Research and Education in Computational Science and Engineering

Over the past two decades the field of computational science and engineering (CSE) has penetrated both basic and applied research in academia, industry, and laboratories to advance discovery, optimize systems, support decision-makers, and educate the scientific and engineering workforce. Informed by centuries of theory and experiment, CSE performs computational experiments to answer questions that neither theory nor experiment alone is equipped to answer. CSE provides scientists and engineers of all persuasions with algorithmic inventions and software systems that transcend disciplines and scales. Carried on a wave of digital technology, CSE brings the power of parallelism to bear on troves of data. Mathematics-based advanced computing has become a prevalent means of discovery and innovation in essentially all areas of science, engineering, technology, and society; and the CSE community is at the core of this transformation. However, a combination of disruptive developments---including the architectural complexity of extreme-scale computing, the data revolution that engulfs the planet, and the specialization required to follow the applications to new frontiers---is redefining the scope and reach of the CSE endeavor. This report describes the rapid expansion of CSE and the challenges to sustaining its bold advances. The report also presents strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie

Status and Future Perspectives for Lattice Gauge Theory Calculations to the Exascale and Beyond

In this and a set of companion whitepapers, the USQCD Collaboration lays out a program of science and computing for lattice gauge theory. These whitepapers describe how calculation using lattice QCD (and other gauge theories) can aid the interpretation of ongoing and upcoming experiments in particle and nuclear physics, as well as inspire new ones.Comment: 44 pages. 1 of USQCD whitepapers

Machine Learning for Fluid Mechanics

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from field measurements, experiments and large-scale simulations at multiple spatiotemporal scales. Machine learning offers a wealth of techniques to extract information from data that could be translated into knowledge about the underlying fluid mechanics. Moreover, machine learning algorithms can augment domain knowledge and automate tasks related to flow control and optimization. This article presents an overview of past history, current developments, and emerging opportunities of machine learning for fluid mechanics. It outlines fundamental machine learning methodologies and discusses their uses for understanding, modeling, optimizing, and controlling fluid flows. The strengths and limitations of these methods are addressed from the perspective of scientific inquiry that considers data as an inherent part of modeling, experimentation, and simulation. Machine learning provides a powerful information processing framework that can enrich, and possibly even transform, current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202

Computational Physics on Graphics Processing Units

The use of graphics processing units for scientific computations is an emerging strategy that can significantly speed up various different algorithms. In this review, we discuss advances made in the field of computational physics, focusing on classical molecular dynamics, and on quantum simulations for electronic structure calculations using the density functional theory, wave function techniques, and quantum field theory.Comment: Proceedings of the 11th International Conference, PARA 2012, Helsinki, Finland, June 10-13, 201

Cooperative Path-Planning for Multi-Vehicle Systems

In this paper, we propose a collision avoidance algorithm for multi-vehicle systems, which is a common problem in many areas, including navigation and robotics. In dynamic environments, vehicles may become involved in potential collisions with each other, particularly when the vehicle density is high and the direction of travel is unrestricted. Cooperatively planning vehicle movement can effectively reduce and fairly distribute the detour inconvenience before subsequently returning vehicles to their intended paths. We present a novel method of cooperative path planning for multi-vehicle systems based on reinforcement learning to address this problem as a decision process. A dynamic system is described as a multi-dimensional space formed by vectors as states to represent all participating vehicles’ position and orientation, whilst considering the kinematic constraints of the vehicles. Actions are defined for the system to transit from one state to another. In order to select appropriate actions whilst satisfying the constraints of path smoothness, constant speed and complying with a minimum distance between vehicles, an approximate value function is iteratively developed to indicate the desirability of every state-action pair from the continuous state space and action space. The proposed scheme comprises two phases. The convergence of the value function takes place in the former learning phase, and it is then used as a path planning guideline in the subsequent action phase. This paper summarizes the concept and methodologies used to implement this online cooperative collision avoidance algorithm and presents results and analysis regarding how this cooperative scheme improves upon two baseline schemes where vehicles make movement decisions independently

