Danilo Ansaloni

DiSL: a domain-specific language for bytecode instrumentation

Many dynamic analysis tools for programs written in managed languages such as Java rely on bytecode instrumentation. Tool development is often tedious because of the use of low-level bytecode manipulation libraries. While aspect-oriented programming (AOP) offers high-level abstractions to concisely express certain dynamic analyses, the join point model of mainstream AOP languages such as AspectJ is not well suited for many analysis tasks and the code generated by weavers in support of certain language features incurs high overhead. In this paper we introduce DiSL (domain-specific language for instrumentation), a new language especially designed for dynamic program analysis. DiSL offers an open join point model where any region of bytecodes can be a shadow, synthetic local variables for efficient data passing, efficient access to comprehensive static and dynamic context information, and weave-time execution of user-defined static analysis code. We demonstrate the benefits of DiSL with a case study, recasting an existing dynamic analysis tool originally implemented in AspectJ. We show that the DiSL version offers better code coverage, incurs significantly less overhead, and eases the integration of new analysis features that could not be expressed in AspectJ.

Advanced runtime adaptation for Java

Dynamic aspect-oriented programming (AOP) enables runtime adaptation of aspects, which is important for building sophisticated, aspect-based software engineering tools, such as adaptive profilers or debuggers that dynamically modify instrumentation code in response to user interactions. Today, many AOP frameworks for Java, notably AspectJ, focus on aspect weaving at compile-time or at load-time, and offer only limited support for aspect adaptation and reweaving at runtime. In this paper, we introduce HotWave, an AOP framework based on AspectJ for standard Java Virtual Machines (JVMs). HotWave supports dynamic (re)weaving of previously loaded classes, and it ensures that all classes loaded in a JVM can be (re)woven, including the classes of the standard Java class library. HotWave features a novel mechanism for inter-advice communication, enabling efficient data passing between advices that are woven into the same method. We explain HotWaves programming model and discuss our implementation techniques. As case study, we present an adaptive, aspect-based profiler that leverages HotWaves distinguishing features.

Achieving application-centric performance targets via consolidation on multicores: myth or reality?

Consolidation of multiple applications with diverse and changing resource requirements is common in multicore systems as hardware resources are abundant and opportunities for better system usage are plenty. Can we maximize resource usage in such a system while respecting individual application performance targets or is it an oxymoron to simultaneously meet such conflicting measures? In this work we provide a solution to the above difficult problem by constructing a queueing-theory based tool that we use to accurately predict application scalability on multicores and that can also provide the optimal consolidation suggestions to maximize system resource usage while meeting simultaneously application performance targets. The proposed methodology is light-weight and relies on capturing application resource demands using standard tools, via nonintrusive low-level measurements. We evaluate our approach on an IBM Power7 system using the DaCapo and SPECjvm benchmark suites where each benchmark exhibits different patterns of parallelism. From 900 different consolidations of application instances, our tool accurately predicts the average iteration time of allocated applications with an average error below 10%.

Augmenting static source views in IDEs with dynamic metrics

Mainstream IDEs such as Eclipse support developers in managing software projects mainly by offering static views of the source code. Such a static perspective neglects any information about runtime behavior. However, object-oriented programs heavily rely on polymorphism and late-binding, which makes them difficult to understand just based on their static structure. Developers thus resort to debuggers or profilers to study the systems dynamics. However, the information provided by these tools is volatile and hence cannot be exploited to ease the navigation of the source space. In this paper we present an approach to augment the static source perspective with dynamic metrics such as precise runtime type information, or memory and object allocation statistics. Dynamic metrics can leverage the understanding for the behavior and structure of a system. We rely on dynamic data gathering based on aspects to analyze running Java systems. By solving concrete use cases we illustrate how dynamic metrics directly available in the IDE are useful. We also comprehensively report on the efficiency of our approach to gather dynamic metrics.

Exploiting Dynamic Information in IDEs Improves Speed and Correctness of Software Maintenance Tasks

Modern IDEs such as Eclipse offer static views of the source code, but such views ignore information about the runtime behavior of software systems. Since typical object-oriented systems make heavy use of polymorphism and dynamic binding, static views will miss key information about the runtime architecture. In this paper, we present an approach to gather and integrate dynamic information in the Eclipse IDE with the goal of better supporting typical software maintenance activities. By means of a controlled experiment with 30 professional developers, we show that for typical software maintenance tasks, integrating dynamic information into the Eclipse IDE yields a significant 17.5 percent decrease of time spent while significantly increasing the correctness of the solutions by 33.5 percent. We also provide a comprehensive performance evaluation of our approach.

