HPC

we build a dataset for operation direct water usage in the cooling systems and indirect water embedded in electricity generation. Our dataset consists of the hourly water efficiency of major U.S. cities and states from 2019 to 2023. We also offer cooling system models that capture the impact of weather on water efficiency. We present a preliminary analysis of our dataset and discuss three potential applications that can benefit from it.

We developed this tool to automate the deployment of kubernetes cluster in AWS. We also added the support of adding cluster autoscaling mechanism. Moreover, the tool can be used to deploy HPC frameworks such as Flux Operator in the kubernetes. The ultimate goal of the project is to enable autoscaling in the HPC Applications when running into cloud such as AWS and GKE. We’ve used Python, AWS API, AWS SDK

In this paper, we propose MPR (Market-based Power Reduction), a scalable market-based approach where users actively participate in reducing the HPC system’s power consumption to mitigate overloads. In MPR, HPC users bid to supply, in exchange for incentives, the resource reduction required for handling the overloads.