We present the design and implementation of several optimizations and techniques included in the latest IBM Java TM Just-in-Time (JIT) Compiler. We first discuss some of the modifications we have applied to Sun Microsystems' reference implementation of the Java Virtual Machine (JVM TM) Specification to increase the performance, including a change in the object layout. We then describe each of the optimizations, referring to what had to be taken into account because of both the just-in-time nature of the compiler and the requirements of the Java language specification, such as exception checking. We also present code generation techniques targeting Intel architectures, describing the register allocation schemes, exception handling, and code scheduling. Finally we report on the performance of the IBM JIT compiler, showing both the effectiveness of the individual optimizations and the competitive overall performance of the JIT compiler in comparison with a competitor, using industry-standard benchmarking programs. All the techniques presented here are included in the official product (JIT Compiler version 3.0), which has been integrated into the IBM Developer Kit for Windows TM , Java Technology Edition, Version 1.1.7.
The Java language incurs a runtime overhead for exception checks and object accesses without an interior pointer in order to ensure safety. It also requires type inclusion test, dynamic class loading, and dynamic method calls in order to ensure flexibility. A "JustIn-Time" (JIT) compiler generates native code from Java byte code at runtime. It must improve the runtime performance without compromising the safety and flexibility of the Java language. We designed and implemented effective optimizations for the JIT compiler, such as exception check elimination, common subexpression elimination, simple type inclusion test, method inlining, and resolution of dynamic method call. We evaluate the performance benefits of these optimizations based on various statistics collected using SPECjvm98 and two JavaSoft applications with byte code sizes ranging from 20000 to 280000 bytes. Each optimization contributes to an improvement in the performance of the programs.
Many devirtualization techniques have been proposed to reduce the runtime overhead of dynamic method calls for various objectoriented languages, however, most of them are less effective or cannot be applied for Java in a straightforward manner. This is partly because Java is a statically-typed language and thus transforming a dynamic call to a static one does not make a tangible performance gain (owing to the low overhead of accessing the method table) unless it is inlined, and partly because the dynamic class loading feature of Java prohibits the whole program analysis and optimizations from being applied.We propose a new technique called direct devirtualization with the code patching mechanism. For a given dynamic call site, our compiler first determines whether the call can be devirtualized, by analyzing the current class hierarchy. When the call is devirtualizable and the target method is suitably sized, the compiler generates the inlined code of the method, together with the backup code of making the dynamic call. Only the inlined code is actually executed until our assumption about the devirtualization becomes invalidated, at which time the compiler performs code patching to make the backup code executed subsequently. Since the new technique prevents some code motions across the merge point between the inlined code and the backup code, we have furthermore implemented recently-known analysis techniques, such as type analysis and preexistence analysis, which allow the backup code to be completely eliminated. We made various experiments using 16 real programs to understand the effectiveness and characteristics of the devirtualization techniques in our Java Just-InTime (JIT) compiler. In summary, we reduced the number of dynamic calls by ranging from 8.9% to 97.3% (the average of 40.2%), and we improved the execution performance by ranging from -1% to 133% (with the geometric mean of 16%).
The high performance implementation of Java Virtual Machines (JVM) and Just-In-Time (JIT) compilers is directed toward employing a dynamic compilation system on the basis of online runtime profile information. The trade-off between the compilation overhead and performance benefit is a crucial issue for such a system. This article describes the design and implementation of a dynamic optimization framework in a production-level Java JIT compiler, together with two techniques for profile-directed optimizations: method inlining and code specialization. Our approach is to employ a mixed mode interpreter and a three-level optimizing compiler, supporting level-1 to level-3 optimizations, each of which has a different set of trade-offs between compilation overhead and execution speed. A lightweight sampling profiler operates continuously during the entire period while applications are running to monitor the programs' hot spots. Detailed information on runtime behavior can be collected by dynamically generating instrumentation code that is installed to and uninstalled from the specified recompilation target code. Value profiling with this instrumentation mechanism allows fully automatic profile-directed method inlining and code specialization to be performed on the basis of call site information or specific parameter values at the higher optimization levels. The experimental results show that our approach offers high performance and low compilation overhead in both program startup and steady state measurements in comparison to the previous systems. The two profile-directed optimization techniques contribute significant portions of the improvements.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.