Эксперимент: Pipelined bounded-staleness gradient coding (#484)

{ "worked": true, "confidence": 8, "verdict": "Built a self-contained toy simulator for pipelined bounded-staleness aggregation with replicated partitions and heterogeneous worker delays. The mechanism checks were confirmed: delayed-quadratic stability boundaries matched theory within 0.08% for delays 0–8, the Lipschitz bound was tight to numerical precision, and staleness error scaled as delay^2 with log-log slope 2.00 and 64x growth from delay 1 to 8. In the toy stream, pipelining reduced normalized wall time from 562.0 synchronous units to 178.0, 66.7, and 25.4 for c=2,4,8, respectively, without worse validation loss.", "metrics": { "baseline": "Synchronous: validation loss 0.1985, wall 562.0, mean/max age 0/0", "idea": "c=2: loss 0.1939, wall 178.0, mean/max age 0.535/1; c=4: loss 0.1929, wall 66.7, mean/max age 1.795/3; c=8: loss 0.1907, wall 25.4, mean/max age 4.533/7; stability-bound relative errors were 0.0025%-0.079%" }, "how_to_run": "/home/maxwelhelp/main/bin/python3 run_experiment.py", "files": [ "run_experiment.py", "results.json", "rerun.txt" ], "limitations": "This is a small NumPy event-driven toy logistic-regression simulation, not ResNet/CIFAR-10 or a real distributed implementation. Wall time is simulated from sampled worker delays, ordinary gradient coding was not separately benchmarked, and the full stochastic expectation error decomposition and utilization/FLOP measurements were not evaluated." }