As its name (Load Sharing Facility) implies, LSF shares resources based on flexible policies. Spectrum LSF was designed to support diverse distributed workloads. Evaluators often have preferences that reflect the types of workloads with which they are most familiar. Because of this heritage, the two solutions excel in different areas.
One of the challenges with comparing Spectrum LSF and Kubernetes is that each solution was designed to solve different problems. Spectrum LSF and Kubernetes – understanding the differences In this article, we’ll look at two advanced workload schedulers and discuss their suitability for modern HPC, Analytic, and AI workloads – IBM Spectrum LSF and Kubernetes. As HPC applications have become more diverse, techniques for scheduling and managing workloads have evolved as well. Today, workloads are just as likely to involve collecting or filtering streaming data, using distributed analytics to discover patterns in data, or training machine learning models. Over the past decade, however, what we consider to be an HPC workload has broadened considerably.
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Financial risk management, computational chemistry, seismic modeling, and simulating car crashes in software are all good examples. Scientists and engineers would model complex systems in software on large-scale parallel clusters to predict real-world outcomes. Traditionally, HPC workloads have been all about simulation. Since 1987 - Covering the Fastest Computers in the World and the People Who Run Them
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