Proceedings of the Platform for Advanced Scientific Computing Conference最新文献

SweepNet: A Lightweight CNN Architecture for the Classification of Adaptive Genomic Regions SweepNet:用于自适应基因组区域分类的轻量级CNN架构

Proceedings of the Platform for Advanced Scientific Computing Conference Pub Date : 2023-06-26 DOI: 10.1145/3592979.3593411

Hanqing Zhao, P. Pavlidis, Nikolaos S. Alachiotis

{"title":"SweepNet: A Lightweight CNN Architecture for the Classification of Adaptive Genomic Regions","authors":"Hanqing Zhao, P. Pavlidis, Nikolaos S. Alachiotis","doi":"10.1145/3592979.3593411","DOIUrl":"https://doi.org/10.1145/3592979.3593411","url":null,"abstract":"The accurate identification of positive selection in genomes represents a challenge in the field of population genomics. Several recent approaches have cast this problem as an image classification task and employed Convolutional Neural Networks (CNNs). However, limited efforts have been placed on discovering a practical CNN architecture that can classify images visualizing raw genomic data in the presence of population bottlenecks, migration, and recombination hotspots, factors that typically confound the identification and localization of adaptive genomic regions. In this work, we present SweepNet, a new CNN architecture that resulted from a thorough hyper-parameter-based architecture exploration process. SweepNet has a higher training efficiency than existing CNNs and requires considerably less epochs to achieve high validation accuracy. Furthermore, it performs consistently better in the presence of confounding factors, generating models with higher validation accuracy and lower top-1 error rate for distinguishing between neutrality and a selective sweep. Unlike existing network architectures, the number of trainable parameters of SweepNet remains constant irrespective of the sample size and number of Single Nucleotide Polymorphisms, which reduces the risk of overfitting and leads to more efficient training for large datasets. Our SweepNet implementation is available for download at: https://github.com/Zhaohq96/SweepNet.","PeriodicalId":174137,"journal":{"name":"Proceedings of the Platform for Advanced Scientific Computing Conference","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115155256","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

AI Super-Resolution Subfilter Modeling for Multi-Physics Flows 多物理流的AI超分辨率子滤波器建模

Proceedings of the Platform for Advanced Scientific Computing Conference Pub Date : 2023-06-26 DOI: 10.1145/3592979.3593414

M. Bode

引用次数: 1

Scalable Riemann Solvers with the Discontinuous Galerkin Method for Hyperbolic Network Simulation 双曲型网络仿真的不连续Galerkin法可伸缩Riemann解算器

Proceedings of the Platform for Advanced Scientific Computing Conference Pub Date : 2023-06-26 DOI: 10.1145/3592979.3593421

Aidan Hamilton, Jing-Mei Qiu, Hong Zhang

{"title":"Scalable Riemann Solvers with the Discontinuous Galerkin Method for Hyperbolic Network Simulation","authors":"Aidan Hamilton, Jing-Mei Qiu, Hong Zhang","doi":"10.1145/3592979.3593421","DOIUrl":"https://doi.org/10.1145/3592979.3593421","url":null,"abstract":"We develop a set of highly efficient and effective computational algorithms and simulation tools for fluid simulations on a network. The mathematical models are a set of hyperbolic conservation laws on edges of a network, as well as coupling conditions on junctions of a network. For example, the shallow water system, together with flux balance and continuity conditions at river intersections, model water flows on a river network. The computationally accurate and robust discontinuous Galerkin methods, coupled with explicit strong stability preserving Runge-Kutta methods, are implemented for simulations on network edges. Meanwhile, linear and nonlinear scalable Riemann solvers are being developed and implemented at network vertices. These network simulations result in tools that are added to the existing PETSc and DMNetwork software libraries for the scientific community in general. Simulation results of a shallow water system on a Mississippi river network with over one billion network variables are performed on an extreme-scale computer using up to 8,192 processor with an optimal parallel efficiency. Further potential applications include traffic flow simulations on a highway network and blood flow simulations on a arterial network, among many others.","PeriodicalId":174137,"journal":{"name":"Proceedings of the Platform for Advanced Scientific Computing Conference","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123930284","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

