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

Garbage collection in a large, distributed object store

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.Includes bibliographical references (p. 93-97).by Umesh Maheshwari.Ph.D

Models of higher-order, type-safe, distributed computation over autonomous persistent object stores

A remote procedure call (RPC) mechanism permits the calling of procedures in another address space. RPC is a simple but highly effective mechanism for interprocess communication and enjoys nowadays a great popularity as a tool for building distributed applications. This popularity is partly a result of their overall simplicity but also partly a consequence of more than 20 years of research in transpaxent distribution that have failed to deliver systems that meet the expectations of real-world application programmers. During the same 20 years, persistent systems have proved their suitability for building complex database applications by seamlessly integrating features traditionally found in database management systems into the programming language itself. Some research. effort has been invested on distributed persistent systems, but the outcomes commonly suffer from the same problems found with transparent distribution. In this thesis I claim that a higher-order persistent RPC is useful for building distributed persistent applications. The proposed mechanism is: realistic in the sense that it uses current technology and tolerates partial failures; understandable by application programmers; and general to support the development of many classes of distributed persistent applications. In order to demonstrate the validity of these claims, I propose and have implemented three models for distributed higher-order computation over autonomous persistent stores. Each model has successively exposed new problems which have then been overcome by the next model. Together, the three models provide a general yet simple higher-order persistent RPC that is able to operate in realistic environments with partial failures. The real strength of this thesis is the demonstration of realism and simplicity. A higherorder persistent RPC was not only implemented but also used by programmers without experience of programming distributed applications. Furthermore, a distributed persistent application has been built using these models which would not have been feasible with a traditional (non-persistent) programming language

Graph databases and their application to the Italian Business Register for efficient search of relationships among companies

We studied and tested three of the major graph databases, and we compared them with a relational database. We worked on a dataset representing equity participations among companies, and we found out that the strong points of graph databases are: the purposely designed storage techniques; and their query languages. The main performance increments have been obtained when heavy graph situations are queried; for simpler situations and queries, a relational database performs equally wellope

Client cache management in a distributed object database

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.Includes bibliographical references (leaves 137-143).by Mark Stuart Day.Ph.D

Management of Long-Running High-Performance Persistent Object Stores

The popularity of object-oriented programming languages, such as Java and C++, for large application development has stirred an interest in improved technologies for high-performance, reliable, and scalable object storage. Such storage systems are typically referred to as Persistent Object Stores. This thesis describes the design and implementation of Sphere, a new persistent object store developed at the University of Glasgow, Scotland. The requirements for Sphere included high performance, support for transactional multi-threaded loads, scalability, extensibility, portability, reliability, referential integrity via the use of disk garbage collection, provision for flexible schema evolution, and minimised interaction with the mutator. The Sphere architecture is split into two parts: the core and the application-specific customisations. The core was designed to be modular, in order to encourage research and experimentation, and to be as light-weight as possible, in an attempt to achieve high performance through simplicity. The customisation part includes the code that deals with and is optimised for the specific load of the application that Sphere has to support: object formats, free-space management, etc. Even though specialising this part of the store is not trivial, it has the benefit that the interaction between the mutator and Sphere is direct and more efficient, as translation layers are not necessary. Major design decisions for Sphere included (i) splitting the store into partitions, to facilitate incremental disk garbage collection and schema evolution, (ii) using a flexible two-level free-space management, (Hi) introducing a three-dimensional method-dispatch matrix to invoke store operations, which contributes to Sphere's ease-of-extensibility, (iv) adopting a logical addressing scheme, to allow straightforward object and partition relocation, (v) requiring that Sphere can identify reference fields inside objects, so that it does not have to interact with the mutator in order to do so, and (vi) adopting the well-known ARIES recovery algorithm to ensure fault-tolerance. The thesis contains a detailed overview of Sphere and the context in which it was developed. Then, it concentrates on two areas that were explored using Sphere as the implementation platform. First, bulk object-loading issues are discussed and the Ghosted Allocation promotion algorithm is described. This algorithm was designed to allocate large numbers of objects to a store efficiently and with minimal log traffic and was evaluated using large-scale experiments. Second, the disk garbage collection framework of Sphere is overviewed and the implemented compacting, relocating garbage collector is described, along with the model of synchronisation with the mutator

Sparsely Faceted Arrays: A Mechanism Supporting Parallel Allocation, Communication, and Garbage Collection

Conventional parallel computer architectures do not provide support for non-uniformly distributed objects. In this thesis, I introduce sparsely faceted arrays (SFAs), a new low-level mechanism for naming regions of memory, or facets, on different processors in a distributed, shared memory parallel processing system. Sparsely faceted arrays address the disconnect between the global distributed arrays provided by conventional architectures (e.g. the Cray T3 series), and the requirements of high-level parallel programming methods that wish to use objects that are distributed over only a subset of processing elements. A sparsely faceted array names a virtual globally-distributed array, but actual facets are lazily allocated. By providing simple semantics and making efficient use of memory, SFAs enable efficient implementation of a variety of non-uniformly distributed data structures and related algorithms. I present example applications which use SFAs, and describe and evaluate simple hardware mechanisms for implementing SFAs. Keeping track of which nodes have allocated facets for a particular SFA is an important task that suggests the need for automatic memory management, including garbage collection. To address this need, I first argue that conventional tracing techniques such as mark/sweep and copying GC are inherently unscalable in parallel systems. I then present a parallel memory-management strategy, based on reference-counting, that is capable of garbage collecting sparsely faceted arrays. I also discuss opportunities for hardware support of this garbage collection strategy. I have implemented a high-level hardware/OS simulator featuring hardware support for sparsely faceted arrays and automatic garbage collection. I describe the simulator and outline a few of the numerous details associated with a "real" implementation of SFAs and SFA-aware garbage collection. Simulation results are used throughout this thesis in the evaluation of hardware support mechanisms