Version 6 of Sun Microsystems' Java HotSpot™ VM ships with a redesigned version of the client just-in-time compiler that includes several research results of the last years. The client compiler is at the heart of the VM configuration used by default for interactive desktop applications. For such applications, low startup and pause times are more important than peak performance. This paper outlines the new architecture of the client compiler and shows how it interacts with the VM. It presents the intermediate representation that now uses static single-assignment (SSA) form and the linear scan algorithm for global register allocation. Efficient support for exception handling and deoptimization fulfills the demands that are imposed by the dynamic features of the Java programming language. The evaluation shows that the new client compiler generates better code in less time. The popular SPECjvm98 benchmark suite is executed 45% faster, while the compilation speed is also up to 40% better. This indicates that a carefully selected set of global optimizations can also be integrated in just-in-time compilers that focus on compilation speed and not on peak performance. In addition, the paper presents the impact of several optimizations on execution and compilation speed. As the source code is freely available, the Java HotSpot™ VM and the client compiler are the ideal basis for experiments with new feedback-directed optimizations in a production-level Java just-in-time compiler. The paper outlines research projects that add fast algorithms for escape analysis, automatic object inlining, and array bounds check elimination.

The Java TM programming language contains built-in synchronization primitives for use in constructing multithreaded programs. Efficient implementation of these synchronization primitives is necessary in order to achieve high performance. Recent research [9, 12, 10, 3, 7] has focused on the run-time elimination of the atomic operations required to implement object monitor synchronization primitives. This paper describes a novel technique called store-free biased locking which eliminates all synchronization-related atomic operations on uncontended object monitors. The technique supports the bulk transfer of object ownership from one thread to another, and the selective disabling of the optimization where unprofitable, using epoch-based bulk rebiasing and revocation. It has been implemented in the production version of the Java HotSpot TM VM and has yielded significant performance improvements on a range of benchmarks and applications. The technique is applicable to any virtual machine-based programming language implementation with mostly block-structured locking primitives.

Interactive autonomous and directable characters require certain architectural support such as a motor system which moves the character based on decisions made by the behavior system. While previous work has focused on these higher-level systems, we focus on the infrastructure provided by the graphics system. We describe how a graphics system designed with synthetic characters in mind can be behavior friendly, simplifying motor and behavior system construction and making new characters easier to create. The system described in this paper was the underlying support for "Swamped!", an interactive 3D cartoon demonstrated in the Enhanced Realities section of SIGGRAPH 98. We describe how our design and implementation allowed us to achieve real-time performance in this Java application.

We describe a face modeling system which estimates complete facial structure and texture from a real-time video stream. The system begins with a face trading algorithm which detects and stabilizes live facial images into a canonical 3D pose. The resulting canonical texture is then processed by a statistical model to filter imperfections and estimate unknown components such as missing pixels and underlying 3D structure. This statistical model is a soft mixture of eigenfeature selectors which span the 3D deformations and texture changes across a training set of laser scanned faces. An iterative algorithm is introduced for determining the dimensional partitioning of the eigenfeatures to maximize their generalization capability over a cross-validation set of data. The model's abilities to filter and estimate absent facial components are then demonstrated over incomplete 3D data. This ultimately allows the model to span known and regress unknown facial information front stabilized natural video sequences generated by a face tracking algorithm. The resulting continuous and dynamic estimation of the model's parameters over a video sequence generates a compact temporal description of the 3D deformations and texture changes of the face.