Hardware-Agnostic Interactive Exascale In Situ Visualization of Particle-In-Cell Simulations 与硬件无关的粒子在细胞内模拟的交互式百亿亿次原位可视化

Proceedings of the Platform for Advanced Scientific Computing Conference Pub Date : 2023-06-26 DOI: 10.1145/3592979.3593408

F. Meyer, Benjamín Hernández, R. Pausch, R. Widera, David Groß, S. Bastrakov, A. Huebl, G. Juckeland, J. Kelling, M. Leinhauser, David Rogers, U. Schramm, K. Steiniger, S. Gumhold, Jeff Young, M. Bussmann, S. Chandrasekaran, A. Debus

{"title":"Hardware-Agnostic Interactive Exascale In Situ Visualization of Particle-In-Cell Simulations","authors":"F. Meyer, Benjamín Hernández, R. Pausch, R. Widera, David Groß, S. Bastrakov, A. Huebl, G. Juckeland, J. Kelling, M. Leinhauser, David Rogers, U. Schramm, K. Steiniger, S. Gumhold, Jeff Young, M. Bussmann, S. Chandrasekaran, A. Debus","doi":"10.1145/3592979.3593408","DOIUrl":"https://doi.org/10.1145/3592979.3593408","url":null,"abstract":"The volume of data generated by exascale simulations requires scalable tools for analysis and visualization. Due to the relatively low I/O bandwidth of modern HPC systems, it is crucial to work as close as possible with simulated data via in situ approaches. In situ visualization provides insights into simulation data and, with the help of additional interactive analysis tools, can support the scientific discovery process at an early stage. Such in situ visualization tools need to be hardware-independent given the ever-increasing hardware diversity of modern supercomputers. We present a new in situ 3D vector field visualization algorithm for particle-in-cell (PIC) simulations and performance evaluation of the solution developed at large-scale. We create a solution in a hardware-agnostic approach to support high throughput and interactive in situ processing on leadership class computing systems. To that end, we demonstrate performance portability on Summit's and the Frontier's pre-exascale testbed at the Oak Ridge Leadership Computing Facility.","PeriodicalId":174137,"journal":{"name":"Proceedings of the Platform for Advanced Scientific Computing Conference","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132203672","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

Towards Lattice QCD+QED Simulations on GPUs 基于gpu的点阵QCD+QED仿真研究

Proceedings of the Platform for Advanced Scientific Computing Conference Pub Date : 2023-06-26 DOI: 10.1145/3592979.3593406

Roman Gruber, Anton Kozhevnikov, M. Marinković, T. Schulthess, R. Solcà

引用次数: 0

Performance Study of Convolutional Neural Network Architectures for 3D Incompressible Flow Simulations 卷积神经网络结构在三维不可压缩流动模拟中的性能研究

Proceedings of the Platform for Advanced Scientific Computing Conference Pub Date : 2023-06-26 DOI: 10.1145/3592979.3593416

Ekhi Ajuria Illarramendi, M. Bauerheim, N. Ashton, Coretin Lapeyre, B. Cuenot

{"title":"Performance Study of Convolutional Neural Network Architectures for 3D Incompressible Flow Simulations","authors":"Ekhi Ajuria Illarramendi, M. Bauerheim, N. Ashton, Coretin Lapeyre, B. Cuenot","doi":"10.1145/3592979.3593416","DOIUrl":"https://doi.org/10.1145/3592979.3593416","url":null,"abstract":"Recently, correctly handling spatial information from multiple scales has proven to be essential in Machine Learning (ML) applications on Computational Fluid Dynamics (CFD) problems. For these type of applications, Convolutional Neural Networks (CNN) that use Multiple Downsampled Branches (MDBs) to efficiently encode spatial information from different spatial scales have proven to be some of the most successful architectures. However, not many guidelines exist to build these architectures, particularly when applied to more challenging 3D configurations. Thus, this work focuses on studying the impact of the choice of the number of down-sampled branches, accuracy and performance-wise in 3D incompressible fluid test cases, where a CNN is used to solve the Poisson equation. The influence of this parameter is assessed by performing multiple trainings of Unet architectures with varying MDBs on a cloud-computing environment. These trained networks are then tested on two 3D CFD problems: a plume and a Von Karman vortex street at various operating points, where the solution of the neural network is coupled to a nonlinear advection equation.","PeriodicalId":174137,"journal":{"name":"Proceedings of the Platform for Advanced Scientific Computing Conference","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130424829","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

