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Jan 31, 2021 · We present a new practical technique to help users make informed and cost-efficient GPU selections: make performance predictions with the help of a GPU that ...
This work presents a new practical technique to help users make informed and cost-efficient GPU selections: make performance predictions using the help of a ...
We believe that using an exist- ing GPU to make operation execution time predictions for a different GPU is reasonable because deep learning users often already ...
Many GPUs available for deep neural network (DNN) training. • Each has a different cost and performance. • Which should a user choose for training? The ...
Missing: Approach. | Show results with:Approach.
In this paper, we propose DNNPerf, a novel ML-based tool for predicting the runtime performance of deep learning models using Graph Neural Network. DNNPerf.
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This work presents a new practical technique to help users make informed and cost-efficient GPU selections: make performance predictions with the help of a ...
In this paper, we propose DNNPerf, a novel ML-based tool for predicting the runtime performance of deep learning models using Graph Neural Network. DNNPerf ...
... Many analytical-based performance models have been proposed to predict the performance of DNNs. We can classify these approaches as regression-based [40], ...
Feb 23, 2021 · In this paper, we propose DNNPerf, a novel and general machine learning approach to predict the runtime performance of DL models using Graph Neural Network.
2019, Conference on Machine Learning and Systems. Computational Performance Predictions for Deep Neural Network Training: A Runtime-Based Approach. Geoffrey X ...