27 papers · ranked by Valyu relevance
Aneesh Khole, Atharva Thakar, Avadhoot Kulkarni, Hrithik Jadhav + 2 more
'Shreyas Shende' 'Varad Karajkhede'] Abstract— Computer systems have evolved over the years starting from sizable, single-user, slow, and expensive machines to multi-user, fast, cheaper, and small-sized machines. The use of multi-user computer networks has given rise to a new paradigm of computing known as Distributed…
Wei Geng, Yulong Zhang, Dirk Kutscher, Abhishek Kumar + 2 more
'Pan Hui'] Information-Centric Networking (ICN), with its data-oriented operation and generally more powerful forwarding layer, provides an attractive platform for distributed computing. This paper provides a systematic overview and categorization of different distributed computing approaches in ICN encompassing…
Tsolak Ghukasyan, Vahagn Altunyan, Aram Bughdaryan, Tigran Aghajanyan + 3 more
This paper presents the Smart Distributed Data Factory (SDDF), an AI-driven distributed computing platform designed to address challenges in drug discovery by creating comprehensive datasets of molecular conformations and their properties. SDDF uses volunteer computing, leveraging the processing power of personal…
Mei Chen
One of the most effective approaches to improve resource usage efficiency and degree of resource collecting is to integrate resources. Many studies on the integration of information resources are also available. The search engines are the most well-known. At the same time, this article intends to optimize the…
Jin-Woo Kwon, Seong-Jin Yun, Won-Tae Kim, Chase Wu + 2 more
'Kihyeon Kwon'] As natural disasters become extensive, due to various environmental problems, such as the global warming, it is difficult for the disaster management systems to rapidly provide disaster prediction services, due to complex natural phenomena. Digital twins can effectively provide the services using…
Ian Foster, Carl Kesselman
In this introductory chapter, we lay the groundwork for the rest of the book by providing a more detailed picture of the expected purpose, shape, and architecture of future grid systems. We structure the chapter in terms of six questions that we believe are central to this discussion: Why do we need computational…
Jose L Figueroa, Richard Allen White
We now exist in the era of massive datasets from genomics, large language models, and all the known knowledge of humanity right at our fingertips. Much of this data is becoming more accessible; however, processing such data remains an ongoing issue across systems including high performance computing (HPC)…
Haotian Li
Machine learning and deep learning are novel and trending approaches to solving real-world scientific problems. Graph machine learning is dedicated to performing learning methods, such as graph neural networks, on non-Euclidean data such as graphs. Molecules, with their natural graph structures, could be analyzed by…
Xiangchen Zhao, Diyi Hu, Bhaskar Krishnamachari
—With growing deployment of Internet of Things (IoT) and machine learning (ML) applications that need to leverage computation on networked edge and cloud resources, it is important to develop algorithms and tools to place these distributed computations to optimize their performance. We address the problem of optimally…
Tamer Z. Emara, Thanh Trinh, Joshua Zhexue Huang
Nowadays, several companies prefer storing their data on multiple data centers with replication for many reasons. The data that spans various data centers ensures the fastest possible response time for customers and workforces who are geographically separated. It also provides protecting the information from the loss…
Apostolos I. Rikos, Andreas Grammenos, Evangelia Kalyvianaki, Christoforos N. Hadjicostis + 2 more
'Christoforos N. Hadjicostis' 'Themistoklis Charalambous' 'Karl Henrik Johansson'] Abstract—In this paper we analyze the problem of optimal task scheduling for data centers. Given the available resources and tasks, we propose a fast distributed iterative algorithm which operates over a large scale network of nodes and…
Cláudia Brito, Pedro Ferreira, João Paulo
Breakthroughs in sequencing technologies led to an exponential growth of genomic data, providing unprecedented biological in-sights and new therapeutic applications. However, analyzing such large amounts of sensitive data raises key concerns regarding data privacy, specifically when the information is outsourced to…
Xiuzhen Cheng, Minghui Xu, Runyu Pan, Dongxiao Yu + 3 more
'Xiao Xue' 'Weifeng Lyu'] Abstract—With the continuous improvement of information infrastructures, academia and industry have been constantly exploring new computing paradigms to fully exploit computing powers. In this paper, we propose Meta Computing, a new computing paradigm that aims to utilize all available…
David Carrascal, Elisa Rojas, Juan A. Carral, Isaias Martinez-Yelmo + 1 more
