats that would allow a complex understanding of their properties, 3) on parallel algorithms for reconstruction of physical objects from recorded data.

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.