어플리케이션 다운로딩 시스템을 위한 클라이언트 선행 컴파일러

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2014. 8. 문수묵.어플리케이션(앱)을 다운로드 받아서 수행하는 시스템은 DTV나 스마트폰처럼 대중적으로 사용되고 있다. 앱을 다운받아서 사용하는 시스템들은 가상 머신을 주류로 사용하고 있다. 가상 머신의 가장 큰 문제점은 인터프리터를 통한 수행에 의한 느린 성능이며, 이 성능의 향상을 위해 주로 사용되는 기술이 적시 컴파일러이다. 적시 컴파일러는 다운받은 앱의 수행 중에 동적으로 머신 코드로 번역하여 사용하는 기법으로, 동적 컴파일레이션 오버헤드를 가지게 된다. 우리는 이 동적 컴파일레이션 오버헤드를 제거하여 성능을 향상시키는 클라이언트 선행 컴파일러를 제안하였다. 클라이언트 선행 컴파일러는 적시 컴파일러가 생성하는 머신 코드를 앱의 종료될 때 지우지 않고 파일형태로 스토리지에 저장하여 이후에 앱이 다시 수행될 때 저장한 머신 코드를 재활용하여 사용함으로써 런타임 컴파일레이션 오버헤드를 제거하게 된다. 저장한 머신 코드를 재활용할 때 머신 코드에 인코딩된 주소값들은 유효하지 않기 때문에 가상 머신의 현재 값에 맞추어 변경해주는 작업이 필요하다. 이 작업은 주소 재배치이다. 주소 재배치는 저장된 머신 코드만으로 수행할 수가 없기 때문에 추가적인 정보를 머신 코드를 저장하는 과정에서 생성하여 파일에 함께 저장해 주어야 한다. 자바의 상수 풀 해석은 주소 재배치 작업을 어렵게 한다. 우리는 이에 대한 해결책을 만들었다. 주소 재배치를 위한 정보들을 저장하기위해 영구 메모를 많이 사용하는 것도 문제가 된다. 따라서 우리는 주소 재배치 정보를 머신 코드 상에 인코딩하고 압축하여 저장하는 방법을 제안했다. 우리의 클라이언트 선행 컴파일러 기법은 오라클사의 CDC 가상머신 참조구현인 CVM에 구현하였다. 우리의 클라이언트 선행 컴파일러는 벤치마크의 성능을 약 12% 향상시켰다. 또한 우리는 클라이언트 선행 컴파일러 기법을 실제로 판매하는 DTV환경에 구축하여 실제 방송국이 사용하는 어플리케이션을 수행해 보았다. 우리의 클라이언트 선행 컴파일러 방식은 사용자의 실제 환경에서 33%의 좋은 성능 향상을 얻었다. 자바스크립트 가상머신인 구글사의 V8 가상머신은 인터프리터 수행없이 적시 컴파일러만을 사용하고 있다. 우리는 V8 가상 머신에 클라이언트 선행 컴파일러는 적용하였지만, 실제 성능 향상을 얻어내지는 못했다. 이것은 V8 가상 머신의 특징인 내부 객체의 적극적인 사용에 의한 결과이다. 내부 객체는 컴파일러가 생성하여 컴파일러 과정에서 사용되며, 자바스크립트 프로그램에서도 접근하여 사용하게 된다. V8 가상 머신의 컴포넌트들은 대부분 내부 객체로 생성되어, 다른 종류의 가상 머신에 비해서 상당히 많은 내부 객체를 생성하고 있다. V8의 적시 컴파일러가 생성하는 머신 코드에서는 이 내부 객체를 직접 접근하여 사용하게 되어, 클라이언트 선행 컴파일러에 의해 어플리케이션이 수행될 때마다 이 내부 객체는 항상 필요하기 때문에 클라이언트 선행 컴파일러는 내부 객체를 재생성해야만 한다. V8 적시 컴파일러의 런타임 컴파일레이션 오버헤드의 대부분이 내부 객체를 생성하는 오버헤드이기 때문에, 우리의 클라이언트 선행 컴파일러는 이 환경에서 충분한 성능 향상을 얻을 수 없었다.App-downloading systems like DTV and smart phone are popularly used. Virtual machine is mainstream for those systems. One critical problem of app-downloading systems is performance because app is executed by interpreter. A popular solution for improving performance is Just-In-Time Compiler (JITC). JITC compiles to machine code at runtime. So, JITC suffers from runtime compilation overhead. We suggested client Ahead-Of-Time Compiler(c-AOTC) which improves the performance by removing runtime compilation overhead. c-AOTC saves machine code of method generated by JITC in persistent storage and reuses it in next runs. The machine code of a method translated by JITC is cached on a persistent memory of the device, and when the method is invoked again in a later run of the program, the machine code is loaded and executed directly without any translation overhead. One major issue in c-AOTC is relocation because some of the address constants embedded in the cached machine code are not correct when the machine code is loaded and used in a different runthose addresses should be corrected before they are used. Constant pool resolution complicates the relocation problem, and we propose our solutions. The persistent memory overhead for saving the relocation information is also an issue, and we propose a technique to encode the relocation information and compress the machine code efficiently. We developed a c-AOTC on Oracles CDC VM, and evaluation results indicate that c-AOTC can improve the performance as much as an average of 12% for benchmarks. And we adopted c-AOTC approach to commercial DTV platform and test the real xlet applications of commercial broadcasting stations. c-AOTC got average 33% performance improvement on the real xlet application test. V8 JavaScript VM does not use interpreter. Apps are executed only by JITC. We adopted c-AOTC to V8 VM. But we cannot get any good performance result because of V8 VMs characteristics. V8 VM components are generated as internal objects. Internal objects are used for compiling and running of JavaScript program. The machine code of V8 VM addresses internal objects which are different for each run. Because internal objects be　accessed in each run, c-AOTC　must recreate those objects. Because most of compilation overhead of V8 VM is internal object creation overhead, c-AOTC　does not get enough improvements.Chapter 1 Introduction 1 Chatper 2 client-AOTC Approach 4 Chatper 3 Java Virtual Machine and Our JITC 9 3.1 Overview of JVM and the Bytecode 9 3.2 Our JITC on the CVM 14 Chatper 4 Design and Implementation of c-AOTC on JVM 16 4.1 Architecutre of the c-AOTC 16 4.2 Relocation 19 4.2.1 Translated Code Which Needs Relocation 19 4.2.2 Relocation Information and Relocation Process 22 4.2.3 Relocation for Inlined