FPGA Acceleration for HPC Supercapacitor Simulations 用于HPC超级电容器仿真的FPGA加速

Proceedings of the Platform for Advanced Scientific Computing Conference Pub Date : 2023-06-26 DOI: 10.1145/3592979.3593419

Charles Prouveur, M. Haefele, Tobias Kenter, Nils Voss

{"title":"FPGA Acceleration for HPC Supercapacitor Simulations","authors":"Charles Prouveur, M. Haefele, Tobias Kenter, Nils Voss","doi":"10.1145/3592979.3593419","DOIUrl":"https://doi.org/10.1145/3592979.3593419","url":null,"abstract":"In the search of more energy efficient computing devices that could be assembled to build future exascale systems, this study proposes a chip to chip comparison between a CPU, a GPU and a FPGA, as well as a scalability study on multiple FPGAs from two of the available vendors. The application considered here has been extracted from a production code in material science. This allows for the benchmarking of different implementations to be performed on a production test case and not just theoretical ones. The core algorithm is a matrix free conjugate gradient that computes the total electrostatic energy with an Ewald summation at each iteration. This paper depicts the original MPI implementation of the application, details a numerical accuracy study and explains the methodology followed as well as the resulting FPGA implementation based on MaxCompiler. The FPGA implementation using 40 bits floating point number representation outperforms the CPU implementation both in terms of computing power and energy usage resulting in an energy efficiency more than 15 times better. Compared to the GPU of the same generation, the FPGA reaches 60% of the GPU performance while the ratio of the performance per watt is still better by a factor of 2. Thanks to its low average power usage, the FPGA bests both fully loaded CPU and GPU in terms of number of conjugate gradient iterations per second and per watt. Finally, an implementation using oneAPI is described as well, showcasing a new development environment for FPGA in HPC.","PeriodicalId":174137,"journal":{"name":"Proceedings of the Platform for Advanced Scientific Computing Conference","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114071485","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

Understanding the Computing and Analysis Needs for Resiliency of Power Systems from Severe Weather Impacts 了解电力系统在恶劣天气影响下的弹性计算和分析需求

Proceedings of the Platform for Advanced Scientific Computing Conference Pub Date : 2023-06-26 DOI: 10.1145/3592979.3593424

J. Sanyal, M. Dumas, Sangkeun Lee, S. Chinthavali, J. King, Srijib Mukherjee

{"title":"Understanding the Computing and Analysis Needs for Resiliency of Power Systems from Severe Weather Impacts","authors":"J. Sanyal, M. Dumas, Sangkeun Lee, S. Chinthavali, J. King, Srijib Mukherjee","doi":"10.1145/3592979.3593424","DOIUrl":"https://doi.org/10.1145/3592979.3593424","url":null,"abstract":"As the frequency and intensity of severe weather has increased, its effect on the electric grid has manifested in the form of significantly more and larger outages in the United States. This has become especially true for regions that were previously isolated from weather extremes. In this paper, we analyze the weather impacts on the electric power grid across a variety of weather conditions, draw correlations, and provide practical insights into the operational state of these systems. High resolution computational modeling of specific meteorological variables, computational approaches to solving power system models under these conditions, and the types of resiliency needs are highlighted as goal-oriented computing approaches are being built to address grid resiliency needs. An example analysis correlating outages to 1km day-ahead weather from two historical winter storms, calculated on a large cluster using a combination of interpolated and extrapolated inputs from multiple instrumented sites to workflows that produce primary meteorological outputs, is shown as initial proof of concept.","PeriodicalId":174137,"journal":{"name":"Proceedings of the Platform for Advanced Scientific Computing Conference","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132966376","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

Longitudinal Effects on Plant Species Involved in Agriculture and Pandemic Emergence Undergoing Changes in Abiotic Stress 非生物胁迫变化对农业和大流行发生中涉及的植物物种的纵向影响

