How to measure and reduce the carbon footprint of neuroimaging research computing using open standards and tools

Brisbane Convention & Exhibition Centre 

The storage and processing of neuroimaging data uses energy, and therefore has a carbon footprint. Data centres currently account for 1-4% of global CO2 emissions, a number that may continue to rise with advances in machine-learning. In this session, we’ll discuss the impact of neuroimaging pipelines on carbon emissions followed by demonstration of tools and strategies for researchers to help reduce it. There exist several open-tools that can help you estimate and track the impact of computing tasks. We will provide a walk through of Green Algorithms (web-based), CarbonTracker (embedded), and GA4HPC (cluster-side) to help researchers adopt them in their own work. We will offer recommendations on less carbon-intensive software tools and practices. Through a fMRIPrep use case, we will show that you can reduce the carbon footprint up to 48%.We will also present tools that can be deployed institution- or HPC-wide to minimize the carbon footprint at scale by adopting “green” compute job schedulers such as Climate Aware Task Scheduler (CATS) that can optimize times and locations to minimize carbon intensity. Sustainable science practices heavily intersect with open-science principles that promote data sharing and reuse. Here we discuss best practices for individual researchers and much needed community contributions to instill sustainability as a core goal of scientific research. We will present the on-going community initiatives such as COBIDAS that facilitate standardized reporting and discovery of neuroimaging research artifacts to avoid duplication of heavy computation. We will also highlight ongoing software efforts (e.g. CATS, fMRIPrepCleanup) where researchers can contribute, maintain, and help improve these tools to promote and enable efficient data storage and green-computing.