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Deep learning workload scheduling in gpu datacenters: A survey
Deep learning (DL) has demonstrated its remarkable success in a wide variety of fields. The
development of a DL model is a time-consuming and resource-intensive procedure. Hence …
development of a DL model is a time-consuming and resource-intensive procedure. Hence …
{THC}: Accelerating distributed deep learning using tensor homomorphic compression
Deep neural networks (DNNs) are the de facto standard for essential use cases, such as
image classification, computer vision, and natural language processing. As DNNs and …
image classification, computer vision, and natural language processing. As DNNs and …
Resource allocation and workload scheduling for large-scale distributed deep learning: A survey
With rapidly increasing distributed deep learning workloads in large-scale data centers,
efficient distributed deep learning framework strategies for resource allocation and workload …
efficient distributed deep learning framework strategies for resource allocation and workload …
Crux: Gpu-efficient communication scheduling for deep learning training
Deep learning training (DLT), eg, large language model (LLM) training, has become one of
the most important services in multitenant cloud computing. By deeply studying in …
the most important services in multitenant cloud computing. By deeply studying in …
MLTCP: A Distributed Technique to Approximate Centralized Flow Scheduling For Machine Learning
This paper argues that congestion control protocols in machine learning datacenters sit at a
sweet spot between centralized and distributed flow scheduling solutions. We present …
sweet spot between centralized and distributed flow scheduling solutions. We present …
Mltcp: Congestion control for dnn training
We present MLTCP, a technique to augment today's congestion control algorithms to
accelerate DNN training jobs in shared GPU clusters. MLTCP enables the communication …
accelerate DNN training jobs in shared GPU clusters. MLTCP enables the communication …
PSscheduler: A parameter synchronization scheduling algorithm for distributed machine learning in reconfigurable optical networks
With the increasing size of training datasets and models, parameter synchronization stage
puts a heavy burden on the network, and communication has become one of the main …
puts a heavy burden on the network, and communication has become one of the main …
Understanding the Throughput Bounds of Reconfigurable Datacenter Networks
The increasing gap between the growth of datacenter traffic volume and the capacity of
electrical switches led to the emergence of reconfigurable datacenter network designs …
electrical switches led to the emergence of reconfigurable datacenter network designs …
Communication optimization for distributed training: architecture, advances, and opportunities
The past few years have witnessed the flourishing of large-scale deep neural network
models with ever-growing parameter numbers. Training such large-scale models typically …
models with ever-growing parameter numbers. Training such large-scale models typically …
Straggler-Aware Gradient Aggregation for Large-Scale Distributed Deep Learning System
Deep Neural Network (DNN) is a critical component of a wide range of applications.
However, with the rapid growth of the training dataset and model size, communication …
However, with the rapid growth of the training dataset and model size, communication …