A cost-benefit analysis of GPU-based EC2 instances for a deep learning algorithm

  • Eva Malta UNICAMP
  • Charles Rodamilans UNICAMP/Mackenzie
  • Sandra Avila UNICAMP
  • Edson Borin UNICAMP


This paper analyzes the cost-benefit of using EC2 instances, specifically the p2 and p3 virtual machine types, which have GPU accelerators, to execute a machine learning algorithm. This analysis includes the runtime of a convolutional neural network executions, and it takes into consideration the necessary time to stabilize the accuracy value with different batch sizes. Also, we measure the cost of using each machine type, and we define a relation be- tween this cost and the execution time for each virtual machine. The results show that, although the price per hour of the p3 instance is three times bigger, it is faster and costs almost the same as the p2 instance type to train the deep learning algorithm.
Palavras-chave: Arquiteturas Dedicadas e Específicas (GPUs, FPGAs e outras), Computação de Alto Desempenho, Computação em Nuvem, Grade, Aglomerado (Cluster) e Peer-to-Peer


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MALTA, Eva; RODAMILANS, Charles; AVILA, Sandra; BORIN, Edson. A cost-benefit analysis of GPU-based EC2 instances for a deep learning algorithm . In: ESCOLA REGIONAL DE ALTO DESEMPENHO DE SÃO PAULO (ERAD-SP), 10. , 2019, Campinas. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2019 . p. 21-24. DOI: https://doi.org/10.5753/eradsp.2019.13588.

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