Peter F. Sweeney

Fast static analysis of C++ virtual function calls

Virtual functions make code easier for programmers to reuse but also make it harder for compilers to analyze. We investigate the ability of three static analysis algorithms to improve C++ programs by resolving virtual function calls, thereby reducing compiled code size and reducing program complexity so as to improve both human and automated program understanding and analysis. In measurements of seven programs of significant size (5000 to 20000 lines of code each) we found that on average the most precise of the three algorithms resolved 71% of the virtual function calls and reduced compiled code size by 25%. This algorithm is very fast: it analyzes 3300 source lines per second on an 80 MHz PowerPC 601. Because of its accuracy and speed, this algorithm is an excellent candidate for inclusion in production C++ compilers.

Adaptive optimization in the Jalapeno JVM

Future high-performance virtual machines will improve performance through sophisticated online feedback-directed optimizations. This paper presents the architecture of the Jalapeno Adaptive Optimization System, a system to support leading-edge virtual machine technology and enable ongoing research on online feedback-directed optimizations. We describe the extensible system architecture, based on a federation of threads with asynchronous communication. We present an implementation of the general architecture that supports adaptive multi-level optimization based purely on statistical sampling. We empirically demonstrate that this profiling technique has low overhead and can improve startup and steady-state performance, even without the presence of online feedback-directed optimizations. The paper also describes and evaluates an online feedback-directed inlining optimization based on statistical edge sampling. The system is written completely in Java, applying the described techniques not only to application code and standard libraries, but also to the virtual machine itself.

Producing wrong data without doing anything obviously wrong

This paper presents a surprising result: changing a seemingly innocuous aspect of an experimental setup can cause a systems researcher to draw wrong conclusions from an experiment. What appears to be an innocuous aspect in the experimental setup may in fact introduce a significant bias in an evaluation. This phenomenon is called measurement bias in the natural and social sciences. Our results demonstrate that measurement bias is significant and commonplace in computer system evaluation. By significant we mean that measurement bias can lead to a performance analysis that either over-states an effect or even yields an incorrect conclusion. By commonplace we mean that measurement bias occurs in all architectures that we tried (Pentium 4, Core 2, and m5 O3CPU), both compilers that we tried (gcc and Intels C compiler), and most of the SPEC CPU2006 C programs. Thus, we cannot ignore measurement bias. Nevertheless, in a literature survey of 133 recent papers from ASPLOS, PACT, PLDI, and CGO, we determined that none of the papers with experimental results adequately consider measurement bias. Inspired by similar problems and their solutions in other sciences, we describe and demonstrate two methods, one for detecting (causal analysis) and one for avoiding (setup randomization) measurement bias.

A Survey of Adaptive Optimization in Virtual Machines

Virtual machines face significant performance challenges beyond those confronted by traditional static optimizers. First, portable program representations and dynamic language features, such as dynamic class loading, force the deferral of most optimizations until runtime, inducing runtime optimization overhead. Second, modular program representations preclude many forms of whole-program interprocedural optimization. Third, virtual machines incur additional costs for runtime services such as security guarantees and automatic memory management. To address these challenges, vendors have invested considerable resources into adaptive optimization systems in production virtual machines. Today, mainstream virtual machine implementations include substantial infrastructure for online monitoring and profiling, runtime compilation, and feedback-directed optimization. As a result, adaptive optimization has begun to mature as a widespread production-level technology. This paper surveys the evolution and current state of adaptive optimization technology in virtual machines.

Three steps to views: extending the object-oriented paradigm

At the core of any sophisticated software development and maintenance environment is a large mass of complex data. The data (the central data of the environment) is composed of smaller sets of data that can be related in complicated and often subtle ways. The user or developer of the environment will be more effective if they are able to deal with conceptual slices, or views , of the large, complex structure. This paper presents an architectural building block for object-based software environments based on the views concept. The building block allows the construction of global abstractions that describe unified behavior of large sets of objects. The basis of the architecture relies on extending the object-oriented paradigm in three steps: (1) defining multiple interfaces in object classes; (2) controlling visibility of instance variables; and (3) allowing multiple copies of an instance variable to occur within an object instance. This paper focuses on the technical aspects of the views approach.