병렬 및 분산 임베디드 시스템을 위한 모델 기반 코드 생성 프레임워크

학위논문(박사)--서울대학교 대학원 :공과대학 컴퓨터공학부,2020. 2. 하순회.소프트웨어 설계 생산성 및 유지보수성을 향상시키기 위해 다양한 소프트웨어 개발 방법론이 제안되었지만, 대부분의 연구는 응용 소프트웨어를 하나의 프로세서에서 동작시키는 데에 초점을 맞추고 있다. 또한, 임베디드 시스템을 개발하는 데에 필요한 지연이나 자원 요구 사항에 대한 비기능적 요구 사항을 고려하지 않고 있기 때문에 일반적인 소프트웨어 개발 방법론을 임베디드 소프트웨어를 개발하는 데에 적용하는 것은 적합하지 않다. 이 논문에서는 병렬 및 분산 임베디드 시스템을 대상으로 하는 소프트웨어를 모델로 표현하고, 이를 소프트웨어 분석이나 개발에 활용하는 개발 방법론을 소개한다. 우리의 모델에서 응용 소프트웨어는 계층적으로 표현할 수 있는 여러 개의 태스크로 이루어져 있으며, 하드웨어 플랫폼과 독립적으로 명세한다. 태스크 간의 통신 및 동기화는 모델이 정의한 규약이 정해져 있고, 이러한 규약을 통해 실제 프로그램을 실행하기 전에 소프트웨어 에러를 정적 분석을 통해 확인할 수 있고, 이는 응용의 검증 복잡도를 줄이는 데에 기여한다. 지정한 하드웨어 플랫폼에서 동작하는 프로그램은 태스크들을 프로세서에 매핑한 이후에 자동적으로 합성할 수 있다. 위의 모델 기반 소프트웨어 개발 방법론에서 사용하는 프로그램 합성기를 본 논문에서 제안하였는데, 명세한 플랫폼 요구 사항을 바탕으로 병렬 및 분산 임베디드 시스템을에서 동작하는 코드를 생성한다. 여러 개의 정형적 모델들을 계층적으로 표현하여 응용의 동적 행태를 나타고, 합성기는 여러 모델로 구성된 계층적인 모델로부터 병렬성을 고려하여 태스크를 실행할 수 있다. 또한, 프로그램 합성기에서 다양한 플랫폼이나 네트워크를 지원할 수 있도록 코드를 관리하는 방법도 보여주고 있다. 본 논문에서 제시하는 소프트웨어 개발 방법론은 6개의 하드웨어 플랫폼과 3 종류의 네트워크로 구성되어 있는 실제 감시 소프트웨어 시스템 응용 예제와 이종 멀티 프로세서를 활용하는 원격 딥 러닝 예제를 수행하여 개발 방법론의 적용 가능성을 시험하였다. 또한, 프로그램 합성기가 새로운 플랫폼이나 네트워크를 지원하기 위해 필요로 하는 개발 비용도 실제 측정 및 예측하여 상대적으로 적은 노력으로 새로운 플랫폼을 지원할 수 있음을 확인하였다. 많은 임베디드 시스템에서 예상치 못한 하드웨어 에러에 대해 결함을 감내하는 것을 필요로 하기 때문에 결함 감내에 대한 코드를 자동으로 생성하는 연구도 진행하였다. 본 기법에서 결함 감내 설정에 따라 태스크 그래프를 수정하는 방식을 활용하였으며, 결함 감내의 비기능적 요구 사항을 응용 개발자가 쉽게 적용할 수 있도록 하였다. 또한, 결함 감내 지원하는 것과 관련하여 실제 수동으로 구현했을 경우와 비교하였고, 결함 주입 도구를 이용하여 결함 발생 시나리오를 재현하거나, 임의로 결함을 주입하는 실험을 수행하였다. 마지막으로 결함 감내를 실험할 때에 활용한 결함 주입 도구는 본 논문의 또 다른 기여 사항 중 하나로 리눅스 환경으로 대상으로 응용 영역 및 커널 영역에 결함을 주입하는 도구를 개발하였다. 시스템의 견고성을 검증하기 위해 결함을 주입하여 결함 시나리오를 재현하는 것은 널리 사용되는 방법으로, 본 논문에서 개발된 결함 주입 도구는 시스템이 동작하는 도중에 재현 가능한 결함을 주입할 수 있는 도구이다. 커널 영역에서의 결함 주입을 위해 두 종류의 결함 주입 방법을 제공하며, 하나는 커널 GNU 디버거를 이용한 방법이고, 다른 하나는 ARM 하드웨어 브레이크포인트를 활용한 방법이다. 응용 영역에서 결함을 주입하기 위해 GDB 기반 결함 주입 방법을 이용하여 동일 시스템 혹은 원격 시스템의 응용에 결함을 주입할 수 있다. 결함 주입 도구에 대한 실험은 ODROID-XU4 보드에서 진행하였다.While various software development methodologies have been proposed to increase the design productivity and maintainability of software, they usually focus on the development of application software running on a single processing element, without concern about the non-functional requirements of an embedded system such as latency and resource requirements. In this thesis, we present a model-based software development method for parallel and distributed embedded systems. An application is specified as a set of tasks that follow a set of given rules for communication and synchronization in a hierarchical fashion, independently of the hardware platform. Having such rules enables us to perform static analysis to check some software errors at compile time to reduce the verification difficulty. Platform-specific program is synthesized automatically after mapping of tasks onto processing elements is determined. The program synthesizer is also proposed to generate codes which satisfies platform requirements for parallel and distributed embedded systems. As multiple models which can express dynamic behaviors can be depicted hierarchically, the synthesizer supports to manage multiple task graphs with a different hierarchy to run tasks with parallelism. Also, the synthesizer shows methods of managing codes for heterogeneous platforms and generating various communication methods. The viability of the proposed software development method is verified with a real-life surveillance application that runs on six processing elements with three remote communication methods, and remote deep learning example