Parallel dynamic analysis on multicores with aspect-oriented programming

In most aspects, advice are synchronously executed by application threads, which may cause high overhead if advice execution is frequent or computationally expensive. When synchronous advice execution is not a necessity, asynchronous advice execution has the potential to parallelize program and advice execution on multicores. However, asynchronous advice execution requires communication between threads, causing some overhead. In order to mitigate such overhead, we introduce buffered advice, a new AOP mechanism for aggregating advice invocations in a thread-local buffer, which is processed when it is full. For asynchronous processing of full buffers, the communication overhead is paid only once per buffer, instead of once per advice invocation. We present an enhanced AOP programming model and framework based on AspectJ, which ease the use of buffered advice and support pluggable, custom buffer processing strategies. As case study, we optimize an existing aspect for data race detection using buffered advice. A thorough evaluation with standard benchmarks confirms that the use of buffered advice yields significant speedup on multicores.

HotWave: creating adaptive tools with dynamic aspect-oriented programming in Java

Developing tools for profiling, debugging, testing, and reverse engineering is error-prone, time-consuming, and therefore costly when using low-level techniques, such as bytecode instrumentation. As a solution to these problems, we promote tool development in Java using high-level aspect-oriented programming (AOP). We demonstrate that the use of aspects yields compact tools that are easy to develop and extend. As enabling technology, we rely on HotWave, a new tool for dynamic and comprehensive aspect weaving. HotWave reconciles compatibility with existing virtual machine and AOP technologies. It provides support for runtime adaptation of aspects and reweaving of previously loaded code, as well as the ability to weave aspects into all methods executing in a Java Virtual Machine, including methods in the standard Java class library. HotWave also features a new mechanism for efficiently passing data between advices that are woven into the same method. We demonstrate the benefits of HotWaves distinguishing features with two case studies in the area of profiling.

new Scala() instance of Java: a comparison of the memory behaviour of Java and Scala programs

While often designed with a single language in mind, managed runtimes like the Java virtual machine (JVM) have become the target of not one but many languages, all of which benefit from the runtimes services. One of these services is automatic memory management. In this paper, we compare and contrast the memory behaviour of programs written in Java and Scala, respectively, two languages which both target the same platform: the JVM. We both analyze core object demographics like object lifetimes as well as secondary properties of objects like their associated monitors and identity hash-codes. We find that objects in Scala programs have lower survival rates and higher rates of immutability, which is only partly explained by the memory behaviour of objects representing closures or boxed primitives. Other metrics vary more by benchmark than language.

Model-driven consolidation of Java workloads on multicores

Optimal resource allocation and application consolidation on modern multicore systems that host multiple applications is not easy. Striking a balance among conflicting targets such as maximizing system throughput and system utilization while minimizing application response times is a quandary for system administrators. The purpose of this work is to offer a methodology that can automate the difficult process of identifying how to best consolidate workloads in a multicore environment. We develop a simple approach that treats the hardware and the operating system as a black box and uses measurements to profile the application resource demands. The demands become input to a queueing network model that successfully predicts application scalability and that captures the performance impact of consolidated applications on shared on-chip and off-chip resources. Extensive analysis with the widely used DaCapo Java benchmarks on an IBM Power 7 system illustrates the models ability to accurately predict the systems optimal application mix.

Composition of dynamic analysis aspects

Aspect-oriented programming provides a convenient high-level model to define several kinds of dynamic analyses, in particular thanks to recent advances in exhaustive weaving in core libraries. Casting dynamic analyses as aspects allows the use of a single weaving infrastructure to apply different analyses to the same base program, simultaneously. However, even if dynamic analysis aspects are mutually independent, their mere presence perturbates the observations of others: this is due to the fact that aspectual computation is potentially visible to all aspects. Because current aspect composition approaches do not address this kind of computational interference, combining different analysis aspects yields at best unpredictable results. It is also impossible to flexibly combine various analyses, for instance to analyze an analysis aspect. In this paper we show how the notion of execution levels makes it possible to effectively address these composition issues. In order to realize this approach, we explore the practical and efficient integration of execution levels in a mainstream aspect language, AspectJ. We report on a case study of composing two out-of-the-box analysis aspects in a variety of ways, highlighting the benefits of the approach.