Exploration of Dynamic Memory

Since the advent of the Java programming language and the development of real-time garbage collection, Java has become an option for implementing real-time applications. The memory management choices provided by real-time garbage collection allow for real-time eJava developers to spend more of their time implementing real-time solutions. Unfortunately, the real-time community is not convinced that real-time garbage collection works in managing memory for Java applications deployed in a real-time context. Consequently, the Real-Time for Java Expert Group formulated the Real-Time Specification for Java (RTSJ) standards to make Java a real-time programming language. In lieu of garbage collection, the RTSJ proposed a new memory model called scopes, and a new type of thread called NoHeapRealTimeThread (NHRT), which takes advantage of scopes. While scopes and NHRTs promise predictable allocation and deallocation behaviors, no asymptotic studies have been conducted to investigate the costs associated with these technologies. To understand the costs associated with using these technologies to manage memory, computations and analyses of time and space overheads associated with scopes and NHRTs are presented. These results provide a framework for comparing the RTSJ’s memory management model with real-time garbage collection. Another facet of this research concerns the optimization of novel approaches to garbage collection on multiprocessor systems. Such approaches yield features that are suitable for real-time systems. Although multiprocessor, concurrent garbage collection is not the same as real-time garbage collection, advancements in multiprocessor concurrent garbage collection have demonstrated the feasibility of building low latency multiprocessor real-time garbage collectors. In the nineteen-sixties, only three garbage collection schemes were available, namely reference counting garbage collection, mark-sweep garbage collection, and copying garbage collection. These classical approaches gave new insight into the discipline of memory management and inspired researchers to develop new, more elaborate memory-management techniques. Those insights resulted in a plethora of automatic memory management algorithms and techniques, and a lack of uniformity in the language used to reason about garbage collection. To bring a sense of uniformity to the language used to reason about garbage collection technologies, a taxonomy for comparing garbage collection technologies is presented

Garbage Collection for Java Distributed Objects

We present a distributed garbage collection algorithm for Java distributed objects using the object model provided by the Java Support for Distributed Objects (JSDA) object model and using weak references in Java. The algorithm can also be used for any other Java based distributed object models that use the stub-skeleton paradigm. Furthermore, the solution could also be applied to any language that supports weak references as a mean of interaction with the local garbage collector. We also give a formal definition and a proof of correctness for the proposed algorithm

Formal Derivation of Concurrent Garbage Collectors

Concurrent garbage collectors are notoriously difficult to implement correctly. Previous approaches to the issue of producing correct collectors have mainly been based on posit-and-prove verification or on the application of domain-specific templates and transformations. We show how to derive the upper reaches of a family of concurrent garbage collectors by refinement from a formal specification, emphasizing the application of domain-independent design theories and transformations. A key contribution is an extension to the classical lattice-theoretic fixpoint theorems to account for the dynamics of concurrent mutation and collection.Comment: 38 pages, 21 figures. The short version of this paper appeared in the Proceedings of MPC 201

A fast analysis for thread-local garbage collection with dynamic class loading

Long-running, heavily multi-threaded, Java server applications make stringent demands of garbage collector (GC) performance. Synchronisation of all application threads before garbage collection is a significant bottleneck for JVMs that use native threads. We present a new static analysis and a novel GC framework designed to address this issue by allowing independent collection of thread-local heaps. In contrast to previous work, our solution safely classifies objects even in the presence of dynamic class loading, requires neither write-barriers that may do unbounded work, nor synchronisation, nor locks during thread-local collections; our analysis is sufficiently fast to permit its integration into a high-performance, production-quality virtual machine

