Výsledky vyhledávání - "Computing methodologies → Distributed computing methodologies"

MAD-Max Beyond Single-Node: Enabling Large Machine Learning Model Acceleration on Distributed Systems Autor 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…”
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Submodularity of Distributed Join Computation Autor Li, Rundong, Riedewald, Mirek, Deng, Xinyan

“…We study distributed equi-join computation in the presence of join-attribute skew, which causes load imbalance. Skew can be addressed by more fine-grained…”
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AdaGL: Adaptive Learning for Agile Distributed Training of Gigantic GNNs Autor 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…”
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Centralized Training and Decentralized Control through the Actor-Critic Paradigm for Highly Optimized Multicores Autor 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,…”
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HADFL: Heterogeneity-aware Decentralized Federated Learning Framework Autor Cao, Jing, Lian, Zirui, Liu, Weihong, Zhu, Zongwei, Ji, Cheng

“…Federated learning (FL) supports training models on geographically distributed devices. However, traditional FL systems adopt a centralized synchronous…”
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DeepScaler: Holistic Autoscaling for Microservices Based on Spatiotemporal GNN with Adaptive Graph Learning Autor Meng, Chunyang, Song, Shijie, Tong, Haogang, Pan, Maolin, Yu, Yang

“…Autoscaling functions provide the foundation for achieving elasticity in the modern cloud computing paradigm. It enables dynamic provisioning or…”
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MMDFL: Multi-Model-based Decentralized Federated Learning for Resource-Constrained AIoT Systems Autor Yan, Dengke, Yang, Yanxin, Hu, Ming, Fu, Xin, Chen, Mingsong

“…Along with the prosperity of Artificial Intelligence (AI) techniques, more and more Artificial Intelligence of Things (AIoT) applications adopt Federated…”
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Skywalker: Efficient Alias-Method-Based Graph Sampling and Random Walk on GPUs Autor Wang, Pengyu, Li, Chao, Wang, Jing, Wang, Taolei, Zhang, Lu, Leng, Jingwen, Chen, Quan, Guo, Minyi

“…Graph sampling and random walk operations, capturing the structural properties of graphs, are playing an important role today as we cannot directly adopt…”
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Derm: SLA-aware Resource Management for Highly Dynamic Microservices Autor 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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PreSto: An In-Storage Data Preprocessing System for Training Recommendation Models Autor 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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NDFT: Accelerating Density Functional Theory Calculations via Hardware/Software Co-Design on Near-Data Computing System Autor 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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DS-GL: Advancing Graph Learning via Harnessing Nature's Power within Scalable Dynamical Systems Autor 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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Invited: Waving the Double-Edged Sword: Building Resilient CAVs with Edge and Cloud Computing Autor Liu, Xiangguo, Luo, Yunpeng, Goeckner, Anthony, Chakraborty, Trishna, Jiao, Ruochen, Wang, Ningfei, Wang, Yixuan, Sato, Takami, Chen, Qi Alfred, Zhu, Qi

“…The rapid advancement of edge and cloud computing platforms, vehicular ad-hoc networks, and machine learning techniques have brought both opportunities and…”
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Personalized Heterogeneity-aware Federated Search Towards Better Accuracy and Energy Efficiency Autor 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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Distributing and Load Balancing Sparse Fluid Simulations Autor Shah, C., Hyde, D., Qu, H., Levis, P.

“…This paper describes a general algorithm and a system for load balancing sparse fluid simulations. Automatically distributing sparse fluid simulations…”
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Gluon-Async: A Bulk-Asynchronous System for Distributed and Heterogeneous Graph Analytics Autor Dathathri, Roshan, Gill, Gurbinder, Hoang, Loc, Jatala, Vishwesh, Pingali, Keshav, Nandivada, V. Krishna, Dang, Hoang-Vu, Snir, Marc

“…Distributed graph analytics systems for CPUs, like D-Galois and Gemini, and for GPUs, like D-IrGL and Lux, use a bulk-synchronous parallel (BSP) programming…”
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FaaSConf: QoS-aware Hybrid Resources Configuration for Serverless Workflows Autor Wang, Yilun, Chen, Pengfei, Dou, Hui, Zhang, Yiwen, Yu, Guangba, He, Zilong, Huang, Haiyu

“…Serverless computing, also known as Function-as-a-Service (FaaS), is a significant development trend in modern software system architecture. The workflow…”
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Accelerating Distributed Graphical Fluid Simulations with Micro‐partitioning Autor Qu, Hang, Mashayekhi, Omid, Shah, Chinmayee, Levis, Philip

“…Graphical fluid simulations are CPU‐bound. Parallelizing simulations on hundreds of cores in the computing cloud would make them faster, but requires evenly…”
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GraNNDis: Fast Distributed Graph Neural Network Training Framework for Multi-Server Clusters Autor Song, Jaeyong, Jang, Hongsun, Lim, Hunseong, Jung, Jaewon, Kim, Youngsok, Lee, Jinho

“…Graph neural networks (GNNs) are one of the rapidly growing fields within deep learning. While many distributed GNN training frameworks have been proposed to…”
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MyML: User-Driven Machine Learning Autor 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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