Applications of information sharing for code generation in process virtual machines

As the backbone of many computing environments today, it is important that process virtual machines be both performant and robust in mobile, personal desktop, and enterprise applications. This thesis focusses on code generation within these virtual machines, particularly addressing situations where redundant work is being performed. The goal is to exploit information sharing in order to improve the performance and robustness of virtual machines that are accelerated by native code generation. First, the thesis investigates the potential to share generated code between multiple threads in a dynamic binary translator used to perform instruction set simulation. This is done through a code generation design that allows native code to be executed by any simulated core and adding a mechanism to share native code regions between threads. This is shown to improve the average performance of multi-threaded benchmarks by 1.4x when simulating 128 cores on a quad-core host machine. Secondly, the ahead-of-time code generation system used for executing Android applications is improved through the use of profiling. The thesis investigates the potential for profiles produced by individual users of applications to be shared and merged together to produce a generic profile that still provides a lot of benefit for a new user who is then able to skip the expensive profiling phase. These profiles can not only be used for selective compilation to reduce code-size and installation time, but can also be used for focussed optimisation on vital code regions of an application in order to improve overall performance. With selective compilation applied to a set of popular Android applications, code-size can be reduced by 49.9% on average, while installation time can be reduced by 31.8%, with only an average 8.5% increase in the amount of sequential runtime required to execute the collected profiles. The thesis also shows that, among the tested users, the use of a crowd-sourced and merged profile does not significantly affect their estimated performance loss from selective compilation (0.90x-0.92x) in comparison to when they they perform selective compilation with their own unique profile (0.93x). Furthermore, by proposing a new, more powerful code generator for Android’s virtual machine, these same profiles can be used to perform focussed optimisation, which preliminary results show to increase runtime performance across a set of common Android benchmarks by 1.46x-10.83x. Finally, in such a situation where a new code generator is being added to a virtual machine, it is also important to test the code generator for correctness and robustness. The methods of execution of a virtual machine, such as interpreters and code generators, must share a set of semantics about how programs must be executed, and this can be exploited in order to improve testing. This is done through the application of domain-aware binary fuzzing and differential testing within Android’s virtual machine. The thesis highlights a series of actual code generation and verification bugs that were found in Android’s virtual machine using this testing methodology, as well as comparing the proposed approach to other state-of-the-art fuzzing techniques

Fuzz Testing of Program Performance

Oprava jednej chyby niekedy prináša do programu ďalších desať. Na odhalenie týchto chýb, najmä výkonnostných, často musíme programu poskytnúť vstup, ktorý vynúti jeho správanie pre najhorší prípad. Populárnym riešením pre automatické generovanie vstupov je tzv. fuzzing, avšak jeho cieľom je nájsť funkčné chyby programu. V tejto práci sa preto snažíme vytvoriť automatický generátor vstupov, ktorého úlohou bude vyvolať výkonnostné výkyvy. Navrhli sme preto vyladené fuzzing pravidlá pre mutáciu a spôsob spracovania informácií o behu programu so zámerom zachytiť výkonnostnú degradáciu. Naše riešenie je integrované do nástroja Perun, správcu výkonnostných profilov, ktorý uchováva informácie o každom behu vo forme profilu a je schopný porovnať tieto profily s cieľom detekovať zmenu vo výkone. Takýmto spôsobom môžeme dokázať, že beh programu s určitým vstupom zaberie viac času alebo pamäte. Náš fuzzer sme testovali na niekoľkých umelo vytvorených projektoch, kde ukazuje svoj potenciál generovanými vstupmi, ktoré markantne predlžujú dobu behu programu. Prínosom takéhoto riešenia je možnosť pre vývojárov pravidelne otestovať každú verziu projektu na výskyt výkonnostných chýb a vyhýbať sa im automatickým vyhľadávaním nečakaných vstupov.Fixing one issue sometimes brings another ten to the program. To detect these issues, especially performance issues, we often have to supply the program with input, that forces its worst-case behaviour.  A popular solution to automatic inputs generation is so called fuzzing, however, its intention is to find functional bugs. In this work, we aim to construct an automatic generator of inputs whose task will be to trigger performance fluctuations. So we propose to tune fuzzing mutation rules and ways of processing the information about program run, to particularly trigger the performance bugs. We integrate our solution into a performance profile manager Perun, which stores information about every run as a profile and is able to compare these profiles to check for performance change. Therefore we can prove that executing with certain input takes more time or memory. We tested our fuzzer on several artificial projects, which shows its potential with generated inputs that prolong the runtime of the program. Such a solution would allow developers to regularly test every version of a project for performance bugs and avoid them completely by automatically finding new exhausting inputs before release.