Search · four archives
Search · four archives
16 papers · ranked by Valyu relevance
Rimpi Borah, J. Harshan, V. Lalitha
—Coded computing has emerged as a key framework for addressing the impact of stragglers in distributed computation. While polynomial functions often admit exact recovery under existing coded computing schemes, non-polynomial functions require approximate reconstruction from a finite number of evaluations, posing…
Haobo Jia, Zhuqing Jia, Shenghui Song
We study the problem of locally encoded secure distributed batch matrix multiplication (LESDBMM), where M pairs of sources each encode their respective batches of massive matrices and distribute the generated shares to a subset of N worker nodes. Each worker node computes a response from the received shares and sends…
Rimpi Borah, J. Harshan
Coded computing is a reliable and fault-tolerant paradigm for executing large-scale computational tasks over distributed worker nodes. Among existing coded computing frameworks, Berrut Approximated Coded Computing (BACC) enables distributed computation of arbitrary non-polynomial functions through rational…
Heekang Song, Wan Choi
—In distributed computing systems, reducing the communication load during the data shuffling phase is a critical challenge, as excessive inter-node transmissions are a major performance bottleneck. One promising approach to alleviate this burden is Embedded Index Coding (EIC), which exploits cached data at user nodes…
Javad Maheri, K. K. Krishnan Namboodiri, Petros Elia
We consider a distributed computing system in which a master node coordinates $N$ workers to evaluate a function over $n$ input files, where this function accepts general decomposition. In particular, we focus on the general case where the requested function admits a $d$-uniform decomposition, meaning that it can be…
Ali Khalesi
This work develops a unified learning- and information-theoretic framework for distributed computation and inference across multiple users and servers. The proposed General Multi-User Distributed Computing (GMUDC) model characterizes how computation, communication, and accuracy can be jointly optimized when users…
Xavier Martínez-Luaña, Alba Gude-Santos, Manuel Fernández-Veiga, Rebeca P. Díaz-Redondo
Distributed machine learning enables collaborative model training without centralizing data, but it also exposes learning processes to privacy leakage and malicious manipulation. Existing defenses typically address these threats in isolation and are often tailored to specific learning paradigms or model architectures…
Jesús Gómez-Vilardebó
—We study the problem of computing matrix chain multiplications in a distributed computing cluster. In such systems, performance is often limited by the straggler problem, where the slowest worker dominates the overall computation latency. To resolve this issue, several coded computing strategies have been proposed…
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)…
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…
Yifei Huang, Siying Luo, Bowen Zheng, Chi Wan Sung
In the traditional $(K,L,M_{T},M_{U},N)$ partially connected linear network, a central server stores a library of N files and connects to $(K+L-1)$ transmitters, each equipped with a cache of size $M_{T}$. Each user is connected to L neighboring transmitters and is equipped with a local cache of size $M_{U}$. Motivated…
Mohamad Hayek, Martin Golasowski, Stephan Hachinger, Rubén J. García-Hernández + 8 more
Modern data-management frameworks promise a flexible and efficient management of data and metadata across storage backends. However, such claims need to be put to a meaningful test in daily practice. We conjecture that such frameworks should be fit to construct a data backend for workflows which use geographically…
Jun Wang, Xianghua Chen, Xing Cheng, Jiantong Zhang + 3 more
In edge computing scenarios, the data generated by distributed devices is characterized by its dispersion, heterogeneity, and privacy sensitivity, posing significant challenges to federated clustering, including high communication overhead, difficulty in adapting to non-IID data, and significant privacy leakage risks.…
Authors not listed
This comprehensive review examines the evolution of autonomous materials synthesis laboratories that integrate artificial intelligence with advanced robotics to accelerate discovery. Traditional materials development pipelines typically require 10-20 years, but self-driving laboratories (SDLs) and Materials…
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…
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…