Modern virtual machines often maintain multiple compiled versions of a method. An on-stack replacement (OSR) mechanism enables a virtual machine to transfer execution between compiled versions, even while a method runs. Relying on this mechanism, the system can exploit powerful techniques to reduce compile time and code space, dynamically de-optimize code, and invalidate speculative optimizations. This paper presents a new, simple, mostly compilerindependent mechanism to transfer execution into compiled code. Additionally, we present enhancements to an analytic model for recompilation to exploit OSR for more aggressive optimization. We have implemented these techniques in Jikes RVM and present a comprehensive evaluation, including a study of fully automatic, online, profile-driven deferred compilation.

Today’s 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. 1

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

As hardware complexity increases and virtualization is added at more layers of the execution stack, predicting the performance impact of optimizations becomes increasingly difficult. Production compilers and virtual machines invest substantial development effort in performance tuning to achieve good performance for a range of benchmarks. Although optimizations typically perform well on average, they often have unpredictable impact on running time, sometimes degrading performance significantly. Today’s VMs perform sophisticated feedback-directed optimizations, but these techniques do not address performance degradations, and they actually make the situation worse by making the system more unpredictable. This paper presents an online framework for evaluating the effectiveness of optimizations, enabling an online system to automatically identify and correct performance anomalies that occur at runtime. This work opens the door for a fundamental shift in the way optimizations are developed and tuned for online systems, and may allow the body of work in offline empirical optimization search to be applied automatically at runtime. We present our implementation and evaluation of this system in a product Java VM.

Many optimizations need precise pointer analyses to be effective. Unfortunately, some Java features, such as dynamic class loading, reflection, and native methods, make pointer analyses difficult to develop. Hence, prior pointer analyses for Java either ignore these features or are overly conservative. This paper presents the first non-trivial pointer analysis that deals with all Java language features. This paper identifies all problems in performing Andersen’s pointer analysis for the full Java language, presents solutions to those problems, and uses a full implementation of the solutions in Jikes RVM for validation and performance evaluation. The results from this work should be transferable to other analyses and to other languages.

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Dynamic class loading is an integral part of the Java programming language, offering a number advantages such as lazy class loading and dynamic installation of software components. Unfortunately, these advantages often come at the cost of decreased performance because certain optimizations become more dicult to perform when an optimizing compiler cannot assume that it has seen the whole program. This paper introduces thin guards, a simple but effective technique that uses lightweight runtime tests to identify regions of code within which speculative optimizations can be performed. One application of thin guards is described in detail, demonstrating how they can be used to perform speculative inlining in the presence of dynamic class loading. Our experimental evaluation shows that when used in combination with other traditional compiler optimizations, thin guards can eliminate most of the penalty dynamic class loading. Performance improvements of up to 27% are observed, eliminating up to 92% of the penalty imposed by dynamic class loading.

Method inlining and data flow analysis are two major optimization components for effective program transformations, however they often suffer from the existence of rarely or never executed code contained in the target method. One major problem lies in the assumption that the compilation unit is partitioned at method boundaries. This paper describes the design and implementation of a region-based compilation technique in our dynamic compilation system, in which the compiled regions are selected as code portions without rarely executed code. The key part of this technique is the region selection, partial inlining, and region exit handling. For region selection, we employ both static heuristics and dynamic profiles to identify rare sections of code. The region selection process and method inlining decision are interwoven, so that method inlining exposes other targets for region selection, while the region selection in the inline target conserves the inlining budget, leading to more method inlining. Thus the inlining process can be performed for parts of a method, not for the entire body of the method. When the program attempts to exit from a region boundary, we trigger recompilation and then rely on on-stack replacement to continue the execution from the corresponding entry point in the recompiled code. We have implemented these techniques in our Java JIT compiler, and conducted a comprehensive evaluation. The experimental results show that the approach of region-based compilation achieves approximately 5 % performance improvement on average, while reducing the compilation overhead by 20 to 30%, in comparison to the traditional functionbased compilation techniques.

Optimizing exception handling is critical for programs that frequently throw exceptions. We observed that there are many such exception-intensive programs in various categories of Java programs. There are two commonly used exception handling techniques, stack unwinding and stack cutting. Stack unwinding optimizes the normal path, while stack cutting optimizes the exception handling path. However, there has been no single exception handling technique to optimize both paths. We propose a new technique called Exception-Directed Optimization (Edo), which optimizes exception-intensive programs without slowing down exception-minimal programs. Edo, a feedbackdirected dynamic optimization, consists of three steps, exception path profiling, exception path inlining, and throw elimination. Exception path profiling attempts to detect hot exception paths. Exception path inlining compiles the catching method in a hot exception path, inlining the rest of methods in the path. Throw elimination replaces a throw with the explicit control flow to the corresponding catch. We implemented Edo in IBM's production Justin -Time compiler, and obtained the experimental results, which show that, in SPECjvm98, it improved performance of exceptionintensive programs by up to 18.3% without a#ecting performance of exception-minimal programs at all. Categories and Subject Descriptors D.3 [Software]: Programming Languages; D.3.4 [Programming Languages]: Processors---incremental compilers, optimization, runtime environment General Terms Performance, Experimentation, Languages Keywords Feedback-directed dynamic optimization, dynamic compilers, exception handling, inlining Copyright c # 2001 by the Association for Computing Machinery, Inc. Permission to make digital or hard copies of part or a...

Method inlining and data flow analysis are two major optimization components for effective program transformations, but they often suffer from the existence of rarely or never executed code contained in the target method. One major problem lies in the assumption that the compilation unit is partitioned at method boundaries. This article describes the design and implementation of a region-based compilation technique in our dynamic optimization framework, in which the compiled regions are selected as code portions without rarely executed code. The key parts of this technique are the region selection, partial inlining, and region exit handling. For region selection, we employ both static heuristics and dynamic profiles to identify and eliminate rare sections of code. The region selection process and method inlining decisions are interwoven, so that method inlining exposes other targets for region selection, while the region selection in the inline target conserves the inlining budget, allowing more method inlining to be performed. The inlining process can be performed for parts of a method, not just for the entire body of the method. When the program attempts to exit from a region boundary, we trigger recompilation and then use on-stack replacement to continue the execution from the corresponding entry point in the recompiled code. We have implemented these techniques in our Java JIT compiler, and conducted a comprehensive evaluation. The experimental results show that our region-based compilation approach achieves approximately 4 % performance improvement on average, while reducing the compilation overhead by 10 % to 30%, in comparison to the traditional method-based compilation techniques.