The high performance implementation of Java Virtual Machines (JVM) and Just-In-Time (JIT) compilers is directed toward adaptive compilation optimizations on the basis of online runtime profile information. This paper describes the design and implementation of a dynamic optimization framework in a production-level Java JIT compiler. Our approach is to employ a mixed mode interpreter and a three level optimizing compiler, supporting quick, full, and special optimization, each of which has a different set of tradeoffs between compilation overhead and execution speed. A lightweight sampling profiler operates continuously during the entire program's execution. When necessary, detailed information on runtime behavior is collected by dynamically generating instrumentation code which can be installed to and uninstalled from the specified recompilation target code. Value profiling with this instrumentation mechanism allows fully automatic code specialization to be performed on the basis of specific parameter values or global data at the highest optimization level. The experimental results show that our approach offers high performance and a low code expansion ratio in both program startup and steady state measurements in comparison to the compile-only approach, and that the code specialization can also contribute modest pertbrmance improvements.

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%).

Modern processors support hardware-assist instructions (such as TRT and TROT instructions on IBM zSeries) to accelerate certain functions such as delimiter search and character conversion. Such special instructions have often been used in high performance libraries, but they have not been exploited well in optimizing compilers except for some limited cases. We propose a new idiom recognition technique derived from a topological embedding algorithm [4] to detect idiom patterns in the input program more aggressively than in previous approaches. Our approach can detect a pattern even if the code segment does not exactly match the idiom. For example, we can detect a code segment that includes additional code within the idiom pattern. We implemented our new idiom recognition approach based on the Java Just-In-Time (JIT) compiler that is part of the J9 Java Virtual Machine, and we supported several important idioms for special hardware-assist instructions on the IBM zSeries and on some models of the IBM pSeries. To demonstrate the effectiveness of our technique, we performed two experiments. The first one is to see how many more patterns we can detect compared to the previous approach. The second one is to see how much performance improvement we can achieve over the previous approach. For the first experiment, we used the Java Compatibility Kit (JCK) API tests. For the second one we used IBM XML parser, SPECjvm98, and SPCjbb2000. In summary, relative to a baseline implementation using exact pattern matching, our algorithm converted 75% more loops in JCK tests. We also observed significant performance improvement of the XML parser by 64%, of SPECjvm98 by 1%, and of SPECjbb2000 by 2% on average on a z990. Finally, we observed the JIT compilation time increases by only 0.32% to 0.44%.

Computer designs are shifting from 32-bit architectures to 64-bit architectures, while most of the programs available today are still designed for 32-bit architectures. Java™, for example, specifies the frequently used "int" as a 32-bit data type. If such Java programs are executed on a 64-bit architecture, many 32-bit values must be sign-extended to 64-bit values for integer operations. This causes serious performance overhead. In this paper, we present a fast and effective algorithm for eliminating sign extensions. We implemented this algorithm in the IBM Java Just-in-Time (JIT) compiler for IA-64™. Our experimental results show that our algorithm effectively eliminates the majority of sign extensions. They also show that it significantly improves performance, while it increases JIT compilation time by only 0.11%. We implemented our algorithm for programs in Java, but it can be applied to any language requiring sign extensions.

We present a new algorithm for eliminating null pointer checks from programs written in Java™. Our new algorithm is split into two phases. In the first phase, it moves null checks backward, and it is iterated for a few times with other optimizations to eliminate redundant null checks and maximize the effectiveness of other optimizations. In the second phase, it moves null checks forward and converts many null checks to hardware traps in order to minimize the execution cost of the remaining null checks. As a result, it eliminates many null checks effectively and exploits the maximum use of hardware traps. This algorithm has been implemented in the IBM cross-platform Java Just-in-Time (JIT) compiler. Our experimental results show that our approach improves performance by up to 71% for jBYTEmark and up to 10% for SPECjvm98 over the previously known best algorithm. They also show that it increases JIT compilation time by only 2.3%. Although we implemented our algorithm for Java, it is also applicable for other languages requiring null checking.