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🖥️ Parallel and Distributed Hyperparameter Optimization

Watch a fleet of parallel worker machines search a 3D loss landscape for the best hyperparameters, coordinated by a central scheduler under synchronous or asynchronous distributed search.

AI & Machine Learning3DAdvanced60 FPS

A fleet of worker machines searches a 3D loss landscape in parallel, reporting results to a central scheduler that coordinates the next round of trials — exactly how real distributed tuning jobs scale hyperparameter search across a cluster.

🔬 What It Demonstrates

The colored terrain encodes validation loss across two hyperparameters; workers probe it in parallel while the scheduler tracks the global best. Switching sync mode reveals how a synchronous barrier lets one slow worker stall the entire fleet, while asynchronous updates keep everyone moving.

🎮 How to Use

Set the worker count, pick a search strategy (random, Bayesian-guided, or ASHA early-stopping), choose synchronous or asynchronous coordination, and adjust simulated network latency. Watch trial markers accumulate and the golden best-point update live.

💡 Did You Know?

Successive Halving and Hyperband-style algorithms like ASHA can cut total compute by an order of magnitude by killing clearly underperforming trials early, freeing workers to explore more promising configurations instead.

⚙ Under the hood

Watch a fleet of parallel worker machines search a 3D loss landscape for the best hyperparameters, coordinated by a central scheduler under synchronous or asynchronous distributed search.

machine learninghyperparameter optimizationdistributed computingparallel processingoptimization algorithmsloss landscapeThree.js

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install