2022 IEEE/ACM International Workshop on Artificial Intelligence and Machine Learning for Scientific Applications (AI4S)最新文献

A Case Study on Coupling OpenFOAM with Different Machine Learning Frameworks OpenFOAM与不同机器学习框架的耦合案例研究

2022 IEEE/ACM International Workshop on Artificial Intelligence and Machine Learning for Scientific Applications (AI4S) Pub Date : 2022-11-01 DOI: 10.1109/AI4S56813.2022.00007

F. Orland, Kim Sebastian Brose, Julian Bissantz, F. Ferraro, C. Terboven, C. Hasse

引用次数: 0

Practical Federated Learning Infrastructure for Privacy-Preserving Scientific Computing 保护隐私科学计算的实用联邦学习基础结构

2022 IEEE/ACM International Workshop on Artificial Intelligence and Machine Learning for Scientific Applications (AI4S) Pub Date : 2022-11-01 DOI: 10.1109/AI4S56813.2022.00012

Lesi Wang, Dongfang Zhao

{"title":"Practical Federated Learning Infrastructure for Privacy-Preserving Scientific Computing","authors":"Lesi Wang, Dongfang Zhao","doi":"10.1109/AI4S56813.2022.00012","DOIUrl":"https://doi.org/10.1109/AI4S56813.2022.00012","url":null,"abstract":"Federated learning (FL) is deemed a promising paradigm for privacy-preserving data analytics in collaborative scientific computing. However, there lacks an effective and easy-to-use FL infrastructure for scientific computing and high-performance computing (HPC) environments. The objective of this position paper is two-fold. Firstly, we identify three missing pieces of a scientific FL infrastructure: (i) a native MPI programming interface that can be seamlessly integrated into existing scientific applications, (ii) an independent data layer for the FL system such that the user can pick the persistent medium for her own choice, such as parallel file systems and multidimensional databases, and (iii) efficient encryption protocols that are optimized for scientific workflows. The second objective of this paper is to present a work-in-progress FL infrastructure, namely MPI-FL, which is implemented with PyTorch and MPI4py. We deploy MPI-FL on 1,000 CPU cores and evaluate it with four standard benchmarks: MNIST, Fashion-MNIST, CIFAR-10, and SVHN-extra. It is our hope that the scientific computing and HPC community would find MPI-FL useful for their FL-related projects.","PeriodicalId":262536,"journal":{"name":"2022 IEEE/ACM International Workshop on Artificial Intelligence and Machine Learning for Scientific Applications (AI4S)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131113594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Automated Continual Learning of Defect Identification in Coherent Diffraction Imaging 相干衍射成像缺陷识别的自动连续学习

2022 IEEE/ACM International Workshop on Artificial Intelligence and Machine Learning for Scientific Applications (AI4S) Pub Date : 2022-11-01 DOI: 10.1109/AI4S56813.2022.00006

Orcun Yildiz, Henry Chan, Krishnan Raghavan, W. Judge, M. Cherukara, Prasanna Balaprakash, S. Sankaranarayanan, T. Peterka

引用次数: 0

Ensuring AI For Science is Science: Making Randomness Portable 确保科学AI就是科学:让随机性可移植

2022 IEEE/ACM International Workshop on Artificial Intelligence and Machine Learning for Scientific Applications (AI4S) Pub Date : 2022-11-01 DOI: 10.1109/AI4S56813.2022.00011

H. Ahmed, Roselyne B. Tchoua, J. Lofstead

引用次数: 1

AI4S 22 Workshop Organization AI4S 22车间组织

2022 IEEE/ACM International Workshop on Artificial Intelligence and Machine Learning for Scientific Applications (AI4S) Pub Date : 2022-11-01 DOI: 10.1109/ai4s56813.2022.00005

引用次数: 0

Message from the AI4S22 Workshop Chairs 来自AI4S22工作坊主席的信息

2022 IEEE/ACM International Workshop on Artificial Intelligence and Machine Learning for Scientific Applications (AI4S) Pub Date : 2022-11-01 DOI: 10.1109/ai4s56813.2022.00004

