GPU laboratoř

GPU laboratoř je primárně určena pro vzdálený vývoj vědecko-technických programů využívající výpočtů na GPU (procesorech grafických karet).

Aplikace
Čemu se laboratoř věnuje

Laboratoř využíváme ve výuce předmětů Programování v CUDA (MI-PRC) a Paralelní architektury počítačů (MI-PAP.16) a pro vzdálený vývoj semestrálních a diplomových prací v tomto oboru. Stroje, které tu máme, slouží k předběžnému měření ve výzkumu v oblasti HPC.

Členové laboratoře

Publikace

Multilayer Approach for Joint Direct and Transposed Sparse Matrix Vector Multiplication for Multithreaded CPUs

Autoři
Šimeček, I.; Langr, D.; Kotenkov, I.
Rok
2018
Publikováno
Parallel Processing and Applied Mathematics Part I.. Cham: Springer International Publishing AG, 2018. p. 47-56. Lecture Notes in Computer Science. vol. 10777. ISSN 0302-9743. ISBN 978-3-319-78023-8.
Typ
Stať ve sborníku
Anotace
One of the most common operations executed on modern high-perfor\-mance computing systems is multiplication of a sparse matrix by a dense vector within a shared-memory computational node. Strongly related but far less studied problem is joint direct and transposed sparse matrix-vector multiplication, which is widely needed by certain types of iterative solvers. We propose a multilayer approach for joint sparse multiplication that balances the workload of threads. Measurements prove that our algorithm is scalable and achieve high computational performance for multiple benchmark matrices that arise from various scientific and engineering disciplines.

Parallel solver of large systems of linear inequalities using Fourier--Motzkin elimination

Autoři
Rok
2016
Publikováno
Computing and Informatics. 2016, 35(6), 1307-1337. ISSN 1335-9150.
Typ
Článek
Anotace
Fourier-Motzkin elimination is a computationally expensive but powerful method to solve a system of linear inequalities. These systems arise e.g. in execution order analysis for loop nests or in integer linear programming. This paper focuses on the analysis, design and implementation of a~parallel solver for distributed memory for large systems of linear inequalities using the Fourier--Motzkin elimination algorithm. We also measure the speedup of parallel solver and prove that this implementation results in good scalability.

Všechny publikace

Efficient parallel evaluation of block properties of sparse matrices

Rok
2016
Publikováno
Proceedings of the 2016 Federated Conference on Computer Science and Information Systems. New York: Institute of Electrical and Electronics Engineers, 2016. pp. 709-716. ISBN 978-83-60810-90-3.
Typ
Stať ve sborníku
Anotace
Many storage formats for sparse matrices have been developed. Majority of these formats can be parametrized, so the algorithm for finding optimal parameters is crucial. For overall efficiency, it is important to reduce the execution time of this preprocessing. In this paper, we propose a new algorithm for the determination of the number of nonzero blocks of the given size in a sparse matrix. The proposed algorithm requires relatively a small amount of auxiliary memory. Our approach is based on the Morton reordering and bitwise manipulations. We also present a parallel (multithreaded) version and evaluate its performance and space complexity.

Utilization of GPU Acceleration in Le Bail Fitting Method

Autoři
Šimeček, I.; Mařík, O.; Jelínek, M.
Rok
2015
Publikováno
Romanian Journal of Information Science and Technology (ROMJIST). 2015, 18(2), 182-196. ISSN 1453-8245.
Typ
Článek
Anotace
Le Bail fitting method is a process used in applied crystallography. It can be employed in several phases of crystal structure determination and as it is only one step in a more complex process, it needs to be as fast as possible. This article begins with a short explanation of crystallography terms needed to understand the Le Bail fitting, then continues with the description of the Le Bail fitting method itself and basic principles on which it is based. Then the parallelization method is explained, starting with a more general process, followed by specifics of GPU accelerated computing including short part on optimization. Finally, achieved results are presented along with comparison to sequential implementation and alternative parallelization approaches.

GPU solver for systems of linear equations with infinite precision

Autoři
Rok
2016
Publikováno
17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing. Los Alamitos: IEEE Computer Society, 2016. pp. 121-124. ISBN 978-1-5090-0461-4.
Typ
Stať ve sborníku
Anotace
In this paper, we would like to introduce a GPU accelerated solver for systems of linear equations with an infinite precision. The infinite precision means that the system can provide a precise solution without any rounding error. These errors usually come from limited precision of floating point values within their natural computer representation. In a simplified description, the system is using modular arithmetic for transforming an original SLE into dozens of integer SLEs that are solved in parallel via GPU. In the final step, partial results are used for a calculation of the final solution. The usage of GPU plays a key role in terms of performance because the whole process is computationally very intensive. The GPU solver can provide about one magnitude higher performance than a multithreaded one.

Kde nás najdete?

GPU laboratoř
Katedra počítačových systémů
Fakulta informačních technologií
České vysoké učení technické v Praze

Místnost TH:A-1313 (Budova A, 13. patro)
Thákurova 7
Praha 6 – Dejvice
160 00

Kontaktní osoba

doc. Ing. Ivan Šimeček, Ph.D.

Za obsah stránky zodpovídá: doc. Ing. Štěpán Starosta, Ph.D.