is conducted to use heterogeneous multiprocessing components on distributed systems. Also, supporting a new platform and network requires a small effort by measuring and estimating development costs. Since tolerance to unexpected errors is a required feature of many embedded systems, we also support an automatic fault-tolerant code generation. Fault tolerance can be applied by modifying the task graph based on the selected fault tolerance configurations, so the non-functional requirement of fault tolerance can be easily adopted by an application developer. To compare the effort of supporting fault tolerance, manual implementation of fault tolerance is performed. Also, the fault tolerance method is tested with the fault injection tool to emulate fault scenarios and inject faults randomly. Our fault injection tool, which has used for testing our fault-tolerance method, is another work of this thesis. Emulating fault scenarios by intentionally injecting faults is commonly used to test and verify the robustness of a system. To emulate faults on an embedded system, we present a run-time fault injection framework that can inject a fault on both a kernel and application layer of Linux-based systems. For injecting faults on a kernel layer, two complementary fault injection techniques are used. One is based on Kernel GNU Debugger, and the other is using a hardware breakpoint supported by the ARM architecture. For application-level fault injection, the GDB-based fault injection method is used to inject a fault on a remote application. The viability of the proposed fault injection tool is proved by real-life experiments with an ODROID-XU4 system.Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Contribution 6 1.3 Dissertation Organization 8 Chapter 2 Background 9 2.1 HOPES: Hope of Parallel Embedded Software 9 2.1.1 Software Development Procedure 9 2.1.2 Components of HOPES 12 2.2 Universal Execution Model 13 2.2.1 Task Graph Specification 13 2.2.2 Dataflow specification of an Application 15 2.2.3 Task Code Specification and Generic APIs 21 2.2.4 Meta-data Specification 23 Chapter 3 Program Synthesis for Parallel and Distributed Embedded Systems 24 3.1 Motivational Example 24 3.2 Program Synthesis Overview 26 3.3 Program Synthesis from Hierarchically-mixed Models 30 3.4 Platform Code Synthesis 33 3.5 Communication Code Synthesis 36 3.6 Experiments 40 3.6.1 Development Cost of Supporting New Platforms and Networks 40 3.6.2 Program Synthesis for the Surveillance System Example 44 3.6.3 Remote GPU-accelerated Deep Learning Example 46 3.7 Document Generation 48 3.8 Related Works 49 Chapter 4 Model Transformation for Fault-tolerant Code Synthesis 56 4.1 Fault-tolerant Code Synthesis Techniques 56 4.2 Applying Fault Tolerance Techniques in HOPES 61 4.3 Experiments 62 4.3.1 Development Cost of Applying Fault Tolerance 62 4.3.2 Fault Tolerance Experiments 62 4.4 Random Fault Injection Experiments 65 4.5 Related Works 68 Chapter 5 Fault Injection Framework for Linux-based Embedded Systems 70 5.1 Background 70 5.1.1 Fault Injection Techniques 70 5.1.2 Kernel GNU Debugger 71 5.1.3 ARM Hardware Breakpoint 72 5.2 Fault Injection Framework 74 5.2.1 Overview 74 5.2.2 Architecture 75 5.2.3 Fault Injection Techniques 79 5.2.4 Implementation 83 5.3 Experiments 90 5.3.1 Experiment Setup 90 5.3.2 Performance Comparison of Two Fault Injection Methods 90 5.3.3 Bit-flip Fault Experiments 92 5.3.4 eMMC Controller Fault Experiments 94 Chapter 6 Conclusion 97 Bibliography 99 요 약 108Docto