Proceedings of the Platform for Advanced Scientific Computing Conference Pub Date : 2023-06-26 DOI: 10.1145/3592979.3593402

Mikaela Cashman, Verónica G. Melesse Vergara, John H. Lagergren, Matthew Lane, Jean Merlet, Mikaela Atkinson, J. Streich, C. Bradburne, R. Plowright, Wayne Joubert, Daniel Jacobson

{"title":"Longitudinal Effects on Plant Species Involved in Agriculture and Pandemic Emergence Undergoing Changes in Abiotic Stress","authors":"Mikaela Cashman, Verónica G. Melesse Vergara, John H. Lagergren, Matthew Lane, Jean Merlet, Mikaela Atkinson, J. Streich, C. Bradburne, R. Plowright, Wayne Joubert, Daniel Jacobson","doi":"10.1145/3592979.3593402","DOIUrl":"https://doi.org/10.1145/3592979.3593402","url":null,"abstract":"In this work we identify changes in high-resolution zones across the globe linked by environmental similarity that have implications for agriculture, bioenergy, and zoonosis. We refine exhaustive vector comparison methods with improved similarity metrics as well as provide multiple methods of amalgamation across 744 months of climatic data. The results of the vector comparison are captured as networks which are analyzed using static and longitudinal comparison methods to reveal locations around the globe experiencing dramatic changes in abiotic stress. Specifically we (i) incorporate updated similarity scores and provide a comparison between similarity metrics, (ii) implement a new feature for resource optimization, (iii) compare an agglomerative view to a longitudinal view, (iv) compare across 2-way and 3-way vector comparisons, (v) implement a new form of analysis, and (vi) demonstrate biological applications and discuss implications across a diverse set of species distributions by detecting changes that affect their habitats. Species of interest are related to agriculture (e.g., coffee, wine, chocolate), bioenergy (e.g., poplar, switchgrass, pennycress), as well as those living in zones of concern for zoonotic spillover that may lead to pandemics (e.g., eucalyptus, flying foxes).","PeriodicalId":174137,"journal":{"name":"Proceedings of the Platform for Advanced Scientific Computing Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129241809","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

Cornerstone: Octree Construction Algorithms for Scalable Particle Simulations 基石:可扩展粒子模拟的八叉树构造算法

Proceedings of the Platform for Advanced Scientific Computing Conference Pub Date : 2023-06-26 DOI: 10.1145/3592979.3593417

Sebastian Keller, Aurélien Cavelan, R. Cabezón, L. Mayer, F. Ciorba

{"title":"Cornerstone: Octree Construction Algorithms for Scalable Particle Simulations","authors":"Sebastian Keller, Aurélien Cavelan, R. Cabezón, L. Mayer, F. Ciorba","doi":"10.1145/3592979.3593417","DOIUrl":"https://doi.org/10.1145/3592979.3593417","url":null,"abstract":"This paper presents an octree construction method, called Cornerstone, that facilitates global domain decomposition and interactions between particles in mesh-free numerical simulations. Our method is based on algorithms developed for 3D computer graphics, which we extend to distributed high performance computing (HPC) systems. Cornerstone yields global and locally essential octrees and is able to operate on all levels of tree hierarchies in parallel. The resulting octrees are suitable for supporting the computation of various kinds of short and long range interactions in N-body methods, such as Barnes-Hut and the Fast Multipole Method (FMM). While we provide a CPU implementation, Cornerstone may run entirely on GPUs. This results in significantly faster tree construction compared to execution on CPUs and serves as a powerful building block for the design of simulation codes that move beyond an offloading approach, where only numerically intensive tasks are dispatched to GPUs. With data residing exclusively in GPU memory, Cornerstone eliminates data movements between CPUs and GPUs. As an example, we employ Cornerstone to generate locally essential octrees for a Barnes-Hut treecode running on almost the full LUMI-G system with up to 8 trillion particles.","PeriodicalId":174137,"journal":{"name":"Proceedings of the Platform for Advanced Scientific Computing Conference","volume":"55 49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131728849","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: 2