23 papers · ranked by Valyu relevance
Donald S. Ene, V.I.E Anireh
- Evaluating how well a whole system or set of subsystems performs is one of the primary objectives of performance testing. We can tell via performance assessment if the architecture implementation meets the design objectives. Performance evaluations of several parallel algorithms are compared in this study. Both…
Sirilak Ketchaya, Apisit Rattanatranurak
Quicksort is an important algorithm that uses the divide and conquer concept, and it can be run to solve any problem. The performance of the algorithm can be improved by implementing this algorithm in parallel. In this paper, the parallel sorting algorithm named the Multi-Deque Partition Dual-Deque Merge Sorting…
Jesper Larsson Träff
These lecture notes are designed to accompany an imaginary, virtual, undergraduate, one or two semester course on fundamentals of Parallel Computing as well as to serve as background and reference for graduate courses on High-Performance Computing, parallel algorithms and shared-memory multiprocessor programming. They…
Hassan Mushtaq, Sajid Gul Khawaja, Muhammad Usman Akram, Amanullah Yasin + 3 more
Clustering is the most common method for organizing unlabeled data into its natural groups (called clusters), based on similarity (in some sense or another) among data objects. The Partitioning Around Medoids (PAM) algorithm belongs to the partitioning-based methods of clustering widely used for objects categorization…
Patrizio Dazzi
Embarrassingly parallel problems are characterised by a very small amount of information to be exchanged among the parts they are split in, during their parallel execution. As a consequence they do not require sophisticated, low-latency, high-bandwidth interconnection networks but can be efficiently computed in…
Eray Özkural, Cevdet Aykanat
All-pairs similarity problem asks to find all vector pairs in a set of vectors the similarities of which surpass a given similarity threshold, and it is a computational kernel in data mining and information retrieval for several tasks. We investigate the parallelization of a recent fast sequential algorithm. We propose…
Martin Werner
This paper provides an abstract analysis of parallel processing strategies for spatial and spatio-temporal data. It isolates aspects such as data locality and computational locality as well as redundancy and locally sequential access as central elements of parallel algorithm design for spatial data. Furthermore, the…
Guido Schryen
Solving optimization problems with parallel algorithms has a long tradition in OR. Its future relevance for solving hard optimization problems in many fields, including finance, logistics, production and design, is leveraged through the increasing availability of powerful computing capabilities. Acknowledging the…
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…
Ken D Nguyen, Yi Pan, Ge Nong
Background One of the most fundamental and challenging tasks in bio-informatics is to identify related sequences and their hidden biological significance. The most popular and proven best practice method to accomplish this task is aligning multiple sequences together. However, multiple sequence alignment is a computing…
Wilfried Agbeto, Camille Coti, Vladimir Reinharz
Advances in graph algorithmics have allowed in-depth study of many natural objects from molecular biology or chemistry to social networks. Particularly in molecular biology and cheminformatics, understanding complex structures by identifying conserved sub-structures is a key milestone towards the artificial design of…
J Kyle Medley, Shaik Asifullah, Joseph Hellerstein, Herbert M Sauro
Mechanistic kinetic models of biological pathways are an important tool for understanding biological systems. Constructing kinetic models requires fitting the parameters to experimental data. However, parameter fitting on these models is a non–convex, non–linear optimization problem. Many algorithms have been proposed…
Christoph Stelz, Lukas Hübner, Alexandros Stamatakis
Phylogenetic trees describe the evolutionary history among biological species based on their genomic data. Maximum Likelihood (ML) based phylogenetic inference tools search for the tree and evolutionary model that best explain the observed genomic data. Given the independence of likelihood score calculations between…
Zeyu Xia, Canqun Yang, Chenchen Peng, Yifei Guo + 3 more
'Tao Tang' 'Yingbo Cui'] Background The advent of Single Molecule Real-Time (SMRT) sequencing has overcome many limitations of second-generation sequencing, such as limited read lengths, PCR amplification biases. However, longer reads increase data volume exponentially and high error rates make many existing alignment…
Stuart Byma, Akash Dhasade, Adrian Altenhoff, Christophe Dessimoz + 1 more
This paper presents a new, parallel implementation of clustering and demonstrates its utility in greatly speeding up the process of identifying homologous proteins. Clustering is a technique to reduce the number of comparison needed to find similar pairs in a set of n elements such as protein sequences. Precise…
Yann Garniron, Thomas Applencourt, Kevin Gasperich, Anouar Benali + 15 more
Quantum Package is an open-source programming environment for quantum chemistry specially designed for wave function methods. Its main goal is the development of determinant-driven selected configuration interaction (sCI) methods and multi-reference second-order perturbation theory (PT2). The determinant-driven…
Hamed Khakzad, Yasaman Karami, Seyed Shahriar Arab
Protein tertiary structure prediction (PSP) is one of the most challenging problems in bioinformatics. Different methods have been introduced to solve this problem so far, but PSP is computationally intensive and belongs to the NP-hard class. One of the best solutions to accelerate PSP is the use of a massively…
Yann Garniron, Thomas Applencourt, Kevin Gasperich, Anouar Benali + 15 more
Quantum Package is an open-source programming environment for quantum chemistry specially designed for wave function methods. Its main goal is the development of determinant-driven selected configuration interaction (sCI) methods and multi-reference second-order perturbation theory (PT2). The determinant-driven…
Max Doblas, Oscar Lostes-Cazorla, Quim Aguado-Puig, Cristian Iñiguez + 2 more
Pairwise sequence alignment is a core component of multiple sequencing-data analysis tools. Recent advancements in sequencing technologies have enabled the generation of longer sequences at a much lower price. Thus, long-read sequencing technologies have become increasingly popular in sequencing-based studies. However…
Authors not listed
With the ever-increasing demand for atomistic structures representative of real-life systems as well as the ad-vent of exascale computers, it has now become necessary and possible to use advanced global optimization (GO) techniques to intelligently sample the potential energy surface (PES). Given the previous studies…
Dan Guevarra, Kevin Kan, Yungchieh Lai, Ryan Jones + 5 more
Advancements in artificial intelligence (AI) for science are continually expanding the value proposition for automation in materials and chemistry experiments. The advent of hierarchical decision-making also motivates automation of not only the individual measurements but also the coordination among multiple research…
Authors not listed
This paper presents GLAS (Git-based Lab Automated Scheduler or Get Lab Automation Simplified), an open-source, robust, and highly expandable Git-based architecture designed for laboratory automation. GLAS can be deployed in both partially and fully automated experimental science laboratories, enabling the development…
Jonas Verhellen
Computer-assisted design of small molecules has experienced a resurgence in academic and indus- trial interest due to the widespread use of data-driven techniques such as deep generative models. While the ability to generate molecules that fulfill required chemical properties is encouraging, the use of deep learning…