386 research outputs found
Grassland: A Rapid Algebraic Modeling System for Million-variable Optimization
An algebraic modeling system (AMS) is a type of mathematical software for optimization problems, which allows users to define symbolic mathematical models in a specific language, instantiate them with given source of data, and solve them with the aid of external solver engines. With the bursting scale of business models and increasing need for timeliness, traditional AMSs are not sufficient to meet the following industry needs: 1) million-variable models need to be instantiated from raw data very efficiently; 2) Strictly feasible solution of million-variable models need to be delivered in a rapid manner to make up-to-date decisions against highly dynamic environments. Grassland is a rapid AMS that provides an end-to-end solution to tackle these emerged new challenges. It integrates a parallelized instantiation scheme for large-scale linear constraints, and a sequential decomposition method that accelerates model solving exponentially with an acceptable loss of optimality. Extensive benchmarks on both classical models and real enterprise scenario demonstrate 6-10x speedup of Grassland over state-of-the-art solutions on model instantiation. Our proposed system has been deployed in the large-scale real production planning scenario of Huawei. With the aid of our decomposition method, Grassland successfully accelerated Huawei's million-variable production planning simulation pipeline from hours to 3-5 minutes, supporting near-real-time production plan decision making against highly dynamic supply-demand environment
Performance Portable Solid Mechanics via Matrix-Free -Multigrid
Finite element analysis of solid mechanics is a foundational tool of modern
engineering, with low-order finite element methods and assembled sparse
matrices representing the industry standard for implicit analysis. We use
performance models and numerical experiments to demonstrate that high-order
methods greatly reduce the costs to reach engineering tolerances while enabling
effective use of GPUs. We demonstrate the reliability, efficiency, and
scalability of matrix-free -multigrid methods with algebraic multigrid
coarse solvers through large deformation hyperelastic simulations of multiscale
structures. We investigate accuracy, cost, and execution time on multi-node CPU
and GPU systems for moderate to large models using AMD MI250X (OLCF Crusher),
NVIDIA A100 (NERSC Perlmutter), and V100 (LLNL Lassen and OLCF Summit),
resulting in order of magnitude efficiency improvements over a broad range of
model properties and scales. We discuss efficient matrix-free representation of
Jacobians and demonstrate how automatic differentiation enables rapid
development of nonlinear material models without impacting debuggability and
workflows targeting GPUs
High-Performance Computing: Dos and Donâts
Computational fluid dynamics (CFD) is the main field of computational mechanics that has historically benefited from advances in high-performance computing. High-performance computing involves several techniques to make a simulation efficient and fast, such as distributed memory parallelism, shared memory parallelism, vectorization, memory access optimizations, etc. As an introduction, we present the anatomy of supercomputers, with special emphasis on HPC aspects relevant to CFD. Then, we develop some of the HPC concepts and numerical techniques applied to the complete CFD simulation framework: from preprocess (meshing) to postprocess (visualization) through the simulation itself (assembly and iterative solvers)
ToDD: Topological Compound Fingerprinting in Computer-Aided Drug Discovery
In computer-aided drug discovery (CADD), virtual screening (VS) is used for
identifying the drug candidates that are most likely to bind to a molecular
target in a large library of compounds. Most VS methods to date have focused on
using canonical compound representations (e.g., SMILES strings, Morgan
fingerprints) or generating alternative fingerprints of the compounds by
training progressively more complex variational autoencoders (VAEs) and graph
neural networks (GNNs). Although VAEs and GNNs led to significant improvements
in VS performance, these methods suffer from reduced performance when scaling
to large virtual compound datasets. The performance of these methods has shown
only incremental improvements in the past few years. To address this problem,
we developed a novel method using multiparameter persistence (MP) homology that
produces topological fingerprints of the compounds as multidimensional vectors.
