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Found 10 papers, 3 papers with code
Optimal Direct-Connect Topologies for Collective Communications
We provide an algorithmic framework for constructing direct-connect topologies optimized for the latency-bandwidth tradeoff given a collective communication workload.
Cloud Collectives: Towards Cloud-aware Collectives forML Workloads with Rank Reordering
ML workloads are becoming increasingly popular in the cloud.
AutoLRS: Automatic Learning-Rate Schedule by Bayesian Optimization on the Fly
This mutual-training process between BO and the loss-prediction model allows us to limit the training steps invested in the BO search.
Scaling Distributed Machine Learning with In-Network Aggregation
Training machine learning models in parallel is an increasingly important workload.
ADARES: Adaptive Resource Management for Virtual Machines
Our large-scale analysis confirms that VMs are often misconfigured, either overprovisioned or underprovisioned, and that this problem is pervasive across a wide range of private clusters.
A Hardware-Software Blueprint for Flexible Deep Learning Specialization
Specialized Deep Learning (DL) acceleration stacks, designed for a specific set of frameworks, model architectures, operators, and data types, offer the allure of high performance while sacrificing flexibility.
Parameter Hub: a Rack-Scale Parameter Server for Distributed Deep Neural Network Training
Distributed deep neural network (DDNN) training constitutes an increasingly important workload that frequently runs in the cloud.
Learning to Optimize Tensor Programs
Efficient implementations of tensor operators, such as matrix multiplication and high dimensional convolution, are key enablers of effective deep learning systems.
TVM: An Automated End-to-End Optimizing Compiler for Deep Learning
Experimental results show that TVM delivers performance across hardware back-ends that are competitive with state-of-the-art, hand-tuned libraries for low-power CPU, mobile GPU, and server-class GPUs.
Fast Video Classification via Adaptive Cascading of Deep Models
Recent advances have enabled "oracle" classifiers that can classify across many classes and input distributions with high accuracy without retraining.
