Suchergebnisse - Computing methodologies → Distributed computing methodologies

MAD-Max Beyond Single-Node: Enabling Large Machine Learning Model Acceleration on Distributed Systems von 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 …”
Volltext

MMDFL: Multi-Model-based Decentralized Federated Learning for Resource-Constrained AIoT Systems von 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 …”
Volltext

Skywalker: Efficient Alias-Method-Based Graph Sampling and Random Walk on GPUs von 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 computing-intensive algorithms on large-scale graphs …”
Volltext

PreSto: An In-Storage Data Preprocessing System for Training Recommendation Models von 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 …”
Volltext

Centralized Training and Decentralized Control through the Actor-Critic Paradigm for Highly Optimized Multicores von 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 …”
Volltext

DeepScaler: Holistic Autoscaling for Microservices Based on Spatiotemporal GNN with Adaptive Graph Learning von Meng, Chunyang, Song, Shijie, Tong, Haogang, Pan, Maolin, Yu, Yang

“… Autoscaling functions provide the foundation for achieving elasticity in the modern cloud computing paradigm …”
Volltext

NDFT: Accelerating Density Functional Theory Calculations via Hardware/Software Co-Design on Near-Data Computing System von 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 …”
Volltext

Derm: SLA-aware Resource Management for Highly Dynamic Microservices von 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 …”
Volltext

Gluon-Async: A Bulk-Asynchronous System for Distributed and Heterogeneous Graph Analytics von 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 …”
Volltext

HADFL: Heterogeneity-aware Decentralized Federated Learning Framework von Cao, Jing, Lian, Zirui, Liu, Weihong, Zhu, Zongwei, Ji, Cheng

“… Federated learning (FL) supports training models on geographically distributed devices …”
Volltext

AdaGL: Adaptive Learning for Agile Distributed Training of Gigantic GNNs von 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 …”
Volltext

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

“… We study distributed equi-join computation in the presence of join-attribute skew, which causes load imbalance …”
Weitere Angaben

DS-GL: Advancing Graph Learning via Harnessing Nature's Power within Scalable Dynamical Systems von 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 …”
Volltext

Invited: Waving the Double-Edged Sword: Building Resilient CAVs with Edge and Cloud Computing von 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 …”
Volltext

MyML: User-Driven Machine Learning von 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 …”
Volltext

Personalized Heterogeneity-aware Federated Search Towards Better Accuracy and Energy Efficiency von 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 …”
Volltext

Optimizing Distributed ML Communication with Fused Computation-Collective Operations von Punniyamurthy, Kishore, Hamidouche, Khaled, Beckmann, Bradford M.

“… Machine learning models are distributed across multiple nodes using numerous parallelism strategies …”
Volltext

FedAT: A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tiers von 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 …”
Volltext

Revamping Sampling-Based PGO with Context-Sensitivity and Pseudo-instrumentation von He, Wenlei, Yu, Hongtao, Wang, Lei, Oh, Taewook

“… The ever increasing scale of modern data center demands more effective optimizations, as even a small percentage of performance improvement can result in a …”
Volltext

COCA: Generative Root Cause Analysis for Distributed Systems with Code Knowledge von Li, Yichen, Wu, Yulun, Liu, Jinyang, Jiang, Zhihan, Chen, Zhuangbin, Yu, Guangba, Lyu, Michael R.

“… Runtime failures are commonplace in modern distributed systems. When such issues arise, users often turn to platforms such as Github or JIRA to report them and request assistance …”
Volltext