Our primary contribution is framing the VS process as a new topology-based
graph ranking problem by partitioning a compound into chemical substructures
informed by the periodic properties of its atoms and extracting their
persistent homology features at multiple resolution levels. We show that the
margin loss fine-tuning of pretrained Triplet networks attains highly
competitive results in differentiating between compounds in the embedding space
and ranking their likelihood of becoming effective drug candidates. We further
establish theoretical guarantees for the stability properties of our proposed
MP signatures, and demonstrate that our models, enhanced by the MP signatures,
outperform state-of-the-art methods on benchmark datasets by a wide and highly
statistically significant margin (e.g., 93% gain for Cleves-Jain and 54% gain
for DUD-E Diverse dataset).Comment: NeurIPS, 2022 (36th Conference on Neural Information Processing
Systems
A quasiâcacheâaware model for optimal domain partitioning in parallel geometric multigrid
Stencil computations form the heart of numerical simulations to solve Partial Differential Equations using Finite Difference, Finite Element, and Finite Volume methods. Geometric Multigrid is an optimal O(N), hierarchical tool employing stencil computations in its chief constituents, namely, smoothing, restriction, and interpolation. When Multigrid is parallelized over distributedâshared memory architectures, traditionally, the domain partitioning creates cubic partitions of the mesh to minimize overall communication. Thus, the orthodox approach considers only loadâbalancing and communication minimization for completely determining the domain partitioning. In this article, we show that these two factors are not sufficient to obtain optimal partitions for Parallel Geometric Multigrid. To this effect, we develop and validate a high level analytical model to show that âclose to 2âDâ partitions for Geometric Multigrid can give higher performance than the partitions returned by the MPI_Dims_create() function which minimizes the communication volume by default. We quantify subâdomain level cacheâmisses in Parallel Geometric Multigrid and obtain families of optimal domain partitions. We conclude that the subâdomain level cacheâmisses for the applicationâspecific stencil computational kernel and communicated planes should be taken into account in addition to communication minimization/loadâbalance to obtain optimal partitions for Parallel Geometric Multigrid
Heterogeneous CPU/GPU co-execution of CFD simulations on the POWER9 architecture: Application to airplane aerodynamics
High fidelity Computational Fluid Dynamics simulations are generally
associated with large computing requirements, which are progressively acute
with each new generation of supercomputers. However, significant research
efforts are required to unlock the computing power of leading-edge systems,
currently referred to as pre-Exascale systems, based on increasingly complex
architectures. In this paper, we present the approach implemented in the
computational mechanics code Alya. We describe in detail the parallelization
strategy implemented to fully exploit the different levels of parallelism,
together with a novel co-execution method for the efficient utilization of
heterogeneous CPU/GPU architectures. The latter is based on a multi-code
co-execution approach with a dynamic load balancing mechanism. The assessment
of the performance of all the proposed strategies has been carried out for
airplane simulations on the POWER9 architecture accelerated with NVIDIA Volta
V100 GPUs
Computational Intelligent Models for Alzheimer's Prediction Using Audio Transcript Data
Alzheimer's dementia (AD) is characterized by memory loss, which is one of the earliest symptoms to develop. In this study, we investigated audio transcript data of patients with Alzheimer's dementia. The study involved the use of three intelligent computational approaches: conventional machine learning (Support Vector Machine, Random Forest, Decision Tree), sequential deep learning (LSTM, bidirectional LSTM, CNN-LSTM), and transfer learning (BERT, XLNet) models for automatic detection of linguistic indicators for early diagnosis of Alzheimer's dementia. These models were trained on the DementiaBank clinical transcript dataset. The grid search tuning approach is used for tuning the values of the hyperparameters. Text vectorization is done using the Term Frequency-Inverse Document Frequency (TF-IDF) information retrieval approach. TF-IDF is based on the Bag of Words (BoW) paradigm, which deals with the less and more relevant words in a transcript. Results were evaluated and compared using several performance metrics. The state-of-the-art techniques implemented on DementiaBank dataset in our methodology achieved better performance in terms of accuracy. Transfer learning models showed better classification results in comparison to sequential deep learning models. However, sequential deep learning models outperformed traditional machine learning models. Overall, in terms of accuracy, BERT and XLNet were the most accurate, with accuracy of 93 % and 92 %, respectively
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