Hierarchical Control of the ATLAS Experiment

Control systems at High Energy Physics (HEP) experiments are becoming increasingly complex mainly due to the size, complexity and data volume associated to the front-end instrumentation. In particular, this becomes visible for the ATLAS experiment at the LHC accelerator at CERN. ATLAS will be the largest particle detector ever built, result of an international collaboration of more than 150 institutes. The experiment is composed of 9 different specialized sub-detectors that perform different tasks and have different requirements for operation. The system in charge of the safe and coherent operation of the whole experiment is called Detector Control System (DCS). This thesis presents the integration of the ATLAS DCS into a global control tree following the natural segmentation of the experiment into sub-detectors and smaller sub-systems. The integration of the many different systems composing the DCS includes issues such as: back-end organization, process model identification, fault detection, synchronization with external systems, automation of processes and supervisory control. Distributed control modeling is applied to the widely distributed devices that coexist in ATLAS. Thus, control is achieved by means of many distributed, autonomous and co-operative entities that are hierarchically organized and follow a finite-state machine logic. The key to integration of these systems lies in the so called Finite State Machine tool (FSM), which is based on two main enabling technologies: a SCADA product, and the State Manager Interface (SMI++) toolkit. The SMI++ toolkit has been already used with success in two previous HEP experiments providing functionality such as: an object-oriented language, a finite-state machine logic, an interface to develop expert systems, and a platform-independent communication protocol. This functionality is then used at all levels of the experiment operation process, ranging from the overall supervision down to device integration, enabling the overall sequencing and automation of the experiment. Although the experience gained in the past is an important input for the design of the detector's control hierarchy, further requirements arose due to the complexity and size of ATLAS. In total, around 200.000 channels will be supervised by the DCS and the final control tree will be hundreds of times bigger than any of the antecedents. Thus, in order to apply a hierarchical control model to the ATLAS DCS, a common approach has been proposed to ensure homogeneity between the large-scale distributed software ensembles of sub-detectors. A standard architecture and a human interface have been defined with emphasis on the early detection, monitoring and diagnosis of faults based on a dynamic fault-data mechanism. This mechanism relies on two parallel communication paths that manage the faults while providing a clear description of the detector conditions. The DCS information is split and handled by different types of SMI++ objects; whilst one path of objects manages the operational mode of the system, the other is to handle eventual faults. The proposed strategy has been validated through many different tests with positive results in both functionality and performance. This strategy has been successfully implemented and constitutes the ATLAS standard to build the global control tree. During the operation of the experiment, the DCS, responsible for the detector operation, must be synchronized with the data acquisition system which is in charge of the physics data taking process. The interaction between both systems has so far been limited, but becomes increasingly important as the detector nears completion. A prototype implementation, ready to be used during the sub-detector integration, has achieved data reconciliation by mapping the different segments of the data acquisition system into the DCS control tree. The adopted solution allows the data acquisition control applications to command different DCS sections independently and prevents incorrect physics data taking caused by a failure in a detector part. Finally, the human-machine interface presents and controls the DCS data in the ATLAS control room. The main challenges faced during the design and development phases were: how to support the operator in controlling this large system, how to maintain integration across many displays, and how to provide an effective navigation. These issues have been solved by combining the functionalities provided by both, the SCADA product and the FSM tool. The control hierarchy provides an intuitive structure for the organization of many different displays that are needed for the visualization of the experiment conditions. Each node in the tree represents a workspace that contains the functional information associated with its abstraction level within the hierarchy. By means of an effective navigation, any workspace of the control tree is accessible by the operator or detector expert within a common human interface layout. The interface is modular and flexible enough to be accommodated to new operational scenarios, fulfil the necessities of the different kind of users and facilitate the maintenance during the long lifetime of the detector of up to 20 years. The interface is in use since several months, and the sub-detector's control hierarchies, together with their associated displays, are currently being integrated into the common human-machine interface