Methods 24 4.3 Reducing the Size of .aotc Files 25 4.3.1 Encoding Relocation Information 25 4.3.2 Machine Code Compression 27 4.3.3 Structure of the .aotc File 27 Chatper 5 c-AOTC for DTV JVM platform 29 5.1 DTV software platform 30 5.2 c-AOTC on the DTV 32 5.2.1 Design of c-AOTC on DTV 32 5.2.2 Relocation Problem 35 5.2.3 Example of Relocation 39 5.2.3.1 Relocation Example of JVM c-AOTC 39 5.2.3.3 Relocation Example of DTV c-AOTC 41 Chatper 6 c-AOTC for JavaScript VM 44 6.1 V8 JavaScript VM 44 6.2 Issue and Solution of c-AOTC on V8 JavaScript VM 46 Chatper 7 Experimental Results 51 7.1 Experimental Environment of JVM 51 7.2 Performance Impact of c-AOTC 53 7.3 Space Overhead of c-AOTC 55 7.4 Reducing Number of c-AOTC Methods 60 7.5 c-AOTC with new hot-spot detection heuristics 63 7.5.1 Performance Impact of c-AOTC with new hot-spotdetection heuristics 63 7.5.2 Space Overhead of c-AOTC with new hot-spot detection heuristics 67 7.6 c-AOTC of DTV JVM platform 70 7.6.1 Performance result of DTV platform 70 7.6.2 Analysis of JITCed method of DTV platform 72 7.6.3 Space overhead of DTV platform 74 7.6.4 c-AOTC overhead of DTV platform 75 7.6.5 c-AOTC performance using different xlets c-AOTC file in DTV platform 76 7.7 c-AOTC of V8 JavaScript engine 79 7.7.1 Compilation overhead on V8 JavaScript VM 79 7.7.2 Performance result on V8 JavaScript VM 81 7.7.3 Comparison with c-AOTC of JavaScriptCore VM 83 Chatper 8 Related Work 86 Chatper 9 Conclusion 89 Bibliography 91 초록 99Docto

Interactive web-based visualization

The visualization of large amounts of data, which cannot be easily copied for processing on a user’s local machine, is not yet a fully solved problem. Remote visualization represents one possible solution approach to the problem, and has long been an important research topic. Depending on the device used, modern hardware, such as high-performance GPUs, is sometimes not available. This is another reason for the use of remote visualization. Additionally, due to the growing global networking and collaboration among research groups, collaborative remote visualization solutions are becoming more important. The additional use of collaborative visualization solutions is eventually due to the growing global networking and collaboration among research groups. The attractiveness of web-based remote visualization is greatly increased by the wide availability of web browsers on almost all devices; these are available today on all systems - from desktop computers to smartphones. In order to ensure interactivity, network bandwidth and latency are the biggest challenges that web-based visualization algorithms have to solve. Despite the steady improvements in available bandwidth, these improvements are still significantly slower than, for example, processor performance, resulting in increasing the impact of this bottleneck. For example, visualization of large dynamic data in low-bandwidth environments can be challenging because it requires continuous data transfer. However, bandwidth improvement alone cannot improve the latency because it is also affected by factors such as the distance between server and client and network utilization. To overcome these challenges, a combination of techniques is needed to customize the individual processing steps of the visualization pipeline, from efficient data representation to hardware-accelerated rendering on the client side. This thesis first deals with related work in the field of remote visualization with a particular focus on interactive web-based visualization and then presents techniques for interactive visualization in the browser using modern web standards such as WebGL and HTML5. These techniques enable the visualization of dynamic molecular data sets with more than one million atoms at interactive frame rates using GPU-based ray casting. Due to the limitations which exist in a browser-based environment, the concrete implementation of the GPU-based ray casting had to be customized. Evaluation of the resulting performance shows that GPU-based techniques enable the interactive rendering of large data sets and achieve higher image quality compared to polygon-based techniques. In order to reduce data transfer times and network latency, and improve rendering speed, efficient approaches for data representation and transmission are used. Furthermore, this thesis introduces a GPU-based volume-ray marching technique based on WebGL 