Dalton Jones, Pierre-David Letourneau, Matthew J. Morse, M. Harper Langston
We present the Fast Chebyshev Transform (FCT), a fast, randomized algorithm to compute a Chebyshev approximation of functions in high-dimensions from the knowledge of the location of its nonzero Chebyshev coefficients. Rather than sampling a full-resolution Chebyshev grid in each dimension, we randomly sample several grids with varied resolutions and solve a least-squares problem in coefficient space in order to compute a polynomial approximating the function of interest across all grids simultaneously. We theoretically and empirically show that the FCT exhibits quasi-linear scaling and high numerical accuracy on challenging and complex high-dimensional problems. We demonstrate the effectiveness of our approach compared to alternative Chebyshev approximation schemes. In particular, we highlight our algorithm's effectiveness in high dimensions, demonstrating significant speedups over commonly-used alternative techniques.
{"title":"A Sparse Fast Chebyshev Transform for High-Dimensional Approximation","authors":"Dalton Jones, Pierre-David Letourneau, Matthew J. Morse, M. Harper Langston","doi":"arxiv-2309.14584","DOIUrl":"https://doi.org/arxiv-2309.14584","url":null,"abstract":"We present the Fast Chebyshev Transform (FCT), a fast, randomized algorithm\u0000to compute a Chebyshev approximation of functions in high-dimensions from the\u0000knowledge of the location of its nonzero Chebyshev coefficients. Rather than\u0000sampling a full-resolution Chebyshev grid in each dimension, we randomly sample\u0000several grids with varied resolutions and solve a least-squares problem in\u0000coefficient space in order to compute a polynomial approximating the function\u0000of interest across all grids simultaneously. We theoretically and empirically\u0000show that the FCT exhibits quasi-linear scaling and high numerical accuracy on\u0000challenging and complex high-dimensional problems. We demonstrate the\u0000effectiveness of our approach compared to alternative Chebyshev approximation\u0000schemes. In particular, we highlight our algorithm's effectiveness in high\u0000dimensions, demonstrating significant speedups over commonly-used alternative\u0000techniques.","PeriodicalId":501256,"journal":{"name":"arXiv - CS - Mathematical Software","volume":"14 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138521165","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}
Marton A. Goda, Peter H. Charlton, Joachim A. Behar
Photoplethysmography is a non-invasive optical technique that measures changes in blood volume within tissues. It is commonly and increasingly used for in a variety of research and clinical application to assess vascular dynamics and physiological parameters. Yet, contrary to heart rate variability measures, a field which has seen the development of stable standards and advanced toolboxes and software, no such standards and open tools exist for continuous photoplethysmogram (PPG) analysis. Consequently, the primary objective of this research was to identify, standardize, implement and validate key digital PPG biomarkers. This work describes the creation of a standard Python toolbox, denoted pyPPG, for long-term continuous PPG time series analysis recorded using a standard finger-based transmission pulse oximeter. The improved PPG peak detector had an F1-score of 88.19% for the state-of-the-art benchmark when evaluated on 2,054 adult polysomnography recordings totaling over 91 million reference beats. This algorithm outperformed the open-source original Matlab implementation by ~5% when benchmarked on a subset of 100 randomly selected MESA recordings. More than 3,000 fiducial points were manually annotated by two annotators in order to validate the fiducial points detector. The detector consistently demonstrated high performance, with a mean absolute error of less than 10 ms for all fiducial points. Based on these fiducial points, pyPPG engineers a set of 74 PPG biomarkers. Studying the PPG time series variability using pyPPG can enhance our understanding of the manifestations and etiology of diseases. This toolbox can also be used for biomarker engineering in training data-driven models. pyPPG is available on physiozoo.org
{"title":"pyPPG: A Python toolbox for comprehensive photoplethysmography signal analysis","authors":"Marton A. Goda, Peter H. Charlton, Joachim A. Behar","doi":"arxiv-2309.13767","DOIUrl":"https://doi.org/arxiv-2309.13767","url":null,"abstract":"Photoplethysmography is a non-invasive optical technique that measures\u0000changes in blood volume within tissues. It is commonly and increasingly used\u0000for in a variety of research and clinical application to assess vascular\u0000dynamics and physiological parameters. Yet, contrary to heart rate variability\u0000measures, a field which has seen the development of stable standards and\u0000advanced toolboxes and software, no such standards and open tools exist for\u0000continuous photoplethysmogram (PPG) analysis. Consequently, the primary\u0000objective of this research was to identify, standardize, implement and validate\u0000key digital PPG biomarkers. This work describes the creation of a standard\u0000Python toolbox, denoted pyPPG, for long-term continuous PPG time series\u0000analysis recorded using a standard finger-based transmission pulse oximeter.