Search · four archives
Search · four archives
9 papers · ranked by Valyu relevance
Yuhan Wang, Youlong Wu
Coded distributed computing (CDC) can reduce the communication load for distributed computing systems by introducing redundant computation and creating multicasting opportunities. The optimal computation-communication tradeoff has been well studied for homogeneous systems, and some results have been obtained under…
Federico Brunero, Petros Elia
Coded distributed computing (CDC) is a new technique proposed with the purpose of decreasing the intense data exchange required for parallelizing distributed computing systems. Under the famous MapReduce paradigm, this coded approach has been shown to decrease this communication overhead by a factor that is linearly…
Yingjie Cheng, Gaojun Luo, Xiwang Cao, Martianus Frederic Ezerman + 1 more
'San Ling'] A coded distributed computing (CDC) system aims to reduce the communication load in the MapReduce framework. Such a system has K nodes, N input files, and Q Reduce functions. Each input file is mapped by r nodes and each Reduce function is computed by s nodes. The objective is to achieve the maximum…
Mingming Zhang, Youlong Wu, Minquan Cheng, Dianhua Wu
Coded distributed computing, proposed by Li et al., offers significant potential for reducing the communication load in MapReduce computing systems. In the setting of the cascaded coded distributed computing that consisting of K nodes, N input files, and Q output functions, the objective is to compute each output…
Minquan Cheng, Yongkang Wang, Lingyu Zhang, Youlong Wu
Distributed multi-task learning (DMTL) effectively improves model generalization performance through the collaborative training of multiple related models. However, in large-scale learning scenarios, communication bottlenecks severely limit practical system performance. In this paper, we investigate the communication…
Yingjie Cheng, Gaojun Luo, Xiwang Cao, Martianus Frederic Ezerman + 1 more
'San Ling'] Coded distributed computing (CDC) was introduced to greatly reduce the communication load for MapReduce computing systems. Such a system has K nodes, N input files, and Q Reduce functions. Each input file is mapped by r nodes and each Reduce function is computed by s nodes. The architecture must allow for…
Youlong Wu, Zhen-hao Huang, Kai Yuan, Shuai Ma + 1 more
—Distributed computing frameworks such as MapReduce and Spark are often used to process large-scale data computing jobs. In wireless scenarios, exchanging data among distributed nodes would seriously suffer from the communication bottleneck due to limited communication resources such as bandwidth and power. To address…
Derya Malak
Our work addresses the well-known open problem of distributed computing of bilinear functions of two correlated sources A and B. In a setting with two nodes, with the first node having access to A and the second to B, we establish bounds on the optimal sum-rate that allows a receiver to compute an important class of…
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…