PARyOpt: A software for Parallel Asynchronous Remote Bayesian Optimization

Pokuri, Balaji Sesha Sarath; Lofquist, Alec; Risko, Chad M; Baskar, Ganapathysubramanian,

doi:10.48550/arxiv.1809.04668

Cited by 2 publications

(2 citation statements)

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“…Kandasamy et al [65] proposed an asynchronous parallel BO based on Thompson sampling approach [66]. Pokuri et al [67] implemented PARyOpt package with an asynchronous function evaluator…”

Section: Asynchronous Batch Parallelmentioning

confidence: 99%

aphBO-2GP-3B: A budgeted asynchronous parallel multi-acquisition functions for constrained Bayesian optimization on high-performing computing architecture

Tran¹,

Eldred²,

Wildey³

et al. 2020

Preprint

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High-fidelity complex engineering simulations are highly predictive, but also computationally expensive and often require substantial computational efforts. The mitigation of computational burden is usually enabled through parallelism in high-performance cluster (HPC) architecture. Optimization problems associated with these applications is a challenging problem due to the high computational cost of the high-fidelity simulations. In this paper, an asynchronous constrained batchparallel Bayesian optimization method is proposed to efficiently solve the computationally-expensive simulation-based optimization problems on the HPC platform, with a budgeted computational resource, where the maximum number of simulations is a constant. The advantages of this method are three-fold. First, the efficiency of the Bayesian optimization is improved, where multiple input locations are evaluated massively parallel in an asynchronous manner to accelerate the optimization convergence with respect to physical runtime. This efficiency feature is further improved so that when each of the inputs is finished, another input is queried without waiting for the whole batch to complete. Second, the method can handle both known and unknown constraints. The known constraints are formulated as inequality constraints, which are incorporated by penalizing the acquisition function. The unknown constraints, which cannot be accessed without evaluating the objective function, are coupled to the aphBO-2GP-3B framework using a binary classifier to distinguish feasible and infeasible regions. Third, the proposed method considers several acquisition functions at the same time and sample based on an evolving probability mass distribution function using GP-Hedge scheme [1], where parameters are corresponding to the performance of each acquisition function. The proposed framework is termed aphBO-2GP-3B, which corresponds to

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“…Kandasamy et al [65] proposed an asynchronous parallel BO based on Thompson sampling approach [66]. Pokuri et al [67] implemented PARyOpt package with an asynchronous function evaluator…”

Section: Asynchronous Batch Parallelmentioning

confidence: 99%

aphBO-2GP-3B: A budgeted asynchronous parallel multi-acquisition functions for constrained Bayesian optimization on high-performing computing architecture

Tran¹,

Eldred²,

Wildey³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…In the present context, as opposed to the usual approach of optimizing one infill-criterion per update of the Radial-Basis surrogate, we perform asynchronous optimization using multiple infill-criteria by giving a range of weights for exploration (high-variance) vs. exploitation (high-mean), which updates the response surface at multiple points after each iteration. This enables efficient utilization of High-Performance Computing (HPC) resources, a useful method for accelerating convergence in computationally difficulty problems (the reader is directed to [36] for additional details).…”

Section: Design Problemmentioning

confidence: 99%

Shape design for stabilizing microparticles in inertial microfluidic flows

et al. 2020

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Design of microparticles which stabilize at the centerline of a channel flow when part of a dilute suspension is examined numerically for moderate Reynolds numbers (10 ≤ Re ≤ 80). This problem is motivated by the need for design of shaped particle carriers for use in next generation cell cytometry devices. Stability metrics for particles with arbitrary shapes are formulated based on linear-stability theory. Particle shape is parametrized by a compact, Non-Uniform Rational B-Spline (NURBS)-based representation. Shape-design is posed as an optimization problem and solved using adaptive Bayesian optimization. We focus on designing particles for maximal stability at the channel-centerline robust to perturbations. Our results indicate that centerline-focusing particles are families of characteristic "fish"/"bottle"/"dumbbell"-like shapes, exhibiting fore-aft asymmetry. A parametric exploration is then performed to identify stable particle-designs at different k's (particle chord-to-channel width ratio) and Re's (0.1 ≤ k ≤ 0.4, 10 ≤ Re ≤ 80). Particles at high-k's and Re's are highly stabilized when compared to those at low-k's and Re's. A comparison of the modified dumbbell designs from the current framework also shows better performance to perturbations in Fluid-Structure Interaction (FSI) when compared to the rod-disk model-dumbbell reported previously (Uspal & Doyle, 2014) for low-Re Hele-Shaw flow. We identify a basin of attraction around the centerline, within which any arbitrary release results in rotationally stable centerline-focusing. We find that this basin spans larger release-angle-ranges and lateral locations (tending to the channel width) for narrower channels. This effectively standardizes the notion of global focusing using the current stability-paradigm in narrow channels, which eliminates the need for an independent design for global-focusing in such configurations. The framework detailed in this work is illustrated for 2D cases and is generalizable to stability in 3D flow-fields. The current formulation is agnostic to Re and particle/channel geometry which indicates substantial potential for integration with imaging flow-cytometry tools and microfluidic biosensing-assays.

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PARyOpt: A software for Parallel Asynchronous Remote Bayesian Optimization

Cited by 2 publications

References 0 publications

aphBO-2GP-3B: A budgeted asynchronous parallel multi-acquisition functions for constrained Bayesian optimization on high-performing computing architecture

aphBO-2GP-3B: A budgeted asynchronous parallel multi-acquisition functions for constrained Bayesian optimization on high-performing computing architecture

Shape design for stabilizing microparticles in inertial microfluidic flows

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