Down for the Count? Getting Reference Counting Back in the Ring

Reference counting and tracing are the two fundamental approaches that have underpinned garbage collection since 1960. However, despite some compelling advantages, reference counting is almost completely ignored in implementations of high performance systems today. In this paper we take a detailed look at reference counting to understand its behavior and to improve its performance. We identify key design choices for reference counting and analyze how the behavior of a wide range of benchmarks might affect design decisions. As far as we are aware, this is the first such quantitative study of reference counting. We use insights gleaned from this analysis to introduce a number of optimizations that significantly improve the performance of reference counting. We find that an existing modern implementation of reference counting has an average 30% overhead compared to tracing, and that in combination, our optimizations are able to completely eliminate that overhead. This brings the performance of reference counting on par with that of a well tuned mark-sweep collector. We keep our in-depth analysis of reference counting as general as possible so that it may be useful to other garbage collector implementers. Our finding that reference counting can be made directly competitive with well tuned mark-sweep should shake the community's prejudices about reference counting and perhaps open new opportunities for exploiting reference counting's strengths, such as localization and immediacy of reclamation

ThreadScan: Automatic and Scalable Memory Reclamation

The concurrent memory reclamation problem is that of devising a way for a deallocating thread to verify that no other concurrent threads hold references to a memory block being deallocated. To date, in the absence of automatic garbage collection, there is no satisfactory solution to this problem. Existing tracking methods like hazard pointers, reference counters, or epoch-based techniques like RCU, are either prohibitively expensive or require significant programming expertise, to the extent that implementing them efficiently can be worthy of a publication. None of the existing techniques are automatic or even semi-automated. In this paper, we take a new approach to concurrent memory reclamation: instead of manually tracking access to memory locations as done in techniques like hazard pointers, or restricting shared accesses to specific epoch boundaries as in RCU, our algorithm, called ThreadScan, leverages operating system signaling to automatically detect which memory locations are being accessed by concurrent threads. Initial empirical evidence shows that ThreadScan scales surprisingly well and requires negligible programming effort beyond the standard use of Malloc and Free

Automated Verification of Practical Garbage Collectors

Garbage collectors are notoriously hard to verify, due to their low-level interaction with the underlying system and the general difficulty in reasoning about reachability in graphs. Several papers have presented verified collectors, but either the proofs were hand-written or the collectors were too simplistic to use on practical applications. In this work, we present two mechanically verified garbage collectors, both practical enough to use for real-world C# benchmarks. The collectors and their associated allocators consist of x86 assembly language instructions and macro instructions, annotated with preconditions, postconditions, invariants, and assertions. We used the Boogie verification generator and the Z3 automated theorem prover to verify this assembly language code mechanically. We provide measurements comparing the performance of the verified collector with that of the standard Bartok collectors on off-the-shelf C# benchmarks, demonstrating their competitiveness

Garbage Collection for General Graphs

Garbage collection is moving from being a utility to a requirement of every modern programming language. With multi-core and distributed systems, most programs written recently are heavily multi-threaded and distributed. Distributed and multi-threaded programs are called concurrent programs. Manual memory management is cumbersome and difficult in concurrent programs. Concurrent programming is characterized by multiple independent processes/threads, communication between processes/threads, and uncertainty in the order of concurrent operations. The uncertainty in the order of operations makes manual memory management of concurrent programs difficult. A popular alternative to garbage collection in concurrent programs is to use smart pointers. Smart pointers can collect all garbage only if developer identifies cycles being created in the reference graph. Smart pointer usage does not guarantee protection from memory leaks unless cycle can be detected as process/thread create them. General garbage collectors, on the other hand, can avoid memory leaks, dangling pointers, and double deletion problems in any programming environment without help from the programmer. Concurrent programming is used in shared memory and distributed memory systems. State of the art shared memory systems use a single concurrent garbage collector thread that processes the reference graph. Distributed memory systems have very few complete garbage collection algorithms and those that exist use global barriers, are centralized and do not scale well. This thesis focuses on designing garbage collection algorithms for shared memory and distributed memory systems that satisfy the following properties: concurrent, parallel, scalable, localized (decentralized), low pause time, high promptness, no global synchronization, safe, complete, and operates in linear time