\u0000The improved PPG peak detector had an F1-score of 88.19% for the\u0000state-of-the-art benchmark when evaluated on 2,054 adult polysomnography\u0000recordings totaling over 91 million reference beats. This algorithm\u0000outperformed the open-source original Matlab implementation by ~5% when\u0000benchmarked on a subset of 100 randomly selected MESA recordings. More than\u00003,000 fiducial points were manually annotated by two annotators in order to\u0000validate the fiducial points detector. The detector consistently demonstrated\u0000high performance, with a mean absolute error of less than 10 ms for all\u0000fiducial points. Based on these fiducial points, pyPPG engineers a set of 74\u0000PPG biomarkers. Studying the PPG time series variability using pyPPG can\u0000enhance our understanding of the manifestations and etiology of diseases. This\u0000toolbox can also be used for biomarker engineering in training data-driven\u0000models. pyPPG is available on physiozoo.org","PeriodicalId":501256,"journal":{"name":"arXiv - CS - Mathematical Software","volume":"10 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138521260","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}
Paweł Maczuga, Maciej Skoczeń, Przemysław Rożnawski, Filip Tłuszcz, Marcin Szubert, Marcin Łoś, Witold Dzwinel, Keshav Pingali, Maciej Paszyński
We present an open-source Physics Informed Neural Network environment for simulations of transient phenomena on two-dimensional rectangular domains, with the following features: (1) it is compatible with Google Colab which allows automatic execution on cloud environment; (2) it supports two dimensional time-dependent PDEs; (3) it provides simple interface for definition of the residual loss, boundary condition and initial loss, together with their weights; (4) it support Neumann and Dirichlet boundary conditions; (5) it allows for customizing the number of layers and neurons per layer, as well as for arbitrary activation function; (6) the learning rate and number of epochs are available as parameters; (7) it automatically differentiates PINN with respect to spatial and temporal variables; (8) it provides routines for plotting the convergence (with running average), initial conditions learnt, 2D and 3D snapshots from the simulation and movies (9) it includes a library of problems: (a) non-stationary heat transfer; (b) wave equation modeling a tsunami; (c) atmospheric simulations including thermal inversion; (d) tumor growth simulations.
{"title":"Physics Informed Neural Network Code for 2D Transient Problems (PINN-2DT) Compatible with Google Colab","authors":"Paweł Maczuga, Maciej Skoczeń, Przemysław Rożnawski, Filip Tłuszcz, Marcin Szubert, Marcin Łoś, Witold Dzwinel, Keshav Pingali, Maciej Paszyński","doi":"arxiv-2310.03755","DOIUrl":"https://doi.org/arxiv-2310.03755","url":null,"abstract":"We present an open-source Physics Informed Neural Network environment for\u0000simulations of transient phenomena on two-dimensional rectangular domains, with\u0000the following features: (1) it is compatible with Google Colab which allows\u0000automatic execution on cloud environment; (2) it supports two dimensional\u0000time-dependent PDEs; (3) it provides simple interface for definition of the\u0000residual loss, boundary condition and initial loss, together with their\u0000weights; (4) it support Neumann and Dirichlet boundary conditions; (5) it\u0000allows for customizing the number of layers and neurons per layer, as well as\u0000for arbitrary activation function; (6) the learning rate and number of epochs\u0000are available as parameters; (7) it automatically differentiates PINN with\u0000respect to spatial and temporal variables; (8) it provides routines for\u0000plotting the convergence (with running average), initial conditions learnt, 2D\u0000and 3D snapshots from the simulation and movies (9) it includes a library of\u0000problems: (a) non-stationary heat transfer; (b) wave equation modeling a\u0000tsunami; (c) atmospheric simulations including thermal inversion; (d) tumor\u0000growth simulations.","PeriodicalId":501256,"journal":{"name":"arXiv - CS - Mathematical Software","volume":"16 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138521157","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}
This study proposes an Ensemble Differential Evolution with Simula-tion-Based Hybridization and Self-Adaptation (EDESH-SA) approach for inven-tory management (IM) under uncertainty. In this study, DE with multiple runs is combined with a simulation-based hybridization method that includes a self-adaptive mechanism that dynamically alters mutation and crossover rates based on the success or failure of each iteration. Due to its adaptability, the algorithm is able to handle the complexity and uncertainty present in IM. Utilizing Monte Carlo Simulation (MCS), the continuous review (CR) inventory strategy is ex-amined while accounting for stochasticity and various demand scenarios. This simulation-based approach enables a realistic assessment of the proposed algo-rithm's applicability in resolving the challenges faced by IM in practical settings. The empirical findings demonstrate the potential of the proposed method to im-prove the financial performance of IM and optimize large search spaces. The study makes use of performance testing with the Ackley function and Sensitivity Analysis with Perturbations to investigate how changes in variables affect the objective value. This analysis provides valuable insights into the behavior and robustness of the algorithm.
