Presentation
hws: A Tool for Monitoring Hardware Metrics Across Diverse Vendors: A Case Study on Hyperparameter Optimization Algorithms
SessionSustainable Supercomputing
DescriptionDue to modern hardware's constantly growing energy demands, it is important to consider energy efficiency and power consumption. Especially in the age of AI, where a massive amount of computational power is necessary, energy consumption and the costs involved can become a significant problem. However, gathering this power information in a vendor-independent and portable way is far from trivial.
Therefore, we propose hws a hardware sampling library for Python and C++, which makes it extremely easy to gather hardware information like the current power draw or total power consumption, as well as other metrics like clock frequencies, memory consumption, or utilizations, for CPUs and GPUs from NVIDIA, AMD, and Intel. In a case study, we use our library to analyze three common hyperparameter optimization algorithms for two Neural Network architectures and one GPU-accelerated SVM implementation.
Therefore, we propose hws a hardware sampling library for Python and C++, which makes it extremely easy to gather hardware information like the current power draw or total power consumption, as well as other metrics like clock frequencies, memory consumption, or utilizations, for CPUs and GPUs from NVIDIA, AMD, and Intel. In a case study, we use our library to analyze three common hyperparameter optimization algorithms for two Neural Network architectures and one GPU-accelerated SVM implementation.
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