Hardware-Agnostic and Insightful Efficiency Metrics for Accelerated Systems: Definition and Implementation within TALP

A team from the Barcelona Supercomputing Center (BSC) has published a significant paper introducing new, hardware-agnostic efficiency metrics for modern high-performance computing (HPC) and AI systems. The research, led by Ghazal Rahimi, Victor Lopez, and Marta Garcia-Gasulla, addresses a critical gap: traditional performance metrics fail to capture the complex interactions in heterogeneous platforms that combine CPUs with accelerators like GPUs. Their work extends the established Performance Optimization and Productivity (POP) framework by creating a new hierarchy of metrics that separately quantify host (CPU) and device (GPU/accelerator) efficiency. This allows for precise measurement of hybrid execution, offloading operations, and parallel efficiency on the device side.

The team has implemented these metrics in the TALP module of the Dynamic Load Balancing (DLB) library. TALP is a lightweight monitoring tool that provides measurements both after a job completes (post-mortem) and in real-time, with outputs available in both human-readable and machine-parsable formats. The researchers validated their framework using synthetic benchmarks and three production HPC applications, demonstrating its ability to expose specific inefficiencies in offloading, load balancing, and task orchestration that were previously hidden. This provides developers with concrete, actionable insights to guide optimization efforts for complex, accelerated codes, moving beyond simple utilization percentages to understand the true cost and effectiveness of computation split across different hardware types.