Distributed Computing
70 papers with code • 0 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Distributed Computing
Libraries
Use these libraries to find Distributed Computing models and implementations
MNIST-8M
Latest papers
Cooperative Coevolution for Non-Separable Large-Scale Black-Box Optimization: Convergence Analyses and Distributed Accelerations
Given the ubiquity of non-separable optimization problems in real worlds, in this paper we analyze and extend the large-scale version of the well-known cooperative coevolution (CC), a divide-and-conquer black-box optimization framework, on non-separable functions.
fseval: A Benchmarking Framework for Feature Selection and Feature Ranking Algorithms
The package is open source and can be installed through PyPI.
Scalable Differentially Private Clustering via Hierarchically Separated Trees
google-research/google-research
•
•
We study the private $k$-median and $k$-means clustering problem in $d$ dimensional Euclidean space.
SE-MoE: A Scalable and Efficient Mixture-of-Experts Distributed Training and Inference System
With the increasing diversity of ML infrastructures nowadays, distributed training over heterogeneous computing systems is desired to facilitate the production of big models.
Nebula-I: A General Framework for Collaboratively Training Deep Learning Models on Low-Bandwidth Cloud Clusters
We took natural language processing (NLP) as an example to show how Nebula-I works in different training phases that include: a) pre-training a multilingual language model using two remote clusters; and b) fine-tuning a machine translation model using knowledge distilled from pre-trained models, which run through the most popular paradigm of recent deep learning.
BigDL 2.0: Seamless Scaling of AI Pipelines from Laptops to Distributed Cluster
intel-analytics/BigDL
•
•
CVPR 2022
To address this challenge, we have open sourced BigDL 2. 0 at https://github. com/intel-analytics/BigDL/ under Apache 2. 0 license (combining the original BigDL and Analytics Zoo projects); using BigDL 2. 0, users can simply build conventional Python notebooks on their laptops (with possible AutoML support), which can then be transparently accelerated on a single node (with up-to 9. 6x speedup in our experiments), and seamlessly scaled out to a large cluster (across several hundreds servers in real-world use cases).
Improving Response Time of Home IoT Services in Federated Learning
hwangdongjun/federated_learning_using_websockets
•
•
Though federated learning is useful for protecting privacy, it experiences poor performance in terms of the end-to-end response time in home IoT services, because IoT devices are usually controlled by remote servers in the cloud.
Sky Computing: Accelerating Geo-distributed Computing in Federated Learning
In this paper, we proposed Sky Computing, a load-balanced model parallelism framework to adaptively allocate the weights to devices.
LBCF: A Large-Scale Budget-Constrained Causal Forest Algorithm
www2022paper/www-2022-paper-supplementary-materials
•
•
The proposed approach is currently serving over hundreds of millions of users on the platform and achieves one of the most tremendous improvements over these months.
MCDS: AI Augmented Workflow Scheduling in Mobile Edge Cloud Computing Systems
Workflow scheduling is a long-studied problem in parallel and distributed computing (PDC), aiming to efficiently utilize compute resources to meet user's service requirements.
