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feat(multi-cluster): add dispatch evaluation metrics - #79
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Add average turnaround time, bounded slowdown, actual run time, and ground-truth speedup metrics to the multi-cluster dispatch experiments. Extend Simulation::Statistics with average run time and bounded slowdown, using max(1, turnaround / max(run_time, 10 seconds)). Expose the new statistics through the Python bindings, protobuf API, gRPC responses, and server-generated JSON. Aggregate turnaround and bounded slowdown across systems by completed-job count. Compute average run time and speedup from dispatched workloads, and reject summaries when completed and dispatched job counts disagree. Produce the same overall summary format from the native MPI and Python/gRPC implementations. Update Ser20 MPI statistics messages and add regression coverage for metric calculation, API exposure, serialization, and native summary output. Document the metric definitions and clarify the online dispatch model, native MPI/Ser20 architecture, Python gRPC architecture, and execution-mode terminology.
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Add average turnaround time, bounded slowdown, actual run time, and ground-truth speedup metrics to the multi-cluster dispatch experiments.
Extend Simulation::Statistics with average run time and bounded slowdown, using max(1, turnaround / max(run_time, 10 seconds)). Expose the new statistics through the Python bindings, protobuf API, gRPC responses, and server-generated JSON.
Aggregate turnaround and bounded slowdown across systems by completed-job count. Compute average run time and speedup from dispatched workloads, and reject summaries when completed and dispatched job counts disagree. Produce the same overall summary format from the native MPI and Python/gRPC implementations.
Update Ser20 MPI statistics messages and add regression coverage for metric calculation, API exposure, serialization, and native summary output.
Document the metric definitions and clarify the online dispatch model, native MPI/Ser20 architecture, Python gRPC architecture, and execution-mode terminology.