Miro Hodak

Publications5
h-index2
Citations8
Highly Influential Citations0
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Towards Evaluation of Tensorflow Performance in a Distributed Compute Environment
TLDR
GPU-equipped general-purpose compute clusters can provide comparable training performance to specialized machines designed for AI workloads. Expand
  • 3
Towards Power Efficiency in Deep Learning on Data Center Hardware
TLDR
We directly measure power used by the whole system as well as that used by GPU, CPU, and RAM during DL training to determine their contributions to the overall energy consumption. Expand
  • 3
Performance Implications of Big Data in Scalable Deep Learning: On the Importance of Bandwidth and Caching
TLDR
In this paper, we explore the relationship between Big Data storage, networking, and Deep Learning workloads to understand key factors for designing Big Data/Deep Learning integrated solutions. Expand
  • 1
Challenges in Distributed MLPerf
  • 1
Large-scale quantum transport calculations for electronic devices with over ten thousand atoms