aryandeshwal / bops Goto Github PK

This repository contains the source code and the resources related to the paper "Bayesian Optimization over Permutation Spaces" published at AAAI'22 conference.

We provided three real-world benchmarks to drive future research on this important problem. They are described below:

1. Floorplanning:

   • The simulator file is in floorplanning directory.
   • The input is given in a permutation file (named 'permutation.txt') as a comma separated values from 0-10
   • The output is given by running: ./floorplan_simulation b1_floorplan.blk
   • Permutation file will be read by the simulation internally
   • There are two variants: b1_floorplan.blk and b2_floorplan.blk

2. Cell Placement

   • The simulator file is in cell_placement directory.
   • The input is given in a permutation file (named 'permutation.txt') as a comma separated values from 0-10
   • The output is given by running: ./cp_simulator ex10_40_2_3.dat
   • Permutation file will be read by the simulation internally

3. Heterogeneous Manycore Design

   • There is a dataset file named 'hmd_dataset.pkl' containing around 15K points
   • hmd_dataset.pkl contains a dictionary with two keys 'points' (permutations) and 'vals' (objective values)

As discussed in the paper, we propose two algorithms: BOPS-T and BOPS-H.

• numpy, scipy
• PyTorch, GPyTorch, BoTorch
• MATLAB engine API for python (only for BOPS-T)

The floorplanning directory contains two main files: floorplan_kendall.py and floorplan_mallows.py for BOPS-T and BOPS-H respectively.

In floorplan_mallows.py, evaluate_floorplan method defines a call to the black-box objective function and bo_loop is the entry point for the code. In bo_loop, n_init is the number of initial evaluations to initialize the GP surrogate model. The total budget is given by n_evals-n_init. We use the Expected improvement acquisition function which is optimized via local search with multiple restarts. The number of restarts can be changed in line 127.

BOPS-T utilizes an SDP solver (for acquisition function optimization) implemented here. BOPS-H is built on top of GPyTorch and BoTorch libraries. We thank the original authors for their code.