引用次数: 0

Pattern-based Autotuning of OpenMP Loops using Graph Neural Networks 基于模式的OpenMP循环自动调谐使用图神经网络

2022 IEEE/ACM International Workshop on Artificial Intelligence and Machine Learning for Scientific Applications (AI4S) Pub Date : 2022-11-01 DOI: 10.1109/AI4S56813.2022.00010

Akashnil Dutta, J. Alcaraz, Ali TehraniJamsaz, A. Sikora, Eduardo César, A. Jannesari

{"title":"Pattern-based Autotuning of OpenMP Loops using Graph Neural Networks","authors":"Akashnil Dutta, J. Alcaraz, Ali TehraniJamsaz, A. Sikora, Eduardo César, A. Jannesari","doi":"10.1109/AI4S56813.2022.00010","DOIUrl":"https://doi.org/10.1109/AI4S56813.2022.00010","url":null,"abstract":"Stagnation of Moore's law has led to the increased adoption of parallel programming for enhancing performance of scientific applications. Frequently occurring code and design patterns in scientific applications are often used for transforming serial code to parallel. But, identifying these patterns is not easy. To this end, we propose using Graph Neural Networks for modeling code flow graphs to identify patterns in such parallel code. Additionally, identifying the runtime parameters for best performing parallel code is also challenging. We propose a pattern-guided deep learning based tuning approach, to help identify the best runtime parameters for OpenMP loops. Overall, we aim to identify commonly occurring patterns in parallel loops and use these patterns to guide auto-tuning efforts. We validate our hypothesis on 20 different applications from Polybench, and STREAM benchmark suites. This deep learning-based approach can identify the considered patterns with an overall accuracy of 91%. We validate the usefulness of using patterns for auto-tuning on tuning the number of threads, scheduling policies and chunk size on a single socket system, and the thread count and affinity on a multi-socket machine. Our approach achieves geometric mean speedups of $1.1times$ and $4.7times$ respectively over default OpenMP configurations, compared to brute-force speedups of $1.27times$ and $4.93times$ respectively.","PeriodicalId":262536,"journal":{"name":"2022 IEEE/ACM International Workshop on Artificial Intelligence and Machine Learning for Scientific Applications (AI4S)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130060797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Scalable Integration of Computational Physics Simulations with Machine Learning 计算物理模拟与机器学习的可扩展集成

2022 IEEE/ACM International Workshop on Artificial Intelligence and Machine Learning for Scientific Applications (AI4S) Pub Date : 2022-11-01 DOI: 10.1109/AI4S56813.2022.00013

Mathew Boyer, W. Brewer, D. Jude, I. Dettwiller

{"title":"Scalable Integration of Computational Physics Simulations with Machine Learning","authors":"Mathew Boyer, W. Brewer, D. Jude, I. Dettwiller","doi":"10.1109/AI4S56813.2022.00013","DOIUrl":"https://doi.org/10.1109/AI4S56813.2022.00013","url":null,"abstract":"Integration of machine learning with simulation is part of a growing trend, however, the augmentation of codes in a highly-performant, distributed manner poses a software development challenge. In this work, we explore the question of how to easily augment legacy simulation codes on high-performance computers (HPCs) with machine-learned surrogate models, in a fast, scalable manner. Initial naïve augmentation attempts required significant code modification and resulted in significant slowdown. This led us to explore inference server techniques, which allow for model calls through drop-in functions. In this work, we investigated TensorFlow Serving with $mathbf{gRPC}$ and RedisAI with SmartRedis for server-client inference implementations, where the deep learning platform runs as a persistent process on HPC compute node GPUs and the simulation makes client calls while running on the CPUs. We evaluated inference performance for several use cases on SCOUT, an IBM POWER9 supercomputer, including, real gas equations of state, machine-learned boundary conditions for rotorcraft aerodynamics, and super-resolution techniques. We will discuss key findings on performance. The lessons learned may provide useful advice for researchers to augment their simulation codes in an optimal manner.","PeriodicalId":262536,"journal":{"name":"2022 IEEE/ACM International Workshop on Artificial Intelligence and Machine Learning for Scientific Applications (AI4S)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122270200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Determining HEDP Foams' Quality with Multi-View Deep Learning Classification 用多视图深度学习分类确定HEDP泡沫的质量