{"title":"Ensemble Differential Evolution with Simulation-Based Hybridization and Self-Adaptation for Inventory Management Under Uncertainty","authors":"Sarit Maitra, Vivek Mishra, Sukanya Kundu","doi":"arxiv-2309.12852","DOIUrl":"https://doi.org/arxiv-2309.12852","url":null,"abstract":"This study proposes an Ensemble Differential Evolution with Simula-tion-Based\u0000Hybridization and Self-Adaptation (EDESH-SA) approach for inven-tory management\u0000(IM) under uncertainty. In this study, DE with multiple runs is combined with a\u0000simulation-based hybridization method that includes a self-adaptive mechanism\u0000that dynamically alters mutation and crossover rates based on the success or\u0000failure of each iteration. Due to its adaptability, the algorithm is able to\u0000handle the complexity and uncertainty present in IM. Utilizing Monte Carlo\u0000Simulation (MCS), the continuous review (CR) inventory strategy is ex-amined\u0000while accounting for stochasticity and various demand scenarios. This\u0000simulation-based approach enables a realistic assessment of the proposed\u0000algo-rithm's applicability in resolving the challenges faced by IM in practical\u0000settings. The empirical findings demonstrate the potential of the proposed\u0000method to im-prove the financial performance of IM and optimize large search\u0000spaces. The study makes use of performance testing with the Ackley function and\u0000Sensitivity Analysis with Perturbations to investigate how changes in variables\u0000affect the objective value. This analysis provides valuable insights into the\u0000behavior and robustness of the algorithm.","PeriodicalId":501256,"journal":{"name":"arXiv - CS - Mathematical Software","volume":"12 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138521256","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}
Marcin Rogowski, Brandon C. Y. Yeung, Oliver T. Schmidt, Romit Maulik, Lisandro Dalcin, Matteo Parsani, Gianmarco Mengaldo
We propose a parallel (distributed) version of the spectral proper orthogonal decomposition (SPOD) technique. The parallel SPOD algorithm distributes the spatial dimension of the dataset preserving time. This approach is adopted to preserve the non-distributed fast Fourier transform of the data in time, thereby avoiding the associated bottlenecks. The parallel SPOD algorithm is implemented in the PySPOD (https://github.com/MathEXLab/PySPOD) library and makes use of the standard message passing interface (MPI) library, implemented in Python via mpi4py (https://mpi4py.readthedocs.io/en/stable/). An extensive performance evaluation of the parallel package is provided, including strong and weak scalability analyses. The open-source library allows the analysis of large datasets of interest across the scientific community. Here, we present applications in fluid dynamics and geophysics, that are extremely difficult (if not impossible) to achieve without a parallel algorithm. This work opens the path toward modal analyses of big quasi-stationary data, helping to uncover new unexplored spatio-temporal patterns.
