Using Linux PSI

• Investigate how Linux PSI metrics can be collected and associated with individual jobs within the IBM LSF scheduling environment.
• Analyze CPU, memory, and I/O pressure data from EDA workloads.
• Identify workload patterns and resource requirements using PSI measurements.
• Explore the relationship between I/O pressure and NFS storage performance.
• Develop and validate a basic model for predicting job I/O requirements.
• Provide recommendations on how PSI-based insights can improve scheduling decisions across local and remote datacenter resources.
• Document findings and present recommendations to the HPC engineering team.

• Currently pursuing a Bachelor’s or Master’s degree in Computer Science, Software Engineering, Electrical Engineering, Data Science, or a related field.
• Basic knowledge of Linux operating systems and command-line tools.
• Interest in High-Performance Computing (HPC), distributed systems, and infrastructure engineering.
• Experience with scripting or programming languages such as Python, Bash, or similar.
• Strong analytical and problem-solving skills with an interest in data analysis.
• Familiarity with Linux performance monitoring tools is a plus.
• Self-driven, curious, and comfortable working on research-oriented assignments.
• Good communication skills and ability to work independently and within a team.

What You’ll Learn

• High-Performance Computing environments and job scheduling concepts.
• Linux performance monitoring and Pressure Stall Information (PSI).
• Data analysis and predictive modeling techniques.
• Datacenter infrastructure, storage systems, and NFS performance optimization.
• How data-driven insights can be used to improve large-scale engineering workflows.