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%).

Optimizing exception handling is critical for programs that frequently throw exceptions. We observed that there are many such exception-intensive programs iin various categories of Java programs. There are two commonly used exception handling techniques, 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.

This paper describes a new framework of register allocation based on Chaitin-style coloring. Our focus is on maximizing the chances for live ranges to be allocated to the most preferred registers while not destroying the colorability obtained by graph simplification. Our coloring algorithm uses a graph representation of preferences called a Register Preference Graph, which helps find a good register selection. We then try to relax the register selection order created by the graph simplification. The relaxed order is defined as a partial order, represented using a graph called a Coloring Precedence Graph. Our algorithm utilizes such a partial order for the register selection instead of using the traditional simplification-driven order so that the chances of honoring the preferences are effectively increased. Experimental results show that our coloring algorithm is powerful to simultaneously handle spill decisions, register coalescing, and preference resolutions.

Interpreters play an important role in many languages, and their performance is critical particularly for the popular language Java. The performance of the interpreter is important even for high-performance virtual machines that employ just-in-time compiler technology, because there are advantages in delaying the start of compilation and in reducing the number of the target methods to be compiled. Many techniques have been proposed to improve the performance of various interpreters, but none of them has fully addressed the issues of minimizing redundant memory accesses and the overhead of indirect branches inherent to interpreters running on superscalar processors. These issues are especially serious for Java because each bytecode is typically one or a few bytes long and the execution routine for each bytecode is also short due to the low-level, stack-based semantics of Java bytecode. In this paper, we describe three novel techniques of our Java bytecode interpreter, write-through top-of-stack caching (WT), position-based handler customization (PHC), and position-based speculative decoding (PSD), which ameliorate these problems for the PowerPC processors. We show how each technique contributes to improving the overall performance of the interpreter for major Java benchmark programs on an IBM POWER3 processor. Among three, PHC is the most effective one. We also show that the main source of memory accesses is due to bytecode fetches and that PHC successfully eliminates the majority of them, while it keeps the instruction cache miss ratios small.