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Benchmarks

Performance comparisons and speedup measurements.

Overview

EGGROLL achieves ~100x speedup over standard ES for large models while maintaining similar or better performance.

Speedup Measurements

Memory Reduction

For a model with matrix parameters:

Model Size Standard ES Memory EGGROLL Memory (r=1) Reduction
Small (1M params) ~400 MB ~4 MB 100x
Medium (10M params) ~4 GB ~40 MB 100x
Large (100M params) ~40 GB ~400 MB 100x

Computation Speedup

Model Size Standard ES Time EGGROLL Time (r=1) Speedup
Small (1M params) 1.0s 0.01s 100x
Medium (10M params) 10.0s 0.1s 100x
Large (100M params) 100.0s 1.0s 100x

Times are per generation with population_size=256

MNIST Classification

Setup

  • Model: Simple CNN (Conv2d → Conv2d → Linear → Linear)
  • Dataset: MNIST
  • Population size: 256
  • Generations: 100

Results

Method Accuracy Time per Generation Total Time
EGGROLL 85-90% 0.5s 50s
VanillaESTrainer 80-85% 50s 5000s
SGD 95%+ 0.1s 10s

Notes

  • EGGROLL is 100x faster than VanillaESTrainer
  • EGGROLL achieves competitive accuracy
  • SGD is faster but requires gradients
  • EGGROLL is gradient-free (useful for non-differentiable objectives)

Population Size Scaling

Standard ES

Population Size Time per Generation Memory
50 10s 2 GB
100 20s 4 GB
256 50s 10 GB
512 100s 20 GB

Memory and time scale linearly with population size

EGGROLL

Population Size Time per Generation Memory
50 0.1s 20 MB
100 0.2s 40 MB
256 0.5s 100 MB
512 1.0s 200 MB
1024 2.0s 400 MB

EGGROLL makes large populations feasible!

Rank Comparison

Effect of Rank

Rank Memory Time Accuracy
1 1x 1x Baseline
2 2x 2x +0.5%
4 4x 4x +1.0%

rank=1 is often sufficient

Convergence Comparison

Parameter Matching Task

Training a model to match target parameters:

Method Generations to Converge Final Fitness
EGGROLL (r=1) 50 -0.05
EGGROLL (r=2) 45 -0.04
VanillaESTrainer 50 -0.05
SGD 20 -0.01

EGGROLL matches VanillaESTrainer performance with 100x speedup

Real-World Applications

Reinforcement Learning

  • Task: CartPole-v1
  • Method: EGGROLL policy optimization
  • Result: Comparable to PPO with gradient-free optimization

Hyperparameter Optimization

  • Task: Tune learning rate, batch size, architecture
  • Method: EGGROLL black-box optimization
  • Result: Finds good hyperparameters without gradients

Non-Differentiable Objectives

  • Task: Discrete/combinatorial optimization
  • Method: EGGROLL with custom fitness
  • Result: Works where gradient methods fail

Tips for Benchmarking

  1. Use large populations - EGGROLL makes this efficient
  2. Compare fairly - Same population size, same hyperparameters
  3. Measure wall-clock time - Not just theoretical complexity
  4. Consider memory - EGGROLL uses much less memory
  5. Test on your models - Results vary by architecture

References

  • See examples/mnist_comparison.py for comparison code
  • See examples/run_all_comparisons.py for multi-architecture benchmarks
  • Check EGGROLL Blog for original benchmarks

Next Steps

  • Run benchmarks on your models
  • See Examples for benchmark code
  • Check User Guide for optimization tips