Julia is a dynamically-typed garbage-collected language designed for high performance. Julia has a non-moving tracing collector, which, while performant, is subject to the same unavoidable fragmentation and lack of locality as all other non-moving collectors. In this work, we refactor the Julia runtime with the goal of supporting different garbage collectors, including copying collectors. Rather than integrate a specific collector implementation, we implement a third-party heap interface that allows Julia to work with various collectors, and use that to implement a series of increasingly more advanced designs. Our description of this process sheds light on Julia's existing collector and the challenges of implementing copying garbage collection in a mature, high-performance runtime. We have successfully implemented a third-party heap interface for Julia and demonstrated its utility through integration with the MMTk garbage collection framework. We hope that this account of our multi-year effort will be useful both within the Julia community and the garbage collection research community, as well as providing insights and guidance for future language implementers on how to achieve high-performance garbage collection in a highly-tuned language runtime.

Coverage-guided greybox fuzzers rely on control-flow coverage feedback to explore a target program and uncover bugs. Compared to control-flow coverage, data-flow coverage offers a more fine-grained approximation of program behavior. Data-flow coverage captures behaviors not visible as control flow and should intuitively discover more (or different) bugs. Despite this advantage, fuzzers guided by data-flow coverage have received relatively little attention, appearing mainly in combination with heavyweight program analyses (e.g., taint analysis, symbolic execution). Unfortunately, these more accurate analyses incur a high run-time penalty, impeding fuzzer throughput. Lightweight data-flow alternatives to control-flow fuzzing remain unexplored. We present datAFLow , a greybox fuzzer guided by lightweight data-flow profiling. We also establish a framework for reasoning about data-flow coverage, allowing the computational cost of exploration to be balanced with precision. Using this framework, we extensively evaluate datAFLow across different precisions, comparing it against state-of-the-art fuzzers guided by control flow, taint analysis, and data flow. Our results suggest that the ubiquity of control-flow-guided fuzzers is well-founded. The high run-time costs of data-flow-guided fuzzing (~10 × higher than control-flow-guided fuzzing) significantly reduces fuzzer iteration rates, adversely affecting bug discovery and coverage expansion. Despite this, datAFLow uncovered bugs that state-of-the-art control-flow-guided fuzzers (notably, AFL++) failed to find. This was because data-flow coverage revealed states in the target not visible under control-flow coverage. Thus, we encourage the community to continue exploring lightweight data-flow profiling; specifically, to lower run-time costs and to combine this profiling with control-flow coverage to maximize bug-finding potential.

This Replicating Computational Report (RCR) describes (a) our datAFLow fuzzer and (b) how to replicate the results in “ datAFLow : Toward a Data-Flow-Guided Fuzzer.” Our primary artifact is the datAFLow fuzzer. Unlike traditional coverage-guided greybox fuzzers—which use control-flow coverage to drive program exploration— datAFLow uses data-flow coverage to drive exploration. This is achieved through a set of LLVM-based analyses and transformations. In addition to datAFLow , we also provide a set of tools, scripts, and patches for (a) statically analyzing data flows in a target program, (b) compiling a target program with the datAFLow instrumentation, (c) evaluating datAFLow on the Magma benchmark suite, and (d) evaluating datAFLow on the DDFuzz dataset. datAFLow is available at https://github.com/HexHive/datAFLow.

Mutation-based greybox fuzzing---unquestionably the most widely-used fuzzing technique---relies on a set of non-crashing seed inputs (a corpus) to bootstrap the bug-finding process. When evaluating a fuzzer, common approaches for constructing this corpus include: (i) using an empty file; (ii) using a single seed representative of the target's input format; or (iii) collecting a large number of seeds (e.g., by crawling the Internet). Little thought is given to how this seed choice affects the fuzzing process, and there is no consensus on which approach is best (or even if a best approach exists).

On-stack replacement (OSR) is a performance-critical technology for many languages, especially dynamic languages. Conventional wisdom, apparent in JavaScript engines such as V8 and SpiderMonkey, is that OSR must be implemented in a low-level (i.e., in assembly) and language-specific way.

Ubiquitous mobile platforms such as Android rely on managed language run-time environments, also known as language virtual machines (VMs), to run a diverse range of user applications (apps). Each app runs in its own private VM instance, and each VM makes its own private local decisions in managing its use of processor and memory resources. Moreover, the operating system and the hardware do not communicate their low-level decisions regarding power management with the high-level app environment. This lack of coordination across layers and across apps restricts more effective global use of resources on the device.