2.0, which uses progressive brick-wise data transfer, as well as multiple levels of detail in order to achieve interactive volume rendering of datasets stored on a server. The concepts and results presented in this thesis contribute to the further spread of interactive web-based visualization. The algorithmic and technological advances that have been achieved form a basis for further development of interactive browser-based visualization applications. At the same time, this approach has the potential for enabling future collaborative visualization in the cloud.Die Visualisierung großer Datenmengen, welche nicht ohne Weiteres zur Verarbeitung auf den lokalen Rechner des Anwenders kopiert werden können, ist ein bisher nicht zufriedenstellend gelöstes Problem. Remote-Visualisierung stellt einen möglichen Lösungsansatz dar und ist deshalb seit langem ein relevantes Forschungsthema. Abhängig vom verwendeten Endgerät ist moderne Hardware, wie etwa performante GPUs, teilweise nicht verfügbar. Dies ist ein weiterer Grund für den Einsatz von Remote-Visualisierung. Durch die zunehmende globale Vernetzung und Kollaboration von Forschungsgruppen gewinnt kollaborative Remote-Visualisierung zusätzlich an Bedeutung. Die Attraktivität web-basierter Remote-Visualisierung wird durch die weitreichende Verfügbarkeit von Web-Browsern auf nahezu allen Endgeräten enorm gesteigert; diese sind heutzutage auf allen Systemen - vom Desktop-Computer bis zum Smartphone - vorhanden. Bei der Gewährleistung der Interaktivität sind Bandbreite und Latenz der Netzwerkverbindung die größten Herausforderungen, welche von web-basierten Visualisierungs-Algorithmen gelöst werden müssen. Trotz der stetigen Verbesserungen hinsichtlich der verfügbaren Bandbreite steigt diese signifikant langsamer als beispielsweise die Prozessorleistung, wodurch sich die Auswirkung dieses Flaschenhalses immer weiter verstärkt. So kann beispielsweise die Visualisierung großer dynamischer Daten in Umgebungen mit geringer Bandbreite eine Herausforderung darstellen, da kontinuierlicher Datentransfer benötigt wird. Dennoch kann die alleinige Verbesserung der Bandbreite keine entsprechende Verbesserung der Latenz bewirken, da diese zudem von Faktoren wie der Distanz zwischen Server und Client sowie der Netzwerkauslastung beeinflusst wird. Um diese Herausforderungen zu bewältigen, wird eine Kombination verschiedener Techniken für die Anpassung der einzelnen Verarbeitungsschritte der Visualisierungspipeline benötigt, angefangen bei effizienter Datenrepräsentation bis hin zu hardware-beschleunigtem Rendering auf der Client-Seite. Diese Doktorarbeit befasst sich zunächst mit verwandten Arbeiten auf dem Gebiet der Remote-Visualisierung mit besonderem Fokus auf interaktiver web-basierter Visualisierung und präsentiert danach Techniken für die interaktive Visualisierung im Browser mit Hilfe moderner Web-Standards wie WebGL und HTML5. Diese Techniken ermöglichen die Visualisierung dynamischer molekularer Datensätze mit mehr als einer Million Atomen bei interaktiven Frameraten durch die Verwendung GPU-basierten Raycastings. Aufgrund der Einschränkungen, welche in einer Browser-basierten Umgebung vorliegen, musste die konkrete Implementierung des GPU-basierten Raycastings angepasst werden. Die Evaluation der daraus resultierenden Performanz zeigt, dass GPU-basierte Techniken das interaktive Rendering von großen Datensätzen ermöglichen und eine im Vergleich zu Polygon-basierten Techniken höhere Bildqualität erreichen. Zur Verringerung der Übertragungszeiten, Reduktion der Latenz und Verbesserung der Darstellungsgeschwindigkeit werden effiziente Ansätze zur Datenrepräsentation und übertragung verwendet. Des Weiteren wird in dieser Doktorarbeit eine GPU-basierte Volumen-Ray-Marching-Technik auf Basis von WebGL 2.0 eingeführt, welche progressive blockweise Datenübertragung verwendet, sowie verschiedene Detailgrade, um ein interaktives Volumenrendering von auf dem Server gespeicherten Datensätzen zu erreichen. Die in dieser Doktorarbeit präsentierten Konzepte und Resultate tragen zur weiteren Verbreitung von interaktiver web-basierter Visualisierung bei. Die erzielten algorithmischen und technologischen Fortschritte bilden eine Grundlage für weiterführende Entwicklungen von interaktiven Visualisierungsanwendungen auf Browser-Basis. Gleichzeitig hat dieser Ansatz das Potential, zukünftig kollaborative Visualisierung in der Cloud zu ermöglichen