12 papers · ranked by Valyu relevance
Chansup Byun, William Arcand, David Bestor, Bill Bergeron + 17 more
'Vijay Gadepally' 'Michael E. Houle' 'Matthew Hubbell' 'Hayden Jananthan' 'Michael Jones' 'Kurt Keville' 'Anna Klein' 'Peter Michaleas' 'Lauren Milechin' 'Guillermo Morales' 'Julie Mullen' 'Andrew Prout' 'Albert Reuther' 'Antonio De Rosa' 'Siddharth Samsi' 'Charles Yee' 'Jeremy Kepner'] Abstract—pPython seeks to…
Alexandra Yang
| 1 | Introduction | | 2 | | --- | --- | --- | --- | | 2 | Related Research | | 2 | | 3 | Installation | | 3 | | 4 | Benchmarking | | 3 | | 5 | Single Processor Sorting Algorithms | | 3 | | | 5.1 Sequential Merge Sort | | 3 | | | 5.2 | Python Built-in Sort | 4 | | | 5.3 Multiprocessing Merge Sort | | 4 | | | 5.3.1 |…
Vladislav Skorpil, Vaclav Oujezsky, Arcangelo Castiglione, Gianni D’Angelo
'Gianni D’Angelo'] This paper presents an implementation of the parallelization of genetic algorithms. Three models of parallelized genetic algorithms are presented, namely the Master-Slave genetic algorithm, the Coarse-Grained genetic algorithm, and the Fine-Grained genetic algorithm. Furthermore, these models are…
Oscar Castro, Pierrick Bruneau, Jean-Sébastien Sottet, Dario Torregrossa
'Dario Torregrossa'] Python has become the prime language for application development in the Data Science and Machine Learning domains. However, data scientists are not necessarily experienced programmers. While Python lets them quickly implement their algorithms, when moving at scale, computation efficiency becomes…
Chansup Byun, William Arcand, David Bestor, Bill Bergeron + 16 more
'Vijay Gadepally' 'Michael E. Houle' 'Matthew Hubbell' 'Hayden Jananthan' 'Michael Jones' 'Anna Klein' 'Peter Michaleas' 'Lauren Milechin' 'Guillermo Morales' 'Julie Mullen' 'Andrew Prout' 'Albert Reuther' 'Antonio Rosa' 'Siddharth Samsi' 'Charles Yee' 'Jeremy Kepner'] Abstract—pPython seeks to provide a parallel…
Stephen D. Hudson, Jeffrey Larson, John-Luke Navarro, Stefan M. Wild
libEnsemble is a Python-based toolkit for running dynamic ensembles, developed as part of the DOE Exascale Computing Project. The toolkit utilizes a unique generator–simulator– allocator paradigm, where generators produce input for simulators, simulators evaluate those inputs, and allocators decide whether and when a…
Elena Gelžinytė, Simon Wengert, Tamás K. Stenczel, Hendrik H. Heenen + 3 more
'Karsten Reuter' 'Gábor Cśanyi' 'Noam Bernstein'] Predictive atomistic simulations are increasingly employed for data intensive high throughput studies that take advantage of constantly growing computational resources. To handle the sheer number of individual calculations that are needed in such studies, workflow…
Fabian Woller, Lis Arend, Christian Fuchsberger, Markus List + 1 more
To demonstrate the practical applicability of NApy on a real-world dataset, all statistical tests were performed on the CHRIS study data both without any parallelization (single thread) and with heavy parallelization (64 threads). For correlations, pandas was used as comparison as it was the fastest compared to other…
Bahman Arasteh, Seyed Salar Sefati, Huseyin Kusetogullari, Farzad Kiani + 3 more
Efficient task scheduling remains a key challenge in High-Performance Computing and Internet of Things (IoT) systems, where the sequential execution of nested loops often limits parallelism. This paper proposes a hybrid approach that dynamically parallelizes nested loops in heterogeneous IoT environments. The suggested…
Emad Alamoudi, Felipe Reck, Nils Bundgaard, Frederik Graw + 4 more
'Lutz Brusch' 'Jan Hasenauer' 'Yannik Schälte' 'Abel C.H. Chen'] Approximate Bayesian Computation (ABC) is a widely applicable and popular approach to estimating unknown parameters of mechanistic models. As ABC analyses are computationally expensive, parallelization on high-performance infrastructure is often…
Jan Janssen, Janine George, Julian Geiger, Marnik Bercx + 7 more
Numerous Workflow Management Systems (WfMS) have been developed in the field of computational materials science with different workflow formats, hindering interoperability and reproducibility of workflows in the field. To address this challenge, we introduce here the Python Workflow Definition (PWD) as a workflow…
Reza Rafati Bonab, Ali Akbar Jamali, Kyle Klenk, Mohammad Mahdi Moayeri + 2 more
Parallelization of the SW algorithm can be implemented on different levels: single-instruction, multiple-data (SIMD) parallelism, thread-level parallelism, process-level parallelism, and heterogeneous parallelism. SIMD parallelism, also known as vector-level parallelism, employs a controller to manage multiple…