High performance garbage collectors build upon performance-critical low-level code, typically exhibit multiple levels of concurrency, and are prone to subtle bugs. Implementing, debugging and maintaining such collectors can therefore be extremely challenging. The choice of implementation language is a crucial consideration when building a collector. Typically, the drive for performance and the need for efficient support of low-level memory operations leads to the use of low-level languages like C or C++, which offer little by way of safety and software engineering benefits. This risks undermining the robustness and flexibility of the collector design. Rust's ownership model, lifetime specification, and reference borrowing deliver safety guarantees through a powerful static checker with little runtime overhead. These features make Rust a compelling candidate for a collector implementation language, but they come with restrictions that threaten expressiveness and efficiency. We describe our experience implementing an Immix garbage collector in Rust and C. We discuss the benefits of Rust, the obstacles encountered, and how we overcame them. We show that our Immix implementation has almost identical performance on micro benchmarks, compared to its implementation in C, and outperforms the popular BDW collector on the gcbench micro benchmark. We find that Rust's safety features do not create significant barriers to implementing a high performance collector. Though memory managers are usually considered low-level, our high performance implementation relies on very little unsafe code, with the vast majority of the implementation benefiting from Rust's safety. We see our experience as a compelling proof-of-concept of Rust as an implementation language for high performance garbage collection.

Transactional memory (TM) has long been advocated as a promising pathway to more automated concurrency control for scaling concurrent programs running on parallel hardware. Software TM (STM) has the benefit of being able to run general transactional programs, but at the significant cost of overheads imposed to log memory accesses, mediate access conflicts, and maintain other transaction metadata. Recently, hardware manufacturers have begun to offer commodity hardware TM (HTM) support in their processors wherein the transaction metadata is maintained "for free" in hardware. However, HTM approaches are only best-effort: they cannot successfully run all transactional programs, whether because of hardware capacity issues (causing large transactions to fail), or compatibility restrictions on the processor instructions permitted within hardware transactions (causing transactions that execute those instructions to fail). In such cases, programs must include failure-handling code to attempt the computation by some other software means, since retrying the transaction would be futile. Thus, a canonical use of HTM is lock elision: replacing lock regions with transactions, retrying some number of times in the case of conflicts, but falling back to locking when HTM fails for other reasons.

Dynamic voltage and frequency scaling (DVFS) is ubiquitous on mobile devices as a mechanism for saving energy. Reducing the clock frequency of a processor allows a corresponding reduction in power consumption, as does turning off idle cores. Garbage collection is a canonical example of the sort of memory-bound workload that best responds to such scaling. Here, we explore the impact of frequency scaling for garbage collection in a real mobile device running Android's Dalvik virtual machine, which uses a concurrent collector. By controlling the frequency of the core on which the concurrent collector thread runs we can reduce power significantly. Running established multi-threaded benchmarks shows that total processor energy can be reduced up to 30%, with end-to-end performance loss of at most 10%.

Yieldpoints are critical to the implementation of high performance garbage collected languages, yet the design space is not well understood. Yieldpoints allow a running program to be interrupted at well-defined points in its execution, facilitating exact garbage collection, biased locking, on-stack replacement, profiling, and other important virtual machine behaviors. In this paper we identify and evaluate yieldpoint design choices, including previously undocumented designs and optimizations. One of the designs we identify opens new opportunities for very low overhead profiling. We measure the frequency with which yieldpoints are executed and establish a methodology for evaluating the common case execution time overhead. We also measure the median and worst case time-to-yield. We find that Java benchmarks execute about 100M yieldpoints per second, of which about 1/20000 are taken. The average execution time overhead for untaken yieldpoints on the VM we use ranges from 2.5% to close to zero on modern hardware, depending on the design, and we find that the designs trade off total overhead with worst case time-to-yield. This analysis gives new insight into a critical but overlooked aspect of garbage collector implementation, and identifies a new optimization and new opportunities for very low overhead profiling.

We report on a machine-checked verification of safety for a state-of-the-art, on-the-fly, concurrent, mark-sweep garbage collector that is designed for multi-core architectures with weak memory consistency. The proof explicitly incorporates the relaxed memory semantics of x86 multiprocessors. To our knowledge, this is the first fully machine-checked proof of safety for such a garbage collector. We couch the proof in a framework that system implementers will find appealing, with the fundamental components of the system specified in a simple and intuitive programming language. The abstract model is detailed enough for its correspondence with an assembly language implementation to be straightforward.

