Search Results - "Computing methodologies → Distributed computing methodologies"

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…”
Get full text

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. Skew can be addressed by more fine-grained…”
Get more information

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…”
Get full text

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,…”
Get full text

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. However, traditional FL systems adopt a centralized synchronous…”
Get full text

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. It enables dynamic provisioning or…”
Get full text

MMDFL: Multi-Model-based Decentralized Federated Learning for Resource-Constrained AIoT Systems by 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…”
Get full text

Skywalker: Efficient Alias-Method-Based Graph Sampling and Random Walk on GPUs by 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…”
Get full text

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…”
Get full text

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…”
Get full text

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…”
Get full text

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…”
Get full text

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

“…The rapid advancement of edge and cloud computing platforms, vehicular ad-hoc networks, and machine learning techniques have brought both opportunities and…”
Get full text

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…”
Get full text

Distributing and Load Balancing Sparse Fluid Simulations by 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…”
Get full text

FaaSConf: QoS-aware Hybrid Resources Configuration for Serverless Workflows by 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…”
Get full text

Gluon-Async: A Bulk-Asynchronous System for Distributed and Heterogeneous Graph Analytics by 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…”
Get full text

Accelerating Distributed Graphical Fluid Simulations with Micro‐partitioning by 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…”
Get full text

GraNNDis: Fast Distributed Graph Neural Network Training Framework for Multi-Server Clusters by 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…”
Get full text

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…”
Get full text