{"title":"Unlocking massively parallel spectral proper orthogonal decompositions in the PySPOD package","authors":"Marcin Rogowski, Brandon C. Y. Yeung, Oliver T. Schmidt, Romit Maulik, Lisandro Dalcin, Matteo Parsani, Gianmarco Mengaldo","doi":"arxiv-2309.11808","DOIUrl":"https://doi.org/arxiv-2309.11808","url":null,"abstract":"We propose a parallel (distributed) version of the spectral proper orthogonal\u0000decomposition (SPOD) technique. The parallel SPOD algorithm distributes the\u0000spatial dimension of the dataset preserving time. This approach is adopted to\u0000preserve the non-distributed fast Fourier transform of the data in time,\u0000thereby avoiding the associated bottlenecks. The parallel SPOD algorithm is\u0000implemented in the PySPOD (https://github.com/MathEXLab/PySPOD) library and\u0000makes use of the standard message passing interface (MPI) library, implemented\u0000in Python via mpi4py (https://mpi4py.readthedocs.io/en/stable/). An extensive\u0000performance evaluation of the parallel package is provided, including strong\u0000and weak scalability analyses. The open-source library allows the analysis of\u0000large datasets of interest across the scientific community. Here, we present\u0000applications in fluid dynamics and geophysics, that are extremely difficult (if\u0000not impossible) to achieve without a parallel algorithm. This work opens the\u0000path toward modal analyses of big quasi-stationary data, helping to uncover new\u0000unexplored spatio-temporal patterns.","PeriodicalId":501256,"journal":{"name":"arXiv - CS - Mathematical Software","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138521162","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}
Satisfiability modulo theories (SMT) is a core tool in formal verification. While the SMT-LIB specification language can be used to interact with theorem proving software, a high-level interface allows for faster and easier specifications of complex SMT formulae. In this paper we discuss the design and implementation of a novel publicly-available interface for interacting with SMT-LIB compliant solvers in the Julia programming language.
{"title":"Satisfiability.jl: Satisfiability Modulo Theories in Julia","authors":"Emiko Soroka, Mykel J. Kochenderfer, Sanjay Lall","doi":"arxiv-2309.08778","DOIUrl":"https://doi.org/arxiv-2309.08778","url":null,"abstract":"Satisfiability modulo theories (SMT) is a core tool in formal verification.\u0000While the SMT-LIB specification language can be used to interact with theorem\u0000proving software, a high-level interface allows for faster and easier\u0000specifications of complex SMT formulae. In this paper we discuss the design and\u0000implementation of a novel publicly-available interface for interacting with\u0000SMT-LIB compliant solvers in the Julia programming language.","PeriodicalId":501256,"journal":{"name":"arXiv - CS - Mathematical Software","volume":"12 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138521246","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}
The Chisholm rational approximant is a natural generalization to two variables of the well-known single variable Pad'e approximant, and has the advantage of reducing to the latter when one of the variables is set equals to 0. We present, to our knowledge, the first automated Mathematica package to evaluate diagonal Chisholm approximants of two variable series. For the moment, the package can only be used to evaluate diagonal approximants i.e. the maximum powers of both the variables, in both the numerator and the denominator, is equal to some integer $M$. We further modify the original method so as to allow us to evaluate the approximants around some general point $(x,y)$ not necessarily $(0,0)$. Using the approximants around general point $(x,y)$, allows us to get a better estimate of the result when the point of evaluation is far from $(0,0)$. Several examples of the elementary functions have been studied which shows that the approximants can be useful for analytic continuation and convergence acceleration purposes. We continue our study using various examples of two variable hypergeometric series, $mathrm{Li}_{2,2}(x,y)$ etc that arise in particle physics and in the study of critical phenomena in condensed matter physics. The demonstration of the package is discussed in detail and the Mathematica package is provided as an ancillary file.