The current society is becoming increasingly interconnected and hyper-connected. Communication networks are advancing, as well as logistics networks, or even networks for the transportation and distribution of natural resources. One of the key benefits of the evolution of these networks is to bring consumers closer to…
Ahmed Eleliemy, Florina M. Ciorba
DAPHNE is a new open-source software infrastructure designed to address the increasing demands of integrated data analysis (IDA) pipelines, comprising data management (DM), high performance computing (HPC), and machine learning (ML) systems. Efficiently executing IDA pipelines is challenging due to their diverse…
Maxim Lippeveld, Daniel Peralta, Andrew Filby, Yvan Saeys
Due to high resolution and throughput of modern image cytometry platforms, morphologically profiling generated datasets poses a significant computational challenge. Here, we present Scalable Cytometry Image Processing (SCIP), an image processing software aimed at running on distributed high performance computing…
Alfredo Barron, Dante D. Sanchez-Gallegos, Diana Carrizales-Espinoza, J. L. Gonzalez-Compean + 2 more
'J. L. Gonzalez-Compean' 'Miguel Morales-Sandoval' 'Shih-Chia Huang'] Cloud storage has become a keystone for organizations to manage large volumes of data produced by sensors at the edge as well as information produced by deep and machine learning applications. Nevertheless, the latency produced by geographic…
Xiaoli Wang, Bharadwaj Veeravalli, Xiaobo Song, Kaiqi Zhang + 1 more
'Stefania Perri'] Most of the available divisible-load scheduling models assume that all servers in networked systems are idle before workloads arrive and that they can remain available online during workload computation. In fact, this assumption is not always valid. Different servers on networked systems may have…
Raphaël Robidas, Claude Legault
Computational chemistry is an increasingly active field due to the improvement of computing resources and theoretical tools. However, its use remains usually limited to technically-inclined users due to the technical challenges of preparing, launching and analyzing calculations. In this context, we have developed…
Authors not listed
Machine learning models are transforming data-driven research across scientific disciplines, yet their deployment as accessible and reliable web services remains a significant challenge. We introduce the NERDD framework, a scalable, maintainable, and secure microservices platform designed to support the sustainable…
Swier Garst, Julian Dekker, Marcel Reinders
Federated learning is an upcoming machine learning paradigm which allows data from multiple sources to be used for training of classifiers without the data leaving the source it originally resides. This can be highly valuable for use cases such as medical research, where gathering data at a central location can be…
Malcolm Sim, Mohammad Ghazi Vakili, Felix Strieth-Kalthoff, Han Hao + 4 more
Self-driving laboratories (SDLs), which combine automated experimental hardware with computational experiment planning, have emerged as powerful tools for accelerating materials discovery. The intrinsic complexity created by their multitude of components requires an effective orchestration platform to ensure the…
Ahmed Hadi Ali AL-Jumaili, Ravie Chandren Muniyandi, Mohammad Kamrul Hasan, Johnny Koh Siaw Paw + 2 more
Traditional parallel computing for power management systems has prime challenges such as execution time, computational complexity, and efficiency like process time and delays in power system condition monitoring, particularly consumer power consumption, weather data, and power generation for detecting and predicting…
Altaf Hussain, Muhammad Aleem, Atiq Ur Rehman, Umer Arshad + 1 more
'Michele Pasqua'] Cloud computing provides an opportunity to gain access to the large-scale and high-speed resources without establishing your own computing infrastructure for executing the high-performance computing (HPC) applications. Cloud has the computing resources (i.e., computation power, storage, operating…
Oliver Lee, Malte Gather, Eli Zysman-Colman
We describe a new tool for the efficient management of computational chemistry. Digichem is a program that automates and simplifies nearly the entire computational pipeline, including large-scale batch submission of calculations, analysis and results parsing, the generation of 3D density plots and 2D graphs of…
Authors not listed
Computing electrostatic interactions remains the bottleneck of molecular dynamics (MD) simulations despite more than a century of effort in developing methods to accelerate the calculation. Previously we have developed the Spherical Grid and Treecode (SGT) and Gauss-Legendre-Spherical-t (GLST) algorithms for…
Authors not listed
The era of exascale computing presents both exciting opportunities and unique challenges for quantum mechanical simulations. While the transition from petaflops to exascale computing has been marked by a steady increase in computational power, the shift towards heterogeneous architectures, particularly the dominant…