Distributed Saturation

The Saturation algorithm for symbolic state-space generation, has been a recent break-through in the exhaustive veri cation of complex systems, in particular globally-asyn- chronous/locally-synchronous systems. The algorithm uses a very compact Multiway Decision Diagram (MDD) encoding for states and the fastest symbolic exploration algo- rithm to date. The distributed version of Saturation uses the overall memory available on a network of workstations (NOW) to efficiently spread the memory load during the highly irregular exploration. A crucial factor in limiting the memory consumption during the symbolic state-space generation is the ability to perform garbage collection to free up the memory occupied by dead nodes. However, garbage collection over a NOW requires a nontrivial communication overhead. In addition, operation cache policies become critical while analyzing large-scale systems using the symbolic approach. In this technical report, we develop a garbage collection scheme and several operation cache policies to help on solving extremely complex systems. Experiments show that our schemes improve the performance of the original distributed implementation, SmArTNow, in terms of time and memory efficiency

Deriving distributed garbage collectors from distributed termination algorithms

This thesis concentrates on the derivation of a modularised version of the DMOS distributed garbage collection algorithm and the implementation of this algorithm in a distributed computational environment. DMOS appears to exhibit a unique combination of attractive characteristics for a distributed garbage collector but the original algorithm is known to contain a bug and, previous to this work, lacks a satisfactory, understandable implementation. The relationship between distributed termination detection algorithms and distributed garbage collectors is central to this thesis. A modularised DMOS algorithm is developed using a previously published distributed garbage collector derivation methodology that centres on mapping centralised collection schemes to distributed termination detection algorithms. In examining the utility and suitability of the derivation methodology, a family of six distributed collectors is developed and an extension to the methodology is presented. The research work described in this thesis incorporates the definition and implementation of a distributed computational environment based on the ProcessBase language and a generic definition of a previously unimplemented distributed termination detection algorithm called Task Balancing. The role of distributed termination detection in the DMOS collection mechanisms is defined through a process of step-wise refinement. The implementation of the collector is achieved in two stages; the first stage defines the implementation of two distributed termination mappings with the Task Balancing algorithm; the second stage defines the DMOS collection mechanisms

System Description for a Scalable, Fault-Tolerant, Distributed Garbage Collector

We describe an efficient and fault-tolerant algorithm for distributed cyclic garbage collection. The algorithm imposes few requirements on the local machines and allows for flexibility in the choice of local collector and distributed acyclic garbage collector to use with it. We have emphasized reducing the number and size of network messages without sacrificing the promptness of collection throughout the algorithm. Our proposed collector is a variant of back tracing to avoid extensive synchronization between machines. We have added an explicit forward tracing stage to the standard back tracing stage and designed a tuned heuristic to reduce the total amount of work done by the collector. Of particular note is the development of fault-tolerant cooperation between traces and a heuristic that aggressively reduces the set of suspect objects.Comment: 47 pages, LaTe

Lock-free Concurrent Data Structures

Concurrent data structures are the data sharing side of parallel programming. Data structures give the means to the program to store data, but also provide operations to the program to access and manipulate these data. These operations are implemented through algorithms that have to be efficient. In the sequential setting, data structures are crucially important for the performance of the respective computation. In the parallel programming setting, their importance becomes more crucial because of the increased use of data and resource sharing for utilizing parallelism. The first and main goal of this chapter is to provide a sufficient background and intuition to help the interested reader to navigate in the complex research area of lock-free data structures. The second goal is to offer the programmer familiarity to the subject that will allow her to use truly concurrent methods.Comment: To appear in "Programming Multi-core and Many-core Computing Systems", eds. S. Pllana and F. Xhafa, Wiley Series on Parallel and Distributed Computin

A Cyclic Distributed Garbage Collector for Network Objects

This paper presents an algorithm for distributed garbage collection and outlines its implementation within the Network Objects system. The algorithm is based on a reference listing scheme, which is augmented by partial tracing in order to collect distributed garbage cycles. Processes may be dynamically organised into groups, according to appropriate heuristics, to reclaim distributed garbage cycles. The algorithm places no overhead on local collectors and suspends local mutators only briefly. Partial tracing of the distributed graph involves only objects thought to be part of a garbage cycle: no collaboration with other processes is required. The algorithm offers considerable flexibility, allowing expediency and fault-tolerance to be traded against completeness

Relating goal scheduling, precedence, and memory management in and-parallel execution of logic programs

The interactions among three important issues involved in the implementation of logic programs in parallel (goal scheduling, precedence, and memory management) are discussed. A simplified, parallel memory management model and an efficient, load-balancing goal scheduling strategy are presented. It is shown how, for systems which support "don't know" non-determinism, special care has to be taken during goal scheduling if the space recovery characteristics of sequential systems are to be preserved. A solution based on selecting only "newer" goals for execution is described, and an algorithm is proposed for efficiently maintaining and determining precedence relationships and variable ages across parallel goals. It is argued that the proposed schemes and algorithms make it possible to extend the storage performance of sequential systems to parallel execution without the considerable overhead previously associated with it. The results are applicable to a wide class of parallel and coroutining systems, and they represent an efficient alternative to "all heap" or "spaghetti stack" allocation models