Automatic program generation for scientific computing

The code reuse problem is a common software engineering problem in scientific computing. As a prevailing programming language in many scientific fields, Fortran does not provide support to address this problem. One particular reason is that Fortran lacks the support for generic programming. By applying program-generation techniques, we developed two approaches to address the code reuse problem. The first approach is to design a program generator for the equation-based specification of subroutines that can be generic in the dimensions of arrays, parameter lists, and called subroutines. We apply that approach to a real-world problem in scientific computing, which requires the generic description of inverse ocean modeling tools. In addition to a compiler that can transform generic specifications into efficient Fortran code for models, we have also developed a type system that can identify possible errors already in the specifications. The second approach is to extend Fortran with the support for generic programming. The result is the language Parametric Fortran, which supports defining Fortran program templates by allowing the parameterization of arbitrary Fortran constructs. A Fortran program template can be translated into a regular Fortran program guided by values for the parameters. Parametric Fortran is particularly useful in scientific computing. The applications include defining generic functions, removing duplicated code, and automatic differentiation. The described Fortran extension has also been successfully employed implementing the generic inverse ocean modeling system

A survey of free software for the design, analysis, modelling, and simulation of an unmanned aerial vehicle

The objective of this paper is to analyze free software for the design, analysis, modelling, and simulation of an unmanned aerial vehicle (UAV). Free software is the best choice when the reduction of production costs is necessary; nevertheless, the quality of free software may vary. This paper probably does not include all of the free software, but tries to describe or mention at least the most interesting programs. The first part of this paper summarizes the essential knowledge about UAVs, including the fundamentals of flight mechanics and aerodynamics, and the structure of a UAV system. The second section generally explains the modelling and simulation of a UAV. In the main section, more than 50 free programs for the design, analysis, modelling, and simulation of a UAV are described. Although the selection of the free software has been focused on small subsonic UAVs, the software can also be used for other categories of aircraft in some cases; e.g. for MAVs and large gliders. The applications with an historical importance are also included. Finally, the results of the analysis are evaluated and discussed—a block diagram of the free software is presented, possible connections between the programs are outlined, and future improvements of the free software are suggested. © 2015, CIMNE, Barcelona, Spain.Internal Grant Agency of Tomas Bata University in Zlin [IGA/FAI/2015/001, IGA/FAI/2014/006

A Survey on Compiler Autotuning using Machine Learning

Since the mid-1990s, researchers have been trying to use machine-learning based approaches to solve a number of different compiler optimization problems. These techniques primarily enhance the quality of the obtained results and, more importantly, make it feasible to tackle two main compiler optimization problems: optimization selection (choosing which optimizations to apply) and phase-ordering (choosing the order of applying optimizations). The compiler optimization space continues to grow due to the advancement of applications, increasing number of compiler optimizations, and new target architectures. Generic optimization passes in compilers cannot fully leverage newly introduced optimizations and, therefore, cannot keep up with the pace of increasing options. This survey summarizes and classifies the recent advances in using machine learning for the compiler optimization field, particularly on the two major problems of (1) selecting the best optimizations and (2) the phase-ordering of optimizations. The survey highlights the approaches taken so far, the obtained results, the fine-grain classification among different approaches and finally, the influential papers of the field.Comment: version 5.0 (updated on September 2018)- Preprint Version For our Accepted Journal @ ACM CSUR 2018 (42 pages) - This survey will be updated quarterly here (Send me your new published papers to be added in the subsequent version) History: Received November 2016; Revised August 2017; Revised February 2018; Accepted March 2018

AutoBayes: A System for Generating Data Analysis Programs from Statistical Models

