We present a set of techniques for reducing the memory consumption of object-oriented programs. These techniques include analysis algorithms and optimizations that use the results of these analyses to eliminate fields with constant values, reduce the sizes of fields based on the range of values that can appear in each field, and eliminate fields with common default values or usage patterns. We apply these optimizations both to fields declared by the programmer and to implicit fields in the runtime object header. Although it is possible to apply these techniques to any object-oriented program, we expect they will be particularly appropriate for memory-limited embedded systems. We have implemented these techniques in the MIT FLEX compiler system and applied them to the programs in the SPECjvm98 benchmark suite. Our experimental results show that our combined techniques can reduce the maximum live heap size required for the programs in our benchmark suite by as much as 40%. Some of the optimizations reduce the overall execution time; others may impose modest performance penalties.

Joeq is a virtual machine and compiler infrastructure designed to facilitate research in virtual machine technologies such as Just-InTime and Ahead-Of-Time compilation, advanced garbage collection techniques, distributed computation, sophisticated scheduling algorithms, and advanced run time techniques. Joeq is entirely implemented in Java, leading to reliability, portability, maintainability, and efficiency. It is also language-independent, so code from any supported language can be seamlessly compiled, linked, and executed --- all dynamically. Each component of the virtual machine is written to be independent with a general but well-defined interface, making it easy to experiment with new ideas. Joeq is released as open source software, and is being used as a framework by researchers all over the world on topics ranging from automatic distributed virtual machines to whole-program pointer analysis.

SableVM is an open-source virtual machine for Java, intended as a r esearch framework for efficient execution of Java bytecode. The framework is essentially composed of an extensible bytecode interpreter using state-of-the-art and innovative techniques. Written in the C programming language, and assuming minimal system dependencies, the interpreter emphasizes high-level techniques to support efficient execution. In particular, we introduce new data layouts for classes, virtual tables and object instances that reduce the cost of interface method calls to that of normal virtual calls, allow ecient garbage collection and light synchronization, and make effective use of memory space.

The Java Virtual Machine (JVM) is the corner stone of Java technology, and its efficiency in executing the portable Java bytecodes is crucial for the success of this technology. Interpretation, Just-In-Time (JIT) compilation, and hardware realization are well known solutions for a JVM, and previous research has proposed optimizations for each of these techniques. However, each technique has its pros and cons and may not be uniformly attractive for all hardware platforms. Instead, an understanding of the architectural implications of JVM implementations with real applications, can be crucial to the development of enabling technologies for efficient Java runtime system development on a wide range of platforms (from resource-rich servers to resource-constrained hand-held/embedded systems). Towards this goal, this paper examines architectural issues, from both the hardware and JVM implementation perspectives. It specifically explores the potential of a smart JIT compiler strategy that can ...

Virtual machines face significant performance challenges beyond those confronted by traditional static optimizers. First, portable program representations and dynamic language features, such as dynamic class loading, force the deferral of most optimizations until runtime, inducing runtime optimization overhead. Second, modular

As hardware complexity increases and virtualization is added at more layers of the execution stack, predicting the performance impact of optimizations becomes increasingly difficult. Production compilers and virtual machines invest substantial development effort in performance tuning to achieve good performance for a range of benchmarks. Although optimizations typically perform well on average, they often have unpredictable impact on running time, sometimes degrading performance significantly. Today’s VMs perform sophisticated feedback-directed optimizations, but these techniques do not address performance degradations, and they actually make the situation worse by making the system more unpredictable. This paper presents an online framework for evaluating the effectiveness of optimizations, enabling an online system to automatically identify and correct performance anomalies that occur at runtime. This work opens the door for a fundamental shift in the way optimizations are developed and tuned for online systems, and may allow the body of work in offline empirical optimization search to be applied automatically at runtime. We present our implementation and evaluation of this system in a product Java VM.

This article presents an escape analysis framework for Java to determine (1) if an object is not reachable after its method of creation returns, allowing the object to be allocated on the stack, and (2) if an object is reachable only from a single thread during its lifetime, allowing unnecessary synchronization operations on that object to be removed. We introduce a new program abstraction for escape analysis, the connection graph, that is used to establish reachability relationships between objects and object references. We show that the connection graph can be succinctly summarized for each method such that the same summary information may be used in different calling contexts without introducing imprecision into the analysis. We present an interprocedural algorithm that uses the above property to efficiently compute the connection graph and identify the non-escaping objects for methods and threads. The experimental results, from a prototype implementation of our framework in the IBM High Performance Compiler for Java, are very promising. The percentage of objects that may be allocated on the stack exceeds 70 % of all dynamically created objects in the user code in three out of the ten benchmarks (with a median of 19%), 11 % to 92 % of all mutex lock operations are eliminated in those ten programs (with a median of 51%), and the overall execution time reduction ranges from 2 % to 23 % (with a median of 7%) on a 333 MHz PowerPC workstation with 512 MB memory.

The allocation of lock objects to critical sections in concurrent programs affects both performance and correctness. Recent work explores automatic lock allocation, aiming primarily to minimize conflicts and maximize parallelism by allocating locks to individual critical section interferences. We investigate component-based lock allocation, which allocates locks to entire groups of interfering critical sections. Our allocator depends on a thread-based side effect analysis, and benefits from precise points-to and may happen in parallel information. Thread-local object information has a small impact, and dynamic locks do not improve significantly on static locks. We experiment with a range of small and large Java benchmarks on 2-way, 4-way, and 8-way machines, and find that a single static lock is sufficient for mtrt, that performance degrades by 10 % for hsqldb, that jbb2000 becomes mostly serialized, and that for lusearch, xalan, and jbb2005, component-based lock allocation recovers the performance of the original program. 1.

Current garbage collection (GC) techniques do not (and in general cannot) collect all the garbage that a program produces. This may lead to a performance slowdown and to programs running out of memory space. In this paper, we present a practical algorithm for statically detecting memory leaks occurring in arrays of objects in a garbage collected environment. No previous algorithm exists. The algorithm is conservative, i.e., it never detects a leak on a piece of memory that is subsequently used by the program, although it may fail to identify some leaks. The presence of the detected leaks is exposed to the garbage collector, thus allowing GC to collect more storage. We have instrumented the Java virtual machine to measure the effect of memory leaks in arrays. Our initial experiments indicate that this problem occurs in many Java applications. Our measurements of heap size show improvement on some example programs.

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