Visible to the public Biblio

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Du Li, Alex Potanin, Jonathan Aldrich.  2015.  Delegation vs Inheritance for Typestate Analysis. European Conference on Object-Oriented Programming, Formal Techniques for Java-like Programs track.

For several decades, inheritance and delegation have been widely adopted for code reuse in object-oriented languages. Though extensive research has explored the expressiveness of these techniques, little is known about how the choice between them affects formal reasoning. In this paper, we explore this question by describing two core languages that are identical except for the use of inheritance and delegation, respectively. We add support for formal reasoning about typestate to both languages, and evaluate the complexity of the formal semantics and compare the example specifications. Our study suggests that our variant of delegation can substantially simplify typestate reasoning, while inheritance makes code more succinct in the case where open recursion is used.

Junjie Qian, Witawas Srisa-an, Hong Jiang, Sharad Seth, Du Li, Pan Yi.  2016.  Exploiting FIFO Scheduler to Improve Parallel Garbage Collection Performance.. VEE '16 12th ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments.

Recent studies have found that parallel garbage collection performs worse with more CPUs and more collector threads. As part of this work, we further investigate this enomenon and find that poor scalability is worst in highly scalable Java applications. Our investigation to find the causes clearly reveals that efficient multi-threading in an application can prolong the average object lifespan, which results in less effective garbage collection. We also find that prolonging lifespan is the direct result of Linux's Completely Fair Scheduler due to its round-robin like behavior that can increase the heap contention between the application threads. Instead, if we use pseudo first-in-first-out to schedule application threads in large multicore systems, the garbage collection scalability is significantly improved while the time spent in garbage collection is reduced by as much as 21%. The average execution time of the 24 Java applications used in our study is also reduced by 11%. Based on this observation, we propose two approaches to optimally select scheduling policies based on application scalability profile. Our first approach uses the profile information from one execution to tune the subsequent executions. Our second approach dynamically collects profile information and performs policy selection during execution.

Jonathan Aldrich, Cyrus Omar, Alex Potanin, Du Li.  2014.  Language-Based Architectural Control. Proceedings of the International Workshop on Aliasing, Capabilities and Ownership (IWACO), 2014.

Software architects design systems to achieve quality attributes like security, reliability, and performance. Key to achieving these quality attributes are design constraints governing how components of the system are configured, communicate and access resources. Unfortunately, identifying, specifying, communicating and enforcing important design constraints – achieving architectural control – can be difficult, particularly in large software systems. We argue for the development of architectural frameworks, built to leverage language mechanisms that provide for domain-specific syntax, editor services and explicit control over capabilities, that help increase architectural control. In particular, we argue for concise, centralized architectural descriptions which are responsible for specifying constraints and passing a minimal set of capabilities to downstream system components, or explicitly entrusting them to individuals playing defined roles within a team. By integrating these architectural descriptions directly into the language, the type system can help enforce technical constraints and editor services can help enforce social constraints. We sketch our approach in the context of distributed systems. 

Junjie Qian, Witawas Srisa-an, Du Li, Hong Jiang, Sharad Seth, Yaodong Yang.  2015.  SmartStealing: Analysis and Optimization of Work Stealing in Parallel Garbage Collection for Java VM.. Principles and Practice of Programming in Java (PPPJ).

Parallel garbage collection has been used to speedup the collection process on multicore architectures. Similar to other parallel techniques, balancing the workload among threads is critical to ensuring good overall collection performance. To this end, work stealing is employed by the current stateof-the-art Java Virtual Machine, OpenJDK, to keep GC threads from idling during a collection process. However, we found that the current algorithm is not efficient. Its usage can often cause GC performance to be worse than when work stealing is not used. In this paper, we identify three factors that affect work stealing efficiency: determining tasks that can benefit from stealing, frequency with which to attempt stealing, and performance impacts of failed stealing attempts. Based on this analysis, we propose SmartStealing, a new algorithm that can automatically decide whether to attempt stealing at a particular point during execution. If stealing is attempted, it can efficiently identify a task to steal from. We then compare the collection performances when (i) the default work stealing algorithm is used, (ii) work stealing is not used at all, and (iii) the SmartStealing approach is used. Without modifying the remaining garbage collection system, the evaluation result shows that SmartStealing can reduce the parallel GC execution time for 19 of the 21 benchmarks. The average reduction is 50.4% and the highest reduction is 78.7%. We also investigate the performances of SmartStealing on NUMA and UMA architectures.