IBM BOA for POWER
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IBM Bayesian Optimization Accelerator (BOA) is a do-it-yourself toolkit to apply state-of-the-art Bayesian inferencing techniques and obtain optimal solutions for complex, real-world design simulations without requiring deep machine learning skills. This talk will describe IBM BOA, its differentiation and ease of use, and how researchers can take advantage of it for optimizing any arbitrary HPC simulation.
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IBM BOA for POWER
- 1. What is Bayesian Optimization? Bayesian optimization is a sequential design strategy for global optimization. Many workflows require you to find a powerful set of parameters solve a problem. The challenge is finding those parameters robustly in as little time as possible. © 2019 IBM Corporation Applied to Computational Chemistry Applied to Engineering Design Applied to Drug Discovery BOA accelerated workflow uses 1/3 of the calculations to achieve 4 orders of magnitude resolution increase BOA performed in 19 hours and ~30 simulations what an expert designer would do in 3 weeks Brute force methods of screening require 20,000 experiments. BOA accelerated method required ~200 IBM BOA gets HPC clients designs here faster than any other method
- 2. Blackbox function optimization § No derivatives f’(x). § No analytic form. § Possibly multiple minima. § Possibly noisy data. § Expensive to calculate. §Grid search - exhaustive §Random search - luck §Simulated annealing – large number of evaluations §Gradient descent - requires derivatives §Genetic algorithm – hard to tune Bayesian Optimization
- 3. Air flow simulation around a car, FPGA synthesizer, reservoir simulation, etc. GPU accelerated Power server (IBM AC922) High performance computing infrastructure (x86/Power Systems/Cloud etc.) Parameter values Result (Chip power consumption, for example) Bayesian optimization 1. Pick a surrogate that represents your prior belief on black box behavior. 2. Define an acquisition function over the surrogate. 3. Repeat: • Select the parameter values by optimizing the acquisition function. • Evaluate the black box for those parameter values. • Update the surrogate through posterior inferencing
- 4. © 2019 International Business Machines Corporation CONFIDENTIAL – INTERNAL USE ONLY Interface Functions Input Data Output Data SOLVE Traditional HPC BOA Interface Function (out) Interface Function (in) NEW: Interface Functions Scheduler Objective Function User Defined Unique to each Client
- 5. IBOA IBM BOA Differentiation • Dimensionality mitigation based on compressive sensing • Smart initialization • Novel acquisition functions • Explainability • Ease of use through software abstractions
- 6. BOA Settings "model":{"gaussian_process": { "kernel_func": "Matern52", "scale_y": True, "scale_x": False, "noise_kernel": True, "use_scikit": False, "optimizer": "LBFGS" }}
- 7. Explore-Exploit Trade-Off • Exploration – Prefers to acquire new knowledge • Exploitation – Prefers to lean heavily on what is already known to drive optimization • Need to strike a balance – too much exploration is inefficient, too much exploitation can result in poor performance. Exploitation PI, 𝜀 = 0 Exploration PI, 𝜀 = 0.2 Probability of Improvement Probability of Improvement does not consider the amount by which an improvement occurs, just that an improvement occurs. Can we tweak this to tell the algorithm what constitutes a significant improvement 𝛾 = 𝑓 𝑥!"#$ − 𝜇 𝑥 + 𝜀 𝜎(𝑥) Denote the improvement as 𝛾 = 𝑓 𝑥!"#$ − 𝜇(𝑥) 𝜎(𝑥) Probability of Improvement (PI) (Kushner): 𝛼%& = Φ(𝛾 𝑥 )
- 8. "sampling_function": { "type": "adaptive_expected_improvement", "epsilon": 0.03, "optimize_acq": False, "outlier": False, "bounds": None, "scale": False }