Vertical profiling: understanding the behavior of object-priented applications

Object-oriented programming languages provide a rich set of features that provide significant software engineering benefits. The increased productivity provided by these features comes at a justifiable cost in a more sophisticated runtime system whose responsibility is to implement these features efficiently. However, the virtualization introduced by this sophistication provides a significant challenge to understanding complete system performance, not found in traditionally compiled languages, such as C or C++. Thus, understanding system performance of such a system requires profiling that spans all levels of the execution stack, such as the hardware, operating system, virtual machine, and application. In this work, we suggest an approach, called <i>vertical profiling</i>, that enables this level of understanding. We illustrate the efficacy of this approach by providing deep understandings of performance problems of Java applications run on a VM with vertical profiling support. By incorporating vertical profiling into a programming environment, the programmer will be able to understand how their program interacts with the underlying abstraction levels, such as application server, VM, operating system, and hardware.

Practical experience with an application extractor for Java

Java programs are routinely transmitted over low-bandwidth network connections as compressed class file archives (i.e., zip files and jar files). Since archive size is directly proportional to download time, it is desirable for applications to be as small as possible. This paper is concerned with the use of program transformations such as removal of dead methods and fields, inlining of method calls, and simplification of the class hierarchy for reducing application size. Such “extraction” techniques are generally believed to be especially useful for applications that use class libraries, since typically only a small fraction of a librarys functionality is used. By “pruning away” unused library functionality, application size can be reduced dramatically. We implemented a number of application extraction techniques in Jax, an application extractor for Java, and evaluate their effectiveness on a set of realistic benchmarks ranging from 27 to 2,332 classes (with archives ranging from 56,796 to 3,810,120 bytes). We report archive size reductions ranging from 13.4% to 90.2% (48.7% on average).

Online Phase Detection Algorithms

Todays virtual machines (VMs) dynamically optimize an application as it is executing, often employing optimizations that are specialized for the current execution profile. An online phase detector determines when an executing program is in a stable period of program execution (a phase) or is in transition. A VM using an online phase detector can apply specialized optimizations during a phase or reconsider optimization decisions between phases. Unfortunately, extant approaches to detecting phase behavior rely on either offline profiling, hardware support, or are targeted toward a particular optimization. In this work, we focus on the enabling technology of online phase detection. More specifically, we contribute (a) a novel framework for online phase detection, (b) multiple instantiations of the framework that produce novel online phase detection algorithms, (c) a novel client- and machine-independent baseline methodology for evaluating the accuracy of an online phase detector, (d) a metric to compare online detectors to this baseline, and (e) a detailed empirical evaluation, using Java applications, of the accuracy of the numerous phase detectors.

A comparative study of static and profile-based heuristics for inlining

In this paper, we present a comparative study of static and profile-based heuristics for inlining. Our motivation for this study is to use the results to design the best inlining algorithm that we can for the Jalapeno dynamic optimizing compiler for Java [6]. We use a well-known approximation algorithm for the KNAPSACK problem as a common “meta-algorithm” for the inlining heuristics studied in this paper. We present performance results for an implementation of these inlining heuristics in the Jalapeno dynamic optimizing compiler. Our performance results show that the inlining heuristics studied in this paper can lead to significant speedups in execution time (up to 1.68x) even with modest limits on code size expansion (at most 10%).

Practical extraction techniques for Java

Reducing application size is important for software that is distributed via the internet, in order to keep download times manageable, and in the domain of embedded systems, where applications are often stored in (Read-Only or Flash) memory. This paper explores extraction techniques such as the removal of unreachable methods and redundant fields, inlining of method calls, and transformation of the class hierarchy for reducing application size. We implemented a number of extraction techniques in Jax, an application extractor for Java, and evaluated their effectiveness on a set of large Java applications. We found that, on average, the class file archives for these benchmarks were reduced to 37.5% of their original size. Modeling dynamic language features such as reflection, and extracting software distributions other than complete applications requires additional user input. We present a uniform approach for supplying this input that relies on MEL, a modular specification language. We also discuss a number of issues and challenges associated with the extraction of embedded systems applications.