2022 IEEE/ACM International Workshop on Artificial Intelligence and Machine Learning for Scientific Applications (AI4S) Pub Date : 2022-08-10 DOI: 10.1109/AI4S56813.2022.00009

Nadav Schneider, M. Rusanovsky, R. Gvishi, G. Oren

{"title":"Determining HEDP Foams' Quality with Multi-View Deep Learning Classification","authors":"Nadav Schneider, M. Rusanovsky, R. Gvishi, G. Oren","doi":"10.1109/AI4S56813.2022.00009","DOIUrl":"https://doi.org/10.1109/AI4S56813.2022.00009","url":null,"abstract":"High energy density physics (HEDP) experiments commonly involve a dynamic wave-front propagating inside a lowdensity foam. This effect affects its density and hence, its transparency. A common problem in foam production is the creation of defective foams. Accurate information on their dimension and homogeneity is required to classify the foams' quality. Therefore, those parameters are being characterized using a 3D-measuring laser confocal microscope. For each foam, five images are taken: two 2D images representing the top and bottom surface foam planes and three images of side cross-sections from 3D scannings. An expert has to do the complicated, harsh, and exhausting work of manually classifying the foam's quality through the image set and only then determine whether the foam can be used in experiments or not. Currently, quality has two binary levels of normal vs. defective. At the same time, experts are commonly required to classify a sub-class of normal-defective, i.e., defective foams but might be sufficient for the needed experiment. This sub-class is problematic due to inconclusive judgment that is primarily intuitive. In this work, we present a novel state─of─the─art multi-view deep learning classification model that mimics the physicist's perspective by automatically determining the foams' quality classification and thus aids the expert. Our model achieved 86% accuracy on upper and lower surface foam planes and 82% on the entire set, suggesting interesting heuristics to the problem. A significant added value in this work is the ability to regress the foam quality instead of binary deduction and even explain the decision visually. The source code used in this work, as well as other relevant sources, are available at: https://github.com/Scientific-Computing-Lab-NRCNIMulti-View-Foams.git.","PeriodicalId":262536,"journal":{"name":"2022 IEEE/ACM International Workshop on Artificial Intelligence and Machine Learning for Scientific Applications (AI4S)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124362398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

PhySRNet: Physics informed super-resolution network for application in computational solid mechanics 物理信息超分辨网络在计算固体力学中的应用

2022 IEEE/ACM International Workshop on Artificial Intelligence and Machine Learning for Scientific Applications (AI4S) Pub Date : 2022-06-30 DOI: 10.1109/AI4S56813.2022.00008

Rajat Arora

{"title":"PhySRNet: Physics informed super-resolution network for application in computational solid mechanics","authors":"Rajat Arora","doi":"10.1109/AI4S56813.2022.00008","DOIUrl":"https://doi.org/10.1109/AI4S56813.2022.00008","url":null,"abstract":"Traditional numerical approaches have been successfully used to model mechanical behavior of heterogeneous materials (composites, multicomponent alloys, and polycrystals) widely used in industrial applications. However, these methods require a fine mesh resulting in computationally expensive and time-consuming calculations. The physics-informed deep-learning based super-resolution framework (PhySRNet) introduced in this paper is aimed at overcoming this computational challenge. PhySRNet enables reconstruction of high-resolution solution fields from their low-resolution counterparts without requiring labeled data, thereby allowing researchers to run their numerical simulations on a coarse mesh. Through an illustrative example, we demonstrate that the super-resolved fields match the accuracy of an advanced numerical solver running at 400 times the coarse mesh resolution and satisfy the (highly nonlinear) governing laws. The approach opens the door to applying machine learning and traditional numerical approaches in tandem to reduce computational complexity and accelerate scientific discovery and engineering design.","PeriodicalId":262536,"journal":{"name":"2022 IEEE/ACM International Workshop on Artificial Intelligence and Machine Learning for Scientific Applications (AI4S)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114325637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8