Object-oriented programming languages confer many benefits, including abstraction, which lets the programmer hide the details of an object’s implementation from the object’s clients. Unfortunately, crossing abstraction boundaries often incurs a substantial run-time overhead in the form of frequent procedure calls. Thus, pervasive use of abstraction, while desirable from a design standpoint, may be impractical when it leads to inefficient programs. Aggressive compiler optimizations can reduce the overhead of abstraction. However, the long compilation times introduced by optimizing compilers delay the programming environment‘s responses to changes in the program. Furthermore, optimization also conflicts with source-level debugging. Thus, programmers are caught on the horns of two dilemmas: they have to choose between abstraction and efficiency, and between responsive programming environments and efficiency. This dissertation shows how to reconcile these seemingly contradictory goals by performing optimizations lazily. Four new techniques work together to achieve high performance and high responsiveness: • Type feedback achieves high performance by allowing the compiler to inline message sends based on information extracted from the runtime system. On average, programs run 1.5 times faster than the previous SELF system; compared to a commercial Smalltalk implementation, two medium-sized benchmarks run about three times faster. This level of performance is obtained with a compiler that is both simpler and faster than previous SELF compilers. • Adaptive optimization achieves high responsiveness without sacrificing performance by using a fast nonoptimizing compiler to generate initial code while automatically recompiling heavily used parts of the program with an optimizing compiler. On a previous-generation workstation like the SPARCstation-2, fewer than 200 pauses exceeded 200 ms during a 50-minute interaction, and 21 pauses exceeded one second. On a currentgeneration workstation, only 13 pauses exceed 400 ms. • Dynamic deoptimization shields the programmer from the complexity of debugging optimized code by transparently recreating non-optimized code as needed. No matter whether a program is optimized or not, it can always be stopped, inspected, and single-stepped. Compared to previous approaches, deoptimization allows more debugging while placing fewer restrictions on the optimizations that can be performed. • Polymorphic inline caching generates type-case sequences on-the-fly to speed up messages sent from the same call site to several different types of object. More significantly, they collect concrete type information for the optimizing compiler. With better performance yet good interactive behavior, these techniques make exploratory programming possible both for pure object-oriented languages and for application domains requiring higher ultimate performance, reconciling exploratory programming, ubiquitous abstraction, and high performance.

Modern generational garbage collectors look for garbage among the young objects, because they have high mortality; however, these objects include the very youngest objects, which clearly are still live. We introduce new garbage collection algorithms, called age-based, some of which postpone consideration of the youngest objects. Collecting less than the whole heap requires write barrier mechanisms to track pointers into the collected region. We describe here a new, efficient write barrier implementation that works for age-based and traditional generational collectors. To compare several collectors, their configurations, and program behavior, we use an accurate simulator that models all heap objects and the pointers among them, but does not model cache or other memory effects. For object-oriented languages, our results demonstrate that an older-first collector, which collects older objects before the youngest ones, copies on average much less data than generational collectors. Our resul...

Generational garbage collectors are able to achieve very small pause times by concentrating on the youngest (most recently allocated) objects when collecting, since objects have been observed to die young in many systems. Generational collectors must keep track of all pointers from older to younger generations, by "monitoring " all stores into the heap. This write barrier has been implemented in a number of ways, varying essentially in the granularity of the information observed and stored. Here we examine a range of write barrier implementations and evaluate their relative performance within a generation scavenging garbage collector for Smalltalk. 1 Introduction Generational collectors achieve short collection pause times partly because they separate heap-allocated objects into two or more generations and do not process all generations during each collection. Empirical studies have shown that in many programs most objects die young, so separating objects by age and focusing collecti...

Modern garbage collectors rely on read and write barriers imposed on heap accesses by the mutator, to keep track of references between different regions of the garbage collected heap, and to synchronize actions of the mutator with those of the collector. It has been a long-standing untested assumption that barriers impose significant overhead to garbage-collected applications. As a result, researchers have devoted effort to development of optimization approaches for elimination of unnecessary barriers, or proposed new algorithms for garbage collection that avoid the need for barriers while retaining the capability for independent collection of heap partitions. On the basis of the results presented here, we dispel the assumption that barrier overhead should be a primary motivator for such efforts. We present a

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The automatic reclamation of storage for unreferenced objects is very important in object databases. Existing language system algorithms for automatic storage reclamation have been shown to be inappropriate. In this paper, we investigate methods to improve the performance of algorithms for automatic storage reclamation of object databases. These algorithms are based on a technique called partitioned garbage collection, in which a subset of the entire database is collected independently of the rest. Specifically, we investigate the policy that is used to select what partition in the database should be collected. The policies that we propose and investigate are based on the intuition that the values of overwritten pointers provide good hints about where to find garbage. Using trace-driven simulation, we show that one of our policies requires less I/O to collect more garbage than any existing implementable policy and performs close to a near-optimal policy over a wide range of database s...

We examine the costs and benefits of a variety of copying garbage collection (GC) mechanisms across multiple architectures and programming languages. Our study covers both low-level object representation and copying issues as well as the mechanisms needed to support more advanced techniques such as generational collection, large object spaces, and type-segregated areas. Our experiments are made possible by a novel performance analysis tool, Oscar. Oscar allows us to capture snapshots of programming language heaps that may then be used to replay garbage collections. The replay program is self-contained and written in C, which makes it easy to port to other architectures and to analyze with standard performance analysis tools. Furthermore, it is possible to study additional programming languages simply by instrumenting existing implementations to capture heap snapshots. In general, we found that careful implementation of GC mechanisms can have a significant benefit. For a simple collecto...

Abstract Type safety and garbage collection in managed languages elimi-nate memory errors such as dangling pointers, double frees, and

Tracing and reference counting are uniformly viewed as being fundamentally different approaches to garbage collection that possess very distinct performance properties. We have implemented highperformance collectors of both types, and in the process observed that the more we optimized them, the more similarly they behaved — that they seem to share some deep structure. We present a formulation of the two algorithms that shows that they are in fact duals of each other. Intuitively, the difference is that tracing operates on live objects, or “matter”, while reference counting operates on dead objects, or “anti-matter”. For every operation performed by the tracing collector, there is a precisely corresponding anti-operation performed by the reference counting collector. Using this framework, we show that all high-performance collectors (for example, deferred reference counting and generational collection) are in fact hybrids of tracing and reference counting. We develop a uniform cost-model for the collectors to quantify the trade-offs that result from choosing different hybridizations of tracing and reference counting. This allows the correct scheme to be selected based on system performance requirements and the expected properties of the target application.

This paper proposes a new scheduling method for multiple murators and a garbage collector running on embedded real-time systems with a single processor and no virtual memory. The hard realtime tasks should reserve a certain amount of heap memory to prevent memory starvation and/or deadline miss. Since the memory requirement depends on the worst-case response time of a garbage collector, the traditional approach in which garbage collection is performed in the background demands large memory space. The proposed scheduling algorithm is based on an aperiodic scheduling technique, sporadic server. This paper also presents a modified copying garbage collection algorithm with hardware support. In order to minimize the worst-case response time of a garbage collector thus reducing the memory requirement, the garbage collector runs as the highest priority task with a preset bandwidth. This paper also investigates the schedulability of a garbage collector and mutator tasks as well as the worst-case memory requirement. Performance analysis shows that the proposed algorithm can provide a considerable reduction in the worst-case memory requirement compared with the background policy. Simulation results demonstrate that the proposed algorithm can produce the feasible memory requirement comparable to the complex on-line scheduling algorithm such as slack stealing.