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eBPF-Based Performance Fingerprint of Containerized HPC Applications
DescriptionHighly optimized systems face the challenge of meeting the computational demands of domain-specific simulations and workflow applications. Those range from low- to high-level implementations and differ widely regarding optimization and requirements, and affecting increasingly the centers' energy efficiency. With today’s heterogeneous clusters, containerization allows arbitrary applications to bypass architectural differences, and focus mainly on core-functions instead of deployment issues.
Our framework determines general performance characteristics of containerized HPC-applications with unknown behavior, and satisfies computational, memory, and interconnect requirements independent of the container-technology. It allows developers and administrators to evaluate runtime parameters of blackbox applications, without inspecting the inside of any container based on kernel-level measurements with eBPF. We derived first algorithms for the Container-Fingerprint, a quantified and comparable runtime characteristic, enabling optimized mapping to target systems.
For evaluation we investigate benchmark applications on different architectures typical for supercomputing centers. Measurements indicate that the derived fingerprints are suitable to distinguish the performance of containers and systems allowing an optimized allocation of HPC-containers. By applying the scheme we work towards a twofold improvement: an increase in the efficiency of system usage and energy consumption, and a deployment optimization of containers by enabling streamlined, requirements-oriented allocations optimizing the balance between resource-usage and time-to-solution.
Our framework determines general performance characteristics of containerized HPC-applications with unknown behavior, and satisfies computational, memory, and interconnect requirements independent of the container-technology. It allows developers and administrators to evaluate runtime parameters of blackbox applications, without inspecting the inside of any container based on kernel-level measurements with eBPF. We derived first algorithms for the Container-Fingerprint, a quantified and comparable runtime characteristic, enabling optimized mapping to target systems.
For evaluation we investigate benchmark applications on different architectures typical for supercomputing centers. Measurements indicate that the derived fingerprints are suitable to distinguish the performance of containers and systems allowing an optimized allocation of HPC-containers. By applying the scheme we work towards a twofold improvement: an increase in the efficiency of system usage and energy consumption, and a deployment optimization of containers by enabling streamlined, requirements-oriented allocations optimizing the balance between resource-usage and time-to-solution.
