Search Results - • Computing methodologies → Distributed algorithms

Submodularity of Distributed Join Computation by Li, Rundong, Riedewald, Mirek, Deng, Xinyan

“…We study distributed equi-join computation in the presence of join-attribute skew, which causes load imbalance…”
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MAD-Max Beyond Single-Node: Enabling Large Machine Learning Model Acceleration on Distributed Systems by Hsia, Samuel, Golden, Alicia, Acun, Bilge, Ardalani, Newsha, DeVito, Zachary, Wei, Gu-Yeon, Brooks, David, Wu, Carole-Jean

“…Training and deploying large-scale machine learning models is time-consuming, requires significant distributed computing infrastructures, and incurs high operational costs…”
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NDFT: Accelerating Density Functional Theory Calculations via Hardware/Software Co-Design on Near-Data Computing System by Jiang, Qingcai, Tu, Buxin, Hao, Xiaoyu, Chen, Junshi, An, Hong

“…Linear-response time-dependent Density Functional Theory (LR-TDDFT) is a widely used method for accurately predicting the excited-state properties of physical…”
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MMDFL: Multi-Model-based Decentralized Federated Learning for Resource-Constrained AIoT Systems by Yan, Dengke, Yang, Yanxin, Hu, Ming, Fu, Xin, Chen, Mingsong

“… However, DFL still faces three major challenges, i.e., limited computing power and network bandwidth of resource-constrained devices, non-Independent and Identically Distributed (non-IID…”
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DS-GL: Advancing Graph Learning via Harnessing Nature's Power within Scalable Dynamical Systems by Song, Ruibing, Wu, Chunshu, Liu, Chuan, Li, Ang, Huang, Michael, Geng, Tony Tong

“…With the rapid digitization of the world, an increasing number of real-world applications are turning to non-Euclidean data, modeled as graphs. Due to their…”
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Centralized Training and Decentralized Control through the Actor-Critic Paradigm for Highly Optimized Multicores by Dietrich, Benedikt, Khdr, Heba, Henkel, Jorg

“…While distributed, neural-network-based resource controllers represent the state of the art for their ability to cope with the ever-expanding decision space, such approaches suffer from several…”
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DeepScaler: Holistic Autoscaling for Microservices Based on Spatiotemporal GNN with Adaptive Graph Learning by Meng, Chunyang, Song, Shijie, Tong, Haogang, Pan, Maolin, Yu, Yang

“…Autoscaling functions provide the foundation for achieving elasticity in the modern cloud computing paradigm…”
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PreSto: An In-Storage Data Preprocessing System for Training Recommendation Models by Lee, Yunjae, Kim, Hyeseong, Rhu, Minsoo

“…Training recommendation systems (RecSys) faces several challenges as it requires the "data preprocessing" stage to preprocess an ample amount of raw data and…”
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HADFL: Heterogeneity-aware Decentralized Federated Learning Framework by Cao, Jing, Lian, Zirui, Liu, Weihong, Zhu, Zongwei, Ji, Cheng

“…Federated learning (FL) supports training models on geographically distributed devices…”
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Derm: SLA-aware Resource Management for Highly Dynamic Microservices by Chen, Liao, Luo, Shutian, Lin, Chenyu, Mo, Zizhao, Xu, Huanle, Ye, Kejiang, Xu, Chengzhong

“…Ensuring efficient resource allocation while providing service level agreement (SLA) guarantees for end-to-end (E2E) latency is crucial for microservice…”
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AdaGL: Adaptive Learning for Agile Distributed Training of Gigantic GNNs by Zhang, Ruisi, Javaheripi, Mojan, Ghodsi, Zahra, Bleiweiss, Amit, Koushanfar, Farinaz

“…Distributed GNN training on contemporary massive and densely connected graphs requires information aggregation from all neighboring nodes, which leads to an explosion of inter-server communications…”
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FedAT: A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tiers by Chai, Zheng, Chen, Yujing, Anwar, Ali, Zhao, Liang, Cheng, Yue, Rangwala, Huzefa

“…Federated learning (FL) involves training a model over massive distributed devices, while keeping the training data localized and private…”
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Invited: Waving the Double-Edged Sword: Building Resilient CAVs with Edge and Cloud Computing by Liu, Xiangguo, Luo, Yunpeng, Goeckner, Anthony, Chakraborty, Trishna, Jiao, Ruochen, Wang, Ningfei, Wang, Yixuan, Sato, Takami, Chen, Qi Alfred, Zhu, Qi

“… for better situation awareness and more intelligent decision making. On the other hand, the more distributed computing process and the wireless nature of V2X…”
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BLOwing Trees to the Ground: Layout Optimization of Decision Trees on Racetrack Memory by Hakert, Christian, Khan, Asif Ali, Chen, Kuan-Hsun, Hameed, Fazal, Castrillon, Jeronimo, Chen, Jian-Jia

“…Modern distributed low power systems tend to integrate machine learning algorithms, which are directly executed on the distributed devices (on the edge…”
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Personalized Heterogeneity-aware Federated Search Towards Better Accuracy and Energy Efficiency by Yang, Zhao, Sun, Qingshuang

“…Federated learning (FL), a new distributed technology, allows us to train the global model on the edge and embedded devices without local data sharing…”
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MyML: User-Driven Machine Learning by Goyal, Vidushi, Bertacco, Valeria, Das, Reetuparna

“…Machine learning (ML) on resource-constrained edge devices is expensive and often requires offloading computation to the cloud, which may compromise the…”
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Network-Offloaded Bandwidth-Optimal Broadcast and Allgather for Distributed AI by Khalilov, Mikhail, Girolamo, Salvatore Di, Chrapek, Marcin, Nudelman, Rami, Bloch, Gil, Hoefler, Torsten

“… To address this problem, we propose a novel bandwidth-optimal Allgather collective algorithm that leverages hardware multicast…”
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SparCML: High-Performance Sparse Communication for Machine Learning by Renggli, Cedric, Ashkboos, Saleh, Aghagolzadeh, Mehdi, Alistarh, Dan, Hoefler, Torsten

“…Applying machine learning techniques to the quickly growing data in science and industry requires highly-scalable algorithms…”
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Optimizing Distributed ML Communication with Fused Computation-Collective Operations by Punniyamurthy, Kishore, Hamidouche, Khaled, Beckmann, Bradford M.

“…Machine learning models are distributed across multiple nodes using numerous parallelism strategies…”
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Clairvoyant Prefetching for Distributed Machine Learning I/O by Dryden, Nikoli, Bohringer, Roman, Ben-Nun, Tal, Hoefler, Torsten

“…I/O is emerging as a major bottleneck for machine learning training, especially in distributed environments…”
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