14 papers · ranked by Valyu relevance
Younghwan Jeong, Taeyoon Kim, Francisco J. González-Castaño
Federated learning (FL) is a promising collaborative learning approach in edge computing, reducing communication costs and addressing the data privacy concerns of traditional cloud-based training. Owing to this, diverse studies have been conducted to distribute FL into industry. However, there still remain the…
Shreshth Tuli, Sukhpal Singh Gill, Peter Garraghan, Rajkumar Buyya + 2 more
'Giuliano Casale' 'Nicholas R. Jennings'] Abstract—Modern large-scale computing systems distribute jobs into multiple smaller tasks which execute in parallel to accelerate job completion rates and reduce energy consumption. However, a common performance problem in such systems is dealing with straggler tasks that are…
Benjamin Wong
—The purpose of this study is to test the effectiveness of current straggler mitigation techniques over different important iterative convergent machine learning(ML) algorithm including Matrix Factorization (MF), Multinomial Logistic Regression (MLR), and Latent Dirichlet Allocation (LDA) . The experiment was conducted…
Youshao Xiao, Lin Ju, Zhenglei Zhou, Siyuan Li + 7 more
Straggler Nodes Authors: ['Youshao Xiao' 'Lin Ju' 'Zhenglei Zhou' 'Siyuan Li' 'Zhaoxin Huan' 'Dalong Zhang' 'Rujie Jiang' 'Lin Wang' 'Xiaolu Zhang' 'Lei Liang' 'Zhou Jun'] Abstract—Many distributed training techniques like Parameter Server and AllReduce have been proposed to take advantage of the increasingly large…
Mehmet S. Aktaş, Emina Soljanin
—Runtime performance variability has been a major issue, hindering predictable and scalable performance in modern distributed systems. Executing requests or jobs redundantly over multiple servers have been shown to be effective for mitigating variability, both in theory and practice. Systems that employ redundancy has…
Krishna Giri Narra, Zhifeng Lin, Mehrdad Kiamari, Salman Avestimehr + 1 more
'Murali Annavaram'] While performing distributed computations in today's cloud-based platforms, execution speed variations among compute nodes can significantly reduce the performance and create bottlenecks like stragglers. Coded computation techniques leverage coding theory to inject computational redundancy and…
Emre Ozfatura, Sennur Ulukus, Deniz Gündüz
When gradient descent (GD) is scaled to many parallel workers for large-scale machine learning applications, its per-iteration computation time is limited by straggling workers. Straggling workers can be tolerated by assigning redundant computations and/or coding across data and computations, but in most existing…
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…
Bin Fan, Bin Tang, Zhihao Qu, Baoliu Ye + 1 more
In wireless distributed computing systems, worker nodes connect to a master node wirelessly and perform large-scale computational tasks that are parallelized across them. However, the common phenomenon of straggling (i.e., worker nodes often experience unpredictable slowdown during computation and communication) and…
Yushen Lin, Zhiguo Ding, Shlomo Shamai (Shitz), Giuseppe Caire + 3 more
Straggler synchronization is a dominant wall-clock bottleneck in synchronous wireless federated learning (FL). Under non-IID data, however, aggressively sampling only fast clients may significantly slow convergence due to statistical heterogeneity. This paper studies PASS-enabled FL, where a radiating pinching antenna…
Mireia Villafáfila García, Antonio José Carpio Camargo, Margarita López Rivas
The fishing gear deployed by fishermen in seas and oceans throughout the world not only captures target species but also unintentionally ensnares non-target species, a phenomenon known as “by-catch”. This unintended capture of marine life can represent significant challenges for the fishing industry, with adverse…
Lukas Hübner, Alexey M. Kozlov, Demian Hespe, Peter Sanders + 1 more
Phylogenetic trees are now routinely inferred on large scale HPC systems with thousands of cores as the parallel scalability of phylogenetic inference tools has improved over the past years to cope with the molecular data avalanche. Thus, the parallel fault tolerance of phylogenetic inference tools has become a…
Evelyn M. Beaury, Jeffrey Smith, Jonathan M. Levine
Land-based mitigation strategies (LBMS) are critical to reducing climate change and will require large areas for their implementation. Yet few studies have considered how and where LBMS compete for land or are mutually compatible across Earth’s surface. We derived high resolution estimates of the spatial distribution…
James A. Fuller, Avi Hakim, Kerton R. Victory, Kashmira Date + 3 more
Mitigation policies, including closure of nonessential businesses, restrictions on gatherings and movement, and stay-at-home orders, have been critical to controlling the COVID-19 pandemic in many countries, but they come with high social and economic costs. European countries that implemented more stringent mitigation…