12 papers · ranked by Valyu relevance
Temitayo Adefemi
—Parallelization has become a cornerstone of modern computing, influencing everything from high-performance supercomputers to everyday mobile devices. This paper presents a comprehensive guide on the fundamentals of parallelization that every computer scientist should know, beginning with a historical perspective that…
Patrizio Dazzi
Embarrassingly parallel problems can be split in parts that are characterized by a really low (or sometime absent) exchange of information during their computation in parallel. As a consequence they can be effectively computed in parallel exploiting commodity hardware, hence without particularly sophisticated…
Brijender Kahanwal
Nowadays, we are to find out solutions to huge computing problems very rapidly. It brings the idea of parallel computing in which several machines or processors work cooperatively for computational tasks. In the past decades, there are a lot of variations in perceiving the importance of parallelism in computing…
Patrick Mukala
| Article Info | ABSTRACT | | --- | --- | | | A myriad of applications ranging from engineering and scientific | | | simulations, image and signal processing as well as high-sensitive data | | | retrieval demand high processing power reaching up to teraflops for their | | | efficient execution. While a standard serial…
Rajendra Purohit, K. R. Chowdhary, Sunıl Dutt Purohıt
—Arrival of multicore systems has enforced a new scenario in computing, the parallel and distributed algorithms are fast replacing the older sequential algorithms, with many challenges of these techniques. The distributed algorithms provide distributed processing using distributed file systems and processing units…
Dhananjay Saikumar
Direct gravitational simulations of n-body systems have a time complexity O(n 2 ), which gets computationally expensive as the number of bodies increases. Distributing this workload to multiple cores significantly speeds up the computation and is the fundamental principle behind parallel computing. This project…
Pierre Carrier, Bill Long, Richard Walsh, Jef Dawson + 4 more
High Performance Computing (HPC) Best Practice offers opportunities to implement lessons learned in areas such as computational chemistry and physics in genomics workflows, specifically Next-Generation Sequencing (NGS) workflows. In this study we will briefly describe how distributed-memory parallelism can be an…
Pau Andrio, Adam Hospital, Cristian Ramon-Cortes, Javier Conejero + 4 more
The usage of workflows has led to progress in many fields of science, where the need to process large amounts of data is coupled with difficulty in accessing and efficiently using High Performance Computing platforms. On the one hand, scientists are focused on their problem and concerned with how to process their data.…
Sikao Guo, Nenad Korolija, Kent Milfeld, Adip Jhaveri + 3 more
Particle-based reaction-diffusion models offer a high-resolution alternative to the continuum reaction-diffusion approach, capturing the discrete and volume-excluding nature of molecules undergoing stochastic dynamics. These methods are thus uniquely capable of simulating explicit self-assembly of particles into…
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…
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…
Alessandro Petrini, Marco Mesiti, Max Schubach, Marco Frasca + 7 more
Several prediction problems in Computational Biology and Genomic Medicine are characterized by both big data as well as a high imbalance between examples to be learned, whereby positive examples can represent a tiny minority with respect to negative examples. For instance, deleterious or pathogenic variants are…