{"title":"$texttt{ChisholmD.wl}$- Automated rational approximant for bi-variate series","authors":"Souvik Bera, Tanay Pathak","doi":"arxiv-2309.07687","DOIUrl":"https://doi.org/arxiv-2309.07687","url":null,"abstract":"The Chisholm rational approximant is a natural generalization to two\u0000variables of the well-known single variable Pad'e approximant, and has the\u0000advantage of reducing to the latter when one of the variables is set equals to\u00000. We present, to our knowledge, the first automated Mathematica package to\u0000evaluate diagonal Chisholm approximants of two variable series. For the moment,\u0000the package can only be used to evaluate diagonal approximants i.e. the maximum\u0000powers of both the variables, in both the numerator and the denominator, is\u0000equal to some integer $M$. We further modify the original method so as to allow\u0000us to evaluate the approximants around some general point $(x,y)$ not\u0000necessarily $(0,0)$. Using the approximants around general point $(x,y)$,\u0000allows us to get a better estimate of the result when the point of evaluation\u0000is far from $(0,0)$. Several examples of the elementary functions have been\u0000studied which shows that the approximants can be useful for analytic\u0000continuation and convergence acceleration purposes. We continue our study using\u0000various examples of two variable hypergeometric series,\u0000$mathrm{Li}_{2,2}(x,y)$ etc that arise in particle physics and in the study of\u0000critical phenomena in condensed matter physics. The demonstration of the\u0000package is discussed in detail and the Mathematica package is provided as an\u0000ancillary file.","PeriodicalId":501256,"journal":{"name":"arXiv - CS - Mathematical Software","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138521244","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}
Hao-Jun Michael Shi, Tsung-Hsien Lee, Shintaro Iwasaki, Jose Gallego-Posada, Zhijing Li, Kaushik Rangadurai, Dheevatsa Mudigere, Michael Rabbat
Shampoo is an online and stochastic optimization algorithm belonging to the AdaGrad family of methods for training neural networks. It constructs a block-diagonal preconditioner where each block consists of a coarse Kronecker product approximation to full-matrix AdaGrad for each parameter of the neural network. In this work, we provide a complete description of the algorithm as well as the performance optimizations that our implementation leverages to train deep networks at-scale in PyTorch. Our implementation enables fast multi-GPU distributed data-parallel training by distributing the memory and computation associated with blocks of each parameter via PyTorch's DTensor data structure and performing an AllGather primitive on the computed search directions at each iteration. This major performance enhancement enables us to achieve at most a 10% performance reduction in per-step wall-clock time compared against standard diagonal-scaling-based adaptive gradient methods. We validate our implementation by performing an ablation study on training ImageNet ResNet50, demonstrating Shampoo's superiority over standard training recipes with minimal hyperparameter tuning.
{"title":"A Distributed Data-Parallel PyTorch Implementation of the Distributed Shampoo Optimizer for Training Neural Networks At-Scale","authors":"Hao-Jun Michael Shi, Tsung-Hsien Lee, Shintaro Iwasaki, Jose Gallego-Posada, Zhijing Li, Kaushik Rangadurai, Dheevatsa Mudigere, Michael Rabbat","doi":"arxiv-2309.06497","DOIUrl":"https://doi.org/arxiv-2309.06497","url":null,"abstract":"Shampoo is an online and stochastic optimization algorithm belonging to the\u0000AdaGrad family of methods for training neural networks. It constructs a\u0000block-diagonal preconditioner where each block consists of a coarse Kronecker\u0000product approximation to full-matrix AdaGrad for each parameter of the neural\u0000network. In this work, we provide a complete description of the algorithm as\u0000well as the performance optimizations that our implementation leverages to\u0000train deep networks at-scale in PyTorch. Our implementation enables fast\u0000multi-GPU distributed data-parallel training by distributing the memory and\u0000computation associated with blocks of each parameter via PyTorch's DTensor data\u0000structure and performing an AllGather primitive on the computed search\u0000directions at each iteration. This major performance enhancement enables us to\u0000achieve at most a 10% performance reduction in per-step wall-clock time\u0000compared against standard diagonal-scaling-based adaptive gradient methods. We\u0000validate our implementation by performing an ablation study on training\u0000ImageNet ResNet50, demonstrating Shampoo's superiority over standard training\u0000recipes with minimal hyperparameter tuning.","PeriodicalId":501256,"journal":{"name":"arXiv - CS - Mathematical Software","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138521248","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}
Amr Elsharkawy, Xiao-Ting Michelle To, Philipp Seitz, Yanbin Chen, Yannick Stade, Manuel Geiger, Qunsheng Huang, Xiaorang Guo, Muhammad Arslan Ansari, Christian B. Mendl, Dieter Kranzlmüller, Martin Schulz
Quantum computing (QC) introduces a novel mode of computation with the possibility of greater computational power that remains to be exploited $unicode{x2013}$ presenting exciting opportunities for high performance computing (HPC) applications. However, recent advancements in the field have made clear that QC does not supplant conventional HPC, but can rather be incorporated into current heterogeneous HPC infrastructures as an additional accelerator, thereby enabling the optimal utilization of both paradigms. The desire for such integration significantly affects the development of software for quantum computers, which in turn influences the necessary software infrastructure. To date, previous review papers have investigated various quantum programming tools (QPTs) (such as languages, libraries, frameworks) in their ability to program, compile, and execute quantum circuits. However, the integration effort with classical HPC frameworks or systems has not been addressed. This study aims to characterize existing QPTs from an HPC perspective, investigating if existing QPTs have the potential to be efficiently integrated with classical computing models and determining where work is still required. This work structures a set of criteria into an analysis blueprint that enables HPC scientists to assess whether a QPT is suitable for the quantum-accelerated classical application at hand.
