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

Optimizing scoped and immortal memory management in real-time java

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The Real-Time Specification for Java (RTSJ) introduces a new memory management model which avoids interfering with the garbage collection process and achieves better deterministic behaviour. In addition to the heap memory, two types of memory areas are provided - immortal and scoped. The research presented in this Thesis aims to optimize the use of the scoped and immortal memory model in RTSJ applications. Firstly, it provides an empirical study of the impact of scoped memory on execution time and memory consumption with different data objects allocated in scoped memory areas. It highlights different characteristics for the scoped memory model related to one of the RTSJ implementations (SUN RTS 2.2). Secondly, a new RTSJ case study which integrates scoped and immortal memory techniques to apply different memory models is presented. A simulation tool for a real-time Java application is developed which is the first in the literature that shows scoped memory and immortal memory consumption of an RTSJ application over a period of time. The simulation tool helps developers to choose the most appropriate scoped memory model by monitoring memory consumption and application execution time. The simulation demonstrates that a developer is able to compare and choose the most appropriate scoped memory design model that achieves the least memory footprint. Results showed that the memory design model with a higher number of scopes achieved the least memory footprint. However, the number of scopes per se does not always indicate a satisfactory memory footprint; choosing the right objects/threads to be allocated into scopes is an important factor to be considered. Recommendations and guidelines for developing RTSJ applications which use a scoped memory model are also provided. Finally, monitoring scoped and immortal memory at runtime may help in catching possible memory leaks. The case study with the simulation tool developed showed a space overhead incurred by immortal memory. In this research, dynamic code slicing is also employed as a debugging technique to explore constant increases in immortal memory. Two programming design patterns are presented for decreasing immortal memory overheads generated by specific data structures. Experimental results showed a significant decrease in immortal memory consumption at runtime

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

Subheap-Augmented Garbage Collection

Automated memory management avoids the tedium and danger of manual techniques. However, as no programmer input is required, no widely available interface exists to permit principled control over sometimes unacceptable performance costs. This dissertation explores the idea that performance-oriented languages should give programmers greater control over where and when the garbage collector (GC) expends effort. We describe an interface and implementation to expose heap partitioning and collection decisions without compromising type safety. We show that our interface allows the programmer to encode a form of reference counting using Hayes\u27 notion of key objects. Preliminary experimental data suggests that our proposed mechanism can avoid high overheads suffered by tracing collectors in some scenarios, especially with tight heaps. However, for other applications, the costs of applying subheaps---in human effort and runtime overheads---remain daunting

A non-projective greedy dependency parser with bidirectional LSTMs

The LyS-FASTPARSE team presents BIST-COVINGTON, a neural implementation of the Covington (2001) algorithm for non-projective dependency parsing. The bidirectional LSTM approach by Kipperwasser and Goldberg (2016) is used to train a greedy parser with a dynamic oracle to mitigate error propagation. The model participated in the CoNLL 2017 UD Shared Task. In spite of not using any ensemble methods and using the baseline segmentation and PoS tagging, the parser obtained good results on both macro-average LAS and UAS in the big treebanks category (55 languages), ranking 7