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Search · four archives
17 papers · ranked by Valyu relevance
Mohsen Amini Salehi, Adel N. Tousi, Hai Duc Nguyen, Murtaza Rangwala + 4 more
Advances in networking and computing technologies throughout the early decades of the 21st century have transformed long-standing dreams of pervasive communication and computation into reality. These technologies now form a rapidly evolving and increasingly complex global infrastructure that will underpin the next…
Peter G. Hawkins, Eli M. Swanson, Megan Feichtel
The size of individual single cell samples continues to grow with advancing technologies, as do the number of samples included in individual experiments and across organizations. This presents challenges for processing this data at scale, both in terms of computational throughput and the required size of the machines…
Xiao Fu, Yuanyuan Xu, Claudionor Ribeiro da Silva
The recent surge in digital agriculture has generated an emerging demand for scalable, resource-efficient solutions capable of handling both close-range images of agricultural products and high-scale remote-sensing images. Deep learning models have high accuracy, but they are expensive and lack the dynamism to be…
Sana Taghipour Anvari, Julian Samaroo, Matin Raayai Ardakani, David Kaeli
—The Fast Fourier Transform (FFT) is a fundamental numerical technique with widespread application in a range of scientific problems. As scientific simulations attempt to exploit exascale systems, there has been a growing demand for distributed FFT algorithms that can effectively utilize modern heterogeneous…
Nandy, Barenya Kumar, Rupesh Nasre
—Relational data, occurring in the real world, are often structured as graphs, which provide the logical abstraction required to make analytical derivations simpler. As graphs get larger, the irregular access patterns exhibited in most graph algorithms, hamper performance. This, along with NUMA and physical memory…
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)…
Leszek Śliwko, Vladimir Getov
This paper presents a novel approach to categorization of modern workload schedulers. We provide descriptions of three classes of schedulers: Operating Systems Process Schedulers, Cluster Systems Jobs Schedulers and Big Data Schedulers. We describe their evolution from early adoptions to modern implementations…
Xaver Stiensmeier, Alexander Kanitz, Jan Krüger, Santiago Insua + 11 more
- We identify the challenges of navigating a fragmented ecosystem of computing environments between research infrastructures, NRENs, and commercial clouds, and position hybrid cloud architectures as a solution to balance performance, cost, scalability, and accessibility. - We describe deployment models and workflow…
Dinesh Sahu, Nidhi, Shiv Prakash, Tiansheng Yang + 4 more
The increasing adoption of Digital Twins (DTs) in distributed edge computing systems necessitates robust fault tolerance mechanisms to ensure high availability and reliability. This paper presents an adaptive fault tolerance framework designed to maintain the continuous operation of DTs in dynamic and…
Stavrinides, Georgios L., Karatza, Helen D.
With the explosive growth of big data, workloads tend to get more complex and computationally demanding. Such applications are processed on distributed interconnected resources that are becoming larger in scale and computational capacity. Data-intensive applications may have different degrees of parallelism and must…
Kevin Garner, Polykarpos Thomadakis, Nikos Chrisochoides
This paper presents a distributed memory method for anisotropic mesh adaptation that is designed to avoid the use of collective communication and global synchronization techniques. In the presented method, meshing functionality is separated from performance aspects by utilizing a separate entity for each - a multicore…
Hao Yu, Jing Fan, Hua Dong, Yadong Jin + 3 more
Highlights What are the main findings?1. The FLEX-SFL framework introduces dynamic, device-aware adaptive model segmentation, entropy-driven client selection, and hierarchical local asynchronous aggregation mechanisms, improving training efficiency and scalability in edge heterogeneous environments. 2. Extensive…
David L. Haggerty, Caleb B. Darden, David M. Lovinger
Accurate pose estimation underpins quantitative analysis of behavior, yet many deep learning-based tracking tools remain optimized for offline workflows that rely on fragmented software pipelines, workstation-grade GPUs, or external middleware to enable real-time deployment. Here we present an integrated…
Panagiotis K. Gkonis, Anastasios Giannopoulos, Nikolaos Nomikos, Lambros Sarakis + 4 more
The goal of the study presented in this work is to analyze all recent advances in the context of the computing continuum and meta-operating systems (meta-OSs). The term continuum includes a variety of diverse hardware and computing elements, as well as network protocols, ranging from lightweight Internet of Things…
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…
Authors not listed
Machine Learning Interatomic Potentials (MLIPs), trained with Quantum Mechanics data, can model potential energy surfaces for molecular systems with very high accuracy and extreme speedups compared to reference quantum calculations, offering a powerful tool for studying complex chemical and biological systems. This…
Kota Kambara, Sintho Wahyuning Ardie, Daisuke Tsugama
Differential gene expression analysis (DEA) via RNA sequencing (RNA-seq) is essential but remains challenging for wet-lab biologists due to command-line complexities. Centralized web platforms democratize this process but suffer from server congestion, long queuing delays, data privacy risks with proprietary datasets…