{"title":"Integration of Quantum Accelerators with High Performance Computing $unicode{x2013}$ A Review of Quantum Programming Tools","authors":"Amr Elsharkawy, Xiao-Ting Michelle To, Philipp Seitz, Yanbin Chen, Yannick Stade, Manuel Geiger, Qunsheng Huang, Xiaorang Guo, Muhammad Arslan Ansari, Christian B. Mendl, Dieter Kranzlmüller, Martin Schulz","doi":"arxiv-2309.06167","DOIUrl":"https://doi.org/arxiv-2309.06167","url":null,"abstract":"Quantum computing (QC) introduces a novel mode of computation with the\u0000possibility of greater computational power that remains to be exploited\u0000$unicode{x2013}$ presenting exciting opportunities for high performance\u0000computing (HPC) applications. However, recent advancements in the field have\u0000made clear that QC does not supplant conventional HPC, but can rather be\u0000incorporated into current heterogeneous HPC infrastructures as an additional\u0000accelerator, thereby enabling the optimal utilization of both paradigms. The\u0000desire for such integration significantly affects the development of software\u0000for quantum computers, which in turn influences the necessary software\u0000infrastructure. To date, previous review papers have investigated various\u0000quantum programming tools (QPTs) (such as languages, libraries, frameworks) in\u0000their ability to program, compile, and execute quantum circuits. However, the\u0000integration effort with classical HPC frameworks or systems has not been\u0000addressed. This study aims to characterize existing QPTs from an HPC\u0000perspective, investigating if existing QPTs have the potential to be\u0000efficiently integrated with classical computing models and determining where\u0000work is still required. This work structures a set of criteria into an analysis\u0000blueprint that enables HPC scientists to assess whether a QPT is suitable for\u0000the quantum-accelerated classical application at hand.","PeriodicalId":501256,"journal":{"name":"arXiv - CS - Mathematical Software","volume":"18 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138521079","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}
In this paper, we introduce CDL, a software library designed for the analysis of permutations and linear orders subject to various structural restrictions. Prominent examples of these restrictions include pattern avoidance, a topic of interest in both computer science and combinatorics, and "never conditions" utilized in social choice and voting theory. CDL offers a range of fundamental functionalities, including identifying the permutations that meet specific restrictions and determining the isomorphism of such sets. To facilitate exploration across extensive domains, CDL incorporates multiple search strategies and heuristics.
{"title":"CDL: A fast and flexible library for the study of permutation sets with structural restrictions","authors":"Bei Zhou, Klas Markstrōm, Søren Riis","doi":"arxiv-2309.06306","DOIUrl":"https://doi.org/arxiv-2309.06306","url":null,"abstract":"In this paper, we introduce CDL, a software library designed for the analysis\u0000of permutations and linear orders subject to various structural restrictions.\u0000Prominent examples of these restrictions include pattern avoidance, a topic of\u0000interest in both computer science and combinatorics, and \"never conditions\"\u0000utilized in social choice and voting theory. CDL offers a range of fundamental functionalities, including identifying the\u0000permutations that meet specific restrictions and determining the isomorphism of\u0000such sets. To facilitate exploration across extensive domains, CDL incorporates\u0000multiple search strategies and heuristics.","PeriodicalId":501256,"journal":{"name":"arXiv - CS - Mathematical Software","volume":"10 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138521253","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}
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