The Java Virtual Machine (JVM) has become an execution platform targeted by many programming languages. However, unlike with Java, a statically-typed language, the performance of the JVM and its Just-In-Time (JIT) compiler with dynamically-typed languages lags behind purpose-built language-specific JIT compilers. In this paper, we aim to contribute to the understanding of the workloads imposed on the JVM by dynamic languages. We use various metrics to characterize the dynamic behavior of a variety of programs written in three dynamic languages (Clojure, Python, and Ruby) executing on the JVM. We identify the differences with respect to Java, and briefly discuss their implications.
The Java Virtual Machine (JVM) today hosts implementations of numerous languages. To achieve high performance, JVM implementations rely on heuristics in choosing compiler optimizations and adapting garbage collection behavior. Historically, these heuristics have been tuned to suit the dynamics of Java programs only. This leads to unnecessarily poor performance in case of non-Java languages, which often exhibit systematic differences in workload behavior. Dynamic metrics characterizing the workload help to identify and quantify useful optimizations, but so far, no cohesive suite of metrics has adequately covered properties that vary systematically between Java and non-Java workloads. We present a suite of such metrics, justifying our choice with reference to a range of guest languages. These metrics are implemented on a common portable infrastructure which ensures ease of deployment and customization.
Abstract. Bytecode instrumentation is a key technique for the implementation of dynamic program analysis tools such as profilers and debuggers. Traditionally, bytecode instrumentation has been supported by low-level bytecode engineering libraries that are difficult to use. Recently, the domain-specific aspect language DiSL has been proposed to provide high-level abstractions for the rapid development of efficient bytecode instrumentations. While DiSL supports user-defined expressions that are evaluated at weave-time, the DiSL programming model requires these expressions to be implemented in separate classes, thus increasing code size and impairing code readability and maintenance. In addition, the DiSL weaver may produce a significant amount of dead code, which may impair some optimizations performed by the runtime. In this paper we introduce Turbo, a novel partial evaluator for DiSL, which processes the generated instrumentation code, performs constant propagation, conditional reduction, and pattern-based code simplification, and executes pure methods at weave-time. With Turbo, it is often unnecessary to wrap expressions for evaluation at weave-time in separate classes, thus simplifying the programming model. We present Turbo's partial evaluation algorithm and illustrate its benefits with several case studies. We evaluate the impact of Turbo on weave-time performance and on runtime performance of the instrumented application.
Abstract. Dynamic program analysis tools based on code instrumentation serve many important software engineering tasks such as profiling, debugging, testing, program comprehension, and reverse engineering. Unfortunately, constructing new analysis tools is unduly difficult, because existing frameworks offer little or no support to the programmer beyond the incidental task of instrumentation. We observe that existing dynamic analysis tools re-address recurring requirements in their essential task: maintaining state which captures some property of the analysed program. This paper presents a general architecture for dynamic program analysis tools which treats the maintenance of analysis state in a modular fashion, consisting of mappers decomposing input events spatially, and updaters aggregating them over time. We show that this architecture captures the requirements of a wide variety of existing analysis tools.
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