Data analysis is an important scientific task which is required whenever information needs to be extracted from raw data. Statistical approaches to data analysis, which use methods from probability theory and numerical analysis, are well-founded but difficult to implement: the development of a statistical data analysis program for any given application is time-consuming and requires substantial knowledge and experience in several areas. In this paper, we describe AutoBayes, a program synthesis system for the generation of data analysis programs from statistical models. A statistical model specifies the properties for each problem variable (i.e., observation or parameter) and its dependencies in the form of a probability distribution. It is a fully declarative problem description, similar in spirit to a set of differential equations. From such a model, AutoBayes generates optimized and fully commented C/C++ code which can be linked dynamically into the Matlab and Octave environments. Code is produced by a schema-guided deductive synthesis process. A schema consists of a code template and applicability constraints which are checked against the model during synthesis using theorem proving technology. AutoBayes augments schema-guided synthesis by symbolic-algebraic computation and can thus derive closed-form solutions for many problems. It is well-suited for tasks like estimating best-fitting model parameters for the given data. Here, we describe AutoBayes's system architecture, in particular the schema-guided synthesis kernel. Its capabilities are illustrated by a number of advanced textbook examples and benchmarks

Proceedings of the Thirteenth Annual Software Engineering Workshop

Topics covered in the workshop included studies and experiments conducted in the Software Engineering Laboratory (SEL), a cooperative effort of NASA Goddard Space Flight Center, the University of Maryland, and Computer Sciences Corporation; software models; software products; and software tools

