11 papers · ranked by Valyu relevance
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…
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…
Marco D. Visser, Sean M. McMahon, Cory Merow, Philip M. Dixon + 3 more
Parallel computing divides calculations into smaller problems and solves these simultaneously, using multiple computing elements (hereafter “workers”). In the biological sciences, many computationally intensive problems are “embarrassingly parallel” , where almost all calculations can be completed in parallel. Common…
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…
Xiandong Meng, Yanqing Ji
This paper focuses on the latest research and critical reviews on modern computing architectures, software and hardware accelerated algorithms for bioinformatics data analysis with an emphasis on one of the most important sequence analysis applications-hidden Markov models (HMM). We show the detailed performance…
Gonzalo Vera, Ritsert C Jansen, Remo L Suppi
Background R is the preferred tool for statistical analysis of many bioinformaticians due in part to the increasing number of freely available analytical methods. Such methods can be quickly reused and adapted to each particular experiment. However, in experiments where large amounts of data are generated, for example…
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…
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…