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1. Managing code transformations for better performance portability

   https://doi.org/10.1177/1094342019865606  

   Teixeira, Thiago SFX ; Gropp, William ; Padua, David ( May 2019 , The International Journal of High Performance Computing Applications)

   null (Ed.)

   Code optimization is an intricate task that is getting more complex as computing systems evolve. Managing the program optimization process, including the implementation and evaluation of code variants, is tedious, inefficient, and errors are likely to be introduced in the process. Moreover, because each platform typically requires a different sequence of transformations to fully harness its computing power, the optimization process complexity grows as new platforms are adopted. To address these issues, systems and frameworks have been proposed to automate the code optimization process. They, however, have not been widely adopted and are primarily used by experts with deep knowledge about underlying architecture and compiler intricacies. This article describes the requirements that we believe necessary for making automatic performance tuning more broadly used, especially in complex, long-lived high-performance computing applications. Besides discussing limitations of current systems and strategies to overcome these, we describe the design of a system that is able to semi-automatically generate efficient platform-specific code. In the proposed system, the code optimization is programmer-guided, separately from application code, on an external file in what we call optimization programming. The language to program the optimization process is able to represent complex collections of transformations and, as a result, generate efficient platform-specific code. A database manages different optimized versions of code regions, providing a pragmatic approach to performance portability, and the framework itself has separate components, allowing the optimized code to be used on systems without installing all of the modules required for the code generation. We present experiments on two different platforms to illustrate the generation of efficient platform-specific code that performs comparable to hand-optimized, vendor-provided code. 

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2. Metaform optics: Bridging nanophotonics and freeform optics

   https://doi.org/10.1126/sciadv.abe5112  

   Nikolov, Daniel K. ; Bauer, Aaron ; Cheng, Fei ; Kato, Hitoshi ; Vamivakas, A. Nick ; Rolland, Jannick P. ( April 2021 , Science Advances)

   null (Ed.)

   The demand for high-resolution optical systems with a compact form factor, such as augmented reality displays, sensors, and mobile cameras, requires creating new optical component architectures. Advances in the design and fabrication of freeform optics and metasurfaces make them potential solutions to address the previous needs. Here, we introduce the concept of a metaform—an optical surface that integrates the combined benefits of a freeform optic and a metasurface into a single optical component. We experimentally realized a miniature imager using a metaform mirror. The mirror is fabricated via an enhanced electron beam lithography process on a freeform substrate. The design degrees of freedom enabled by a metaform will support a new generation of optical systems. 

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3. Fast etch recipe creation with automated model-based process optimization

   https://doi.org/10.1117/12.2583868  

   Ban, Yang ; Kearney, Kara ; Sundahl, Bryan ; Medina, Leandro ; Bonnecaze, Roger T. ; Chopra, Meghali J. ( February 2021 , Proc. SPIE 11615, Advanced Etch Technology and Process Integration for Nanopatterning X)

   Bannister, Julie ; Mohanty, Nihar (Ed.)

   A method for automated creation and optimization of multistep etch recipes is presented. Here we demonstrate how an automated model-based process optimization approach can cut the cost and time of recipe creation by 75% or more as compared with traditional experimental design approaches. Underlying the success of the method are reduced-order physics-based models for simulating the process and performing subsequent analysis of the multi dimensional parameter space. SandBox Studio™ AI is used to automate the model selection, model calibration and subsequent process optimization. The process engineer is only required to provide the incoming stack and experimental measurements for model calibration and updates. The method is applied to the optimization of a channel etch for 3D NAND devices. A reduced-order model that captures the physics and chemistry of the multistep reaction is automatically selected and calibrated. A mirror AI model is simultaneously and automatically created to enable nearly instantaneous predictions across the large process space. The AI model is much faster to evaluate and is used to make a Quilt™, a 2D projection of etch performance in the multidimensional process parameter space. A Quilt™ process map is then used to automatically determine the optimal process window to achieve the target CDs. 

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4. Topology Optimization With Many Right-Hand Sides Using Mirror Descent Stochastic Approximation—Reduction From Many to a Single Sample

   https://doi.org/10.1115/1.4045902  

   Zhang, Xiaojia Shelly ; de Sturler, Eric ; Shapiro, Alexander ( May 2020 , Journal of Applied Mechanics)

   Abstract Practical engineering designs typically involve many load cases. For topology optimization with many deterministic load cases, a large number of linear systems of equations must be solved at each optimization step, leading to an enormous computational cost. To address this challenge, we propose a mirror descent stochastic approximation (MD-SA) framework with various step size strategies to solve topology optimization problems with many load cases. We reformulate the deterministic objective function and gradient into stochastic ones through randomization, derive the MD-SA update, and develop algorithmic strategies. The proposed MD-SA algorithm requires only low accuracy in the stochastic gradient and thus uses only a single sample per optimization step (i.e., the sample size is always one). As a result, we reduce the number of linear systems to solve per step from hundreds to one, which drastically reduces the total computational cost, while maintaining a similar design quality. For example, for one of the design problems, the total number of linear systems to solve and wall clock time are reduced by factors of 223 and 22, respectively. 

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5. Targeting climate‐neutral hydrogen production: Integrating brown and blue pathways with green hydrogen infrastructure via a novel superstructure and simulation‐based life cycle optimization

   https://doi.org/10.1002/aic.17956  

   Lal, Apoorv ; You, Fengqi ( November 2022 , AIChE Journal)

   This article addresses the sustainable design of hydrogen (H₂) production systems that integrate brown and blue pathways with green hydrogen infrastructure. We develop a systematic framework to simultaneously optimize the process superstructure and operating conditions of steam methane reforming (SMR)‐based hydrogen production systems. A comprehensive superstructure that integrates SMR with multiple carbon dioxide capture technologies, electrolyzers, fuel cells, and working fluids in the organic rankine cycle is proposed under varying operating conditions. A life cycle optimization model is then developed by integrating superstructure optimization, life cycle assessment approach, techno‐economic assessment, and process optimization using extensive process simulation models and formulated as a mixed‐integer nonlinear program. We find that the optimal unit‐levelized cost of hydrogen ranges from $1.49 to $3.18 per kg H₂. Moreover, the most environmentally friendly process attains net‐zero life cycle greenhouse gas emissions compared to 10.55 kg CO₂‐eq per kg H₂for the most economically competitive process design.

    

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