클라우드 컴퓨팅 환경기반에서 수치 모델링과 머신러닝을 통한 지구과학 자료생성에 관한 연구

학위논문(박사) -- 서울대학교대학원 : 자연과학대학 지구환경과학부, 2022. 8. 조양기.To investigate changes and phenomena on Earth, many scientists use high-resolution-model results based on numerical models or develop and utilize machine learning-based prediction models with observed data. As information technology advances, there is a need for a practical methodology for generating local and global high-resolution numerical modeling and machine learning-based earth science data. This study recommends data generation and processing using high-resolution numerical models of earth science and machine learning-based prediction models in a cloud environment. To verify the reproducibility and portability of high-resolution numerical ocean model implementation on cloud computing, I simulated and analyzed the performance of a numerical ocean model at various resolutions in the model domain, including the Northwest Pacific Ocean, the East Sea, and the Yellow Sea. With the containerization method, it was possible to respond to changes in various infrastructure environments and achieve computational reproducibility effectively. The data augmentation of subsurface temperature data was performed using generative models to prepare large datasets for model training to predict the vertical temperature distribution in the ocean. To train the prediction model, data augmentation was performed using a generative model for observed data that is relatively insufficient compared to satellite dataset. In addition to observation data, HYCOM datasets were used for performance comparison, and the data distribution of augmented data was similar to the input data distribution. The ensemble method, which combines stand-alone predictive models, improved the performance of the predictive model compared to that of the model based on the existing observed data. Large amounts of computational resources were required for data synthesis, and the synthesis was performed in a cloud-based graphics processing unit environment. High-resolution numerical ocean model simulation, predictive model development, and the data generation method can improve predictive capabilities in the field of ocean science. The numerical modeling and generative models based on cloud computing used in this study can be broadly applied to various fields of earth science.지구의 변화와 현상을 연구하기 위해 많은 과학자들은 수치 모델을 기반으로 한 고해상도 모델 결과를 사용하거나 관측된 데이터로 머신러닝 기반 예측 모델을 개발하고 활용한다. 정보기술이 발전함에 따라 지역 및 전 지구적인 고해상도 수치 모델링과 머신러닝 기반 지구과학 데이터 생성을 위한 실용적인 방법론이 필요하다. 본 연구는 지구과학의 고해상도 수치 모델과 머신러닝 기반 예측 모델을 기반으로 한 데이터 생성 및 처리가 클라우드 환경에서 효과적으로 구현될 수 있음을 제안한다. 클라우드 컴퓨팅에서 고해상도 수치 해양 모델 구현의 재현성과 이식성을 검증하기 위해 북서태평양, 동해, 황해 등 모델 영역의 다양한 해상도에서 수치 해양 모델의 성능을 시뮬레이션하고 분석하였다. 컨테이너화 방식을 통해 다양한 인프라 환경 변화에 대응하고 계산 재현성을 효과적으로 확보할 수 있었다. 머신러닝 기반 데이터 생성의 적용을 검증하기 위해 생성 모델을 이용한 표층 이하 온도 데이터의 데이터 증강을 실행하여 해양의 수직 온도 분포를 예측하는 모델 훈련을 위한 대용량 데이터 세트를 준비했다. 예측모델 훈련을 위해 위성 데이터에 비해 상대적으로 부족한 관측 데이터에 대해서 생성 모델을 사용하여 데이터 증강을 수행하였다. 모델의 예측성능 비교에는 관측 데이터 외에도 HYCOM 데이터 세트를 사용하였으며, 증강 데이터의 데이터 분포는 입력 데이터 분포와 유사함을 확인하였다. 독립형 예측 모델을 결합한 앙상블 방식은 기존 관측 데이터를 기반으로 하는 예측 모델의 성능에 비해 향상되었다. 데이터합성을 위해 많은 양의 계산 자원이 필요했으며, 데이터 합성은 클라우드 기반 GPU 환경에서 수행되었다. 고해상도 수치 해양 모델 시뮬레이션, 예측 모델 개발, 데이터 생성 방법은 해양 과학 분야에서 예측 능력을 향상시킬 수 있다. 본 연구에서 사용된 클라우드 컴퓨팅 기반의 수치 모델링 및 생성 모델은 지구 과학의 다양한 분야에 광범위하게 적용될 수 있다.1. General Introduction 1 2. Performance of numerical ocean modeling on cloud computing 6 2.1. Introduction 6 2.2. Cloud Computing 9 2.2.1. Cloud computing overview 9 2.2.2. Commercial cloud computing services 12 2.3. Numerical model for performance analysis of commercial clouds 15 2.3.1. High Performance Linpack Benchmark 15 2.3.2. Benchmark Sustainable Memory Bandwidth and Memory Latency 16 2.3.3. Numerical Ocean Model 16 2.3.4. Deployment of Numerical Ocean Model and Benchmark Packages on Cloud Clusters 19 2.4. Simulation results 21 2.4.1. Benchmark simulation 21 2.4.2. Ocean model simulation 24 2.5. Analysis of ROMS performance on commercial clouds 26 2.5.1. Performance of ROMS according to H/W resources 26 2.5.2. Performance of ROMS according to grid size 34 2.6. Summary 41 3. Reproducibility of numerical ocean model on the cloud computing 44 3.1. Introduction 44 3.2. Containerization of numerical ocean model 47 3.2.1. Container virtualization 47 3.2.2. Container-based architecture for HPC 49 3.2.3. Container-based architecture for hybrid cloud 53 3.3. Materials and Methods 55 3.3.1. Comparison of traditional and container based HPC cluster workflows 55 3.3.2. Model domain and datasets for numerical simulation 57 3.3.3. Building the container image and registration in the repository 59 3.3.4. Configuring a numeric model execution cluster 64 3.4. Results and Discussion 74 3.4.1. Reproducibility 74 3.4.2. Portability and Performance 76 3.5. Conclusions 81 4. Generative models for the prediction of ocean temperature profile 84 4.1. Introduction 84 4.2. Materials and Methods 87 4.2.1. Model domain and datasets for predicting the subsurface temperature 87 4.2.2. Model architecture for predicting the subsurface temperature 90 4.2.3. Neural network generative models 91 4.2.4. Prediction Models 97 4.2.5. Accuracy 103 4.3. Results and Discussion 104 4.3.1. Data Generation 104 4.3.2. Ensemble Prediction 109 4.3.3. Limitations of this study and future works 111 4.4. Conclusion 111 5. Summary and conclusion 114 6. References 118 7. Abstract (in Korean) 140박