Small mobile devices have evolved to versatile computing systems. Android devices run a complete software stack, including a full Linux kernel, user land with several software daemons and a virtual machine to run applications. On these mobile systems energy is a scarce resource and needs to be handled carefully. Current systems rely on governors that adjust the frequency of individual cores depending on the system load.

Read and write barriers mediate access to the heap allowing the collector to control and monitor mutator actions. For this reason, barriers are a powerful tool in the design of any heap management algorithm, but the prevailing wisdom is that they impose significant costs. However, changes in hardware and workloads make these costs a moving target. Here, we measure the cost of a range of useful barriers on a range of modern hardware and workloads. We confirm some old results and overturn others. We evaluate the microarchitectural sensitivity of barrier performance and the differences among benchmark suites. We also consider barriers in context, focusing on their behavior when used in combination, and investigate a known pathology and evaluate solutions. Our results show that read and write barriers have average overheads as low as 5.4% and 0.9% respectively. We find that barrier overheads are more exposed on the workload provided by the modern DaCapo benchmarks than on old SPECjvm98 benchmarks. Moreover, there are differences in barrier behavior between in-order and out-of- order machines, and their respective memory subsystems, which indicate different barrier choices for different platforms. These changing costs mean that algorithm designers need to reconsider their design choices and the nature of their resulting algorithms in order to exploit the opportunities presented by modern hardware.

Managed languages such as Java and C# are increasingly being considered for hard real-time applications because of their productivity and software engineering advantages. Automatic memory management, or garbage collection, is a key enabler for robust, reusable libraries, yet remains a challenge for analysis and implementation of real-time execution environments. This article comprehensively compares leading approaches to hard real-time garbage collection. There are many design decisions involved in selecting a real-time garbage collection algorithm. For time-based garbage collectors on uniprocessors one must choose whether to use periodic , slack-based or hybrid scheduling. A significant impediment to valid experimental comparison of such choices is that commercial implementations use completely different proprietary infrastructures. We present Minuteman, a framework for experimenting with real-time collection algorithms in the context of a high-performance execution environment for real-time Java. We provide the first comparison of the approaches, both experimentally using realistic workloads, and analytically in terms of schedulability.

Concurrent and incremental collectors require barriers to ensure correct synchronisation between mutator and collector. The overheads imposed by particular barriers on particular systems have been widely studied. Somewhat fewer studies have also compared barriers in terms of their termination properties or the volume of floating garbage they generate. Until now, the consequences for locality of different barrier choices has not been studied, although locality will be of increasing importance for emerging architectures. This paper provides a study of the locality of concurrent write barriers, independent of the processor architecture, virtual machine, compiler or garbage collection algorithm.

Managed languages such as Java and C# are being considered for use in hard real-time systems. A hurdle to their widespread adoption is the lack of garbage collection algorithms that offer predictable space-and-time performance in the face of fragmentation. We introduce SCHISM/CMR, a new concurrent and real-time garbage collector that is fragmentation tolerant and guarantees time-and-space worst-case bounds while providing good throughput. SCHISM/CMR combines mark-region collection of fragmented objects and arrays (arraylets) with separate replication-copying collection of immutable arraylet spines, so as to cope with external fragmentation when running in small heaps. We present an implementation of SCHISM/CMR in the Fiji VM, a high-performance Java virtual machine for mission-critical systems, along with a thorough experimental evaluation on a wide variety of architectures, including server-class and embedded systems. The results show that SCHISM/CMR tolerates fragmentation better than previous schemes, with a much more acceptable throughput penalty.

The indirection of object accesses is a common theme for target domains as diverse as transparent distribution, persistence, and program instrumentation. Virtualizing accesses to fields and methods (by redirecting calls through accessor and indirection methods) allows interposition of arbitrary code, extending the functionality of an application beyond that intended by the original developer.

Managed languages such as Java and C# are increasingly being considered for hard real-time applications because of their productivity and software engineering advantages. Automatic memory management, or garbage collection, is a key enabler for robust, reusable libraries, yet remains a challenge for analysis and implementation of real-time execution environments. This paper comprehensively compares the two leading approaches to hard real-time garbage collection. While there are many design decisions involved in selecting a real-time garbage collection algorithm, for time-based garbage collectors researchers and practitioners remain undecided as to whether to choose periodic scheduling or slack-based scheduling. A significant impediment to valid experimental comparison is that the commercial implementations use completely different proprietary infrastructures. Here, we present Minuteman, a framework for experimenting with real-time collection algorithms in the context of a high-performance execution environment for real-time Java. We provide the first comparison of the two approaches, both experimentally using realistic workloads, and analytically in terms of schedulability.

This paper describes the development and initial evaluation of a new course ``Introduction to Computational Thinking'' taken by science majors to fulfill a college computing requirement. The course was developed by computer science faculty in collaboration with science faculty and it focuses on the role of computing and computational principles in scientific inquiry. It uses Python and Python libraries to teach computational thinking via basic programming concepts, data management concepts, simulation, and visualization. Problems with a computational aspect are drawn from different scientific disciplines and are complemented with lectures from faculty in those areas. Our initial evaluation indicates that the problem-driven approach focused on scientific discovery and computational principles increases the student's interest in computing.

Memory management is a critical issue for correctness and performance in real-time embedded systems. Recent work on real-time garbage collectors has shown that it is possible to provide guarantees on worst-case pause times and minimum mutator utilization time. This paper presents a new hierarchical real-time garbage collection algorithm for mixed-priority and mixed-criticality environments. With hierarchical garbage collection, real-time programmers can partition the heap into a number of heaplets and for each partition choose to run a separate collector with a schedule that matches the allocation behavior and footprint of the real-time task using it. This approach lowers worst-case response times of real-time applications by 26%, while almost doubling mutator utilization - all with only minimal changes to the application code.

Transactional memory (TM) promises to simplify concurrent programming while providing scalability competitive to fine-grained locking. Language-based constructs allow programmers to denote atomic regions declaratively and to rely on the underlying system to provide transactional guarantees along with concurrency. In contrast with fine-grained locking, TM allows programmers to write simpler programs that are composable and deadlock-free.

Modern commodity platforms increasingly support thread-level parallelism, which must be exploited by garbage collected applications. We describe the design and implementation of a portable mostly-concurrent mostly-copying garbage collector that exhibits scalable performance on multi-processors. We characterize its performance for heap-intensive workloads on two different multiprocessor platforms, showing maximum pause times two orders of magnitude shorter than for fully stop-the-world collection at the cost of some total mutator throughput. Categories and Subject Descriptors D.3.4 [Programming Languages]: Processors—code generation, compilers, memory management (garbage collection), run-time environments

As programming languages with managed runtimes become increasingly popular, it is essential that virtual machines are implemented efficiently. The performance of the memory management subsystem can be a defining factor in the performance of the virtualachine as a whole. We present a technique by which garbage collector performance can be improved. We describe an algorithm that combines a standard generational copying collector with a mark and compact collector. We observe that, since most objects do not survive a garbage collection, it is not necessary to reserve space to copy them all. The result is a generational copying collector that operates with a smaller copy reserve overhead than traditional Appel-style collectors. We maintain correctness in the worst case through the use of mark and compact collection. When the reduced copy reserve overflows, a compacting phase ensures that all data are accommodated. We have implemented this algorithm within the framework of Jikes RVM and MMTk. For most benchmarks examined, our experiments show that performance is comparable to or better than a standard generational copying collector.

Since benchmarks drive computer science research and industry product development, which ones we use and how we evaluate them are key questions for the community. Despite complex runtime tradeoffs due to dynamic compilation and garbage collection required for Java programs, many evaluations still use methodologies developed for C, C++, and Fortran. SPEC, the dominant purveyor of benchmarks, compounded this problem by institutionalizing these methodologies for their Java benchmark suite. This paper recommends benchmarking selection and evaluation methodologies, and introduces the DaCapo benchmarks, a set of open source, client-side Java benchmarks. We demonstrate that the complex interactions of (1) architecture, (2) compiler, (3) virtual machine, (4) memory management, and (5) application require more extensive evaluation than C, C++, and Fortran which stress (4) much less, and do not require (3). We use and introduce new value, time-series, and statistical metrics for static and dynamic properties such as code complexity, code size, heap composition, and pointer mutations. No benchmark suite is definitive, but these metrics show that DaCapo improves over SPEC Java in a variety of ways, including more complex code, richer object behaviors, and more demanding memory system requirements. This paper takes a step towards improving methodologies for choosing and evaluating benchmarks to foster innovation in system design and implementation for Java and other managed languages.

This paper proposes two approaches to managing concurrency in Java using a guarded region abstraction. Both approaches use revocation of such regions—the ability to undo their effects automatically and transparently. These new techniques alleviate many of the constraints that inhibit construction of transparently scalable and robust concurrent applications. The first solution, revocable monitors, augments existing mutual exclusion monitors with the ability to dynamically resolve priority inversion and deadlock, by reverting program execution to a consistent state when such situations are detected, while preserving Java semantics. The second technique, transactional monitors, extends the functionality of revocable monitors by implementing guarded regions as lightweight transactions that can be executed concurrently (or in parallel on multiprocessor platforms). The presentation includes discussion of design and implementation issues for both schemes, as well as a detailed performance study to compare their behavior with the traditional, state-of-the-art implementation of Java monitors based on mutual exclusion. Copyright © 2006 John Wiley & Sons, Ltd.

A future is a simple and elegant abstraction that allows concurrency to be expressed often through a relatively small rewrite of a sequential program. In the absence of side-effects, futures serve as benign annotations that mark potentially concurrent regions of code. Unfortunately, when computation relies heavily on mutation as is the case in Java, its meaning is less clear, and much of its intended simplicity lost.This paper explores the definition and implementation of safe futures for Java. One can think of safe futures as truly transparent annotations on method calls, which designate opportunities for concurrency. Serial programs can be made concurrent simply by replacing standard method calls with future invocations. Most significantly, even though some parts of the program are executed concurrently and may indeed operate on shared data, the semblance of serial execution is nonetheless preserved. Thus, program reasoning is simplified since data dependencies present in a sequential program are not violated in a version augmented with safe futures.Besides presenting a programming model and API for safe futures, we formalize the safety conditions that must be satisfied to ensure equivalence between a sequential Java program and its future-annotated counterpart. A detailed implementation study is also provided. Our implementation exploits techniques such as object versioning and task revocation to guarantee necessary safety conditions. We also present an extensive experimental evaluation of our implementation to quantify overheads and limitations. Our experiments indicate that for programs with modest mutation rates on shared data, applications can use futures to profitably exploit parallelism, without sacrificing safety.

Tracing garbage collectors traverse references from live program variables, transitively tracing out the closure of live objects. Memory accesses incurred during tracing are essentially random: a given object may contain references to any other object. Since application heaps are typically much larger than hardware caches, tracing results in many cache misses. Technology trends will make cache misses more important, so tracing is a prime target for prefetching.Simulation of Java benchmarks running with the Boehm-De-mers-Weiser mark-sweep garbage collector for a projected hardware platform reveal high tracing overhead (up to 65% of elapsed time), and that cache misses are a problem. Applying Boehm's default prefetching strategy yields improvements in execution time (16% on average with incremental/generational collection for GC-intensive benchmarks), but analysis shows that his strategy suffers from significant timing problems: prefetches that occur too early or too late relative to their matching loads. This analysis drives development of a new prefetching strategy that yields up to three times the performance improvement of Boehm's strategy for GC-intensive benchmark (27% average speedup), and achieves performance close to that of perfect timing ie, few misses for tracing accesses) on some benchmarks. Validating these simulation results with live runs on current hardware produces average speedup of 6% for the new strategy on GC-intensive benchmarks with a GC configuration that tightly controls heap growth. In contrast, Boehm's default prefetching strategy is ineffective on this platform.

Priority inversion occurs in concurrent programs when low-priority threads hold shared resources needed by some high-priority thread, causing them to block indefinitely. Shared resources are usually guarded by low-level synchronization primitives such as mutual-exclusion locks, semaphores, or monitors. There are two existing solutions to priority inversion. The first, establishing high-level scheduling invariants over synchronization primitives to eliminate priority inversion a priori, is difficult in practice and undecidable in general. Alternatively, run-time avoidance mechanisms such as priority inheritance still force high-priority threads to wait until desired resources are released. We describe a novel compiler and run-time solution to the problem of priority inversion, along with experimental evaluation of its effectiveness. Our approach allows preemption of any thread holding a resource needed by higher-priority threads, forcing it to release its claim on the resource, roll back its execution to the point at which the shared resource was first acquired, and discard any updates made in the interim. The compiler inserts code at synchronization points, permitting rollback of thread execution, and efficient revocation of interim updates. Our design and implementation are realized in the context of IBM's Jikes RVM, a high-quality compiler and runtime system for Java. Our performance results show that throughput of high-priority threads using our scheme can be improved by 30% to 100% when compared with a classical scheduler that does not address priority inversion.

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.

Abstract Partial redundancy elimination (PRE) is a program transformation that identifies and eliminates expressions that are redundant on at least one (but not necessarily all) execution paths of a program without increasing any path length. Chow, Kennedy and co‐workers devised an algorithm (SSAPRE) for performing partial redundancy elimination on intermediate representations in static single assignment (SSA) form. The practicality of that algorithm is limited by the following concerns: (1) it makes assumptions about the namespace that are stronger than those of SSA form and that may not be valid if other optimizations have already been performed on the program; (2) if redundancies occur in nested expressions, the algorithm may expose but not eliminate them (requiring a second pass of the algorithm); (3) it misses cases covered by the state of the art in PRE; and (4) it is difficult to understand and implement. We present an algorithm (A‐SSAPRE) structurally similar to SSAPRE that uses anticipation rather than availability; this algorithm is simpler than SSAPRE, covers more cases, eliminates nested redundancies on a single pass, and makes no assumptions about the namespace. Copyright © 2004 John Wiley & Sons, Ltd.

Abstract Pointer traversals pose significant overhead to the execution of object‐oriented programs, since every access to an object's state requires a pointer dereference. Eliminating redundant pointer traversals reduces both instructions executed as well as redundant memory accesses to relieve pressure on the memory subsystem. We describe an approach to elimination of redundant access expressions that combines partial redundancy elimination (PRE) with type‐based alias analysis (TBAA). To explore the potential of this approach we have implemented an optimization framework for Java class files incorporating TBAA‐based PRE over pointer access expressions. The framework is implemented as a class‐file‐to‐class‐file transformer; optimized classes can then be run in any standard Java execution environment. Our experiments demonstrate improvements in the execution of optimized code for several Java benchmarks running in diverse execution environments: the standard interpreted JDK virtual machine, a virtual machine using ‘just‐in‐time’ compilation, and native binaries compiled off‐line (‘way‐ahead‐of‐time’). Overall, however, our experience is of mixed success with the optimizations, mainly because of the isolation between our optimizer and the underlying execution environments which prevents more effective cooperation between them. We isolate the impact of access path PRE using TBAA, and demonstrate that Java's requirement of precise exceptions can noticeably impact code‐motion optimizations like PRE. Copyright © 2001 John Wiley & Sons, Ltd.

We describe how reachability-based orthogonal persistence can be supported even in uncooperative implementations of languages such as C++ and Modula-3, and without modification to the compiler. Our scheme extends Bartlett's mostly-copying garbage collector to manage both transient objects and resident persistent objects, and to compute the reachability closure necessary for stabilization of the persistent heap. It has been implemented in our prototype of reachability-based persistence for Modula-3, yielding performance competitive with that of comparable, but non-orthogonal, persistent variants of C++. Experimental results, using the 007 object database benchmarks, reveal that the mostly-copying approach offers a straightforward path to efficient orthogonal persistence in these uncooperative environments. The results also characterize the performance of persistence implementations based on virtual memory protection primitives.

A key mechanism of a persistent programming language is its ability to detect and handle references to non-resident objects.Ideally, this mechanism should be hidden from the programmer, allowing the transparent manipulation of all data regardless of its potential lifetime.We term such a mechanism object faulting, in a deliberate analogy with page faulting in virtual memory systems.This paper presents a number of mechanisms for detecting and handling references to persistent objects, and evaluates their relative performance within an implementation of Persistent Smalltalk.

Many operating systems allow user programs to specify the protection level (inaccessible, read-only, read-write) of pages in their virtual memory address space, and to handle any protection violations that may occur. Such page-protection techniques have been exploited by several user-level algorithms for applications including generational garbage collection and persistent stores. Unfortunately, modern hardware has made efficient handling of page protection faults more difficult. Moreover, page-sized granularity may not match the natural granularity of a given application. In light of these problems, we reevaluate the usefulness of page-protection primitives in such applications, by comparing the performance of implementations that make use of the primitives with others that do not. Our results show that for certain applications software solutions outperform solutions that rely on page-protection or other related virtual memory primitives.

article Free Access Share on A comparative performance evaluation of write barrier implementation Authors: Antony L. Hosking View Profile , J. Eliot B. Moss View Profile , Darko Stefanovic View Profile Authors Info & Claims ACM SIGPLAN NoticesVolume 27Issue 10Oct. 1992 pp 92–109https://doi.org/10.1145/141937.141946Online:31 October 1992Publication History 66citation607DownloadsMetricsTotal Citations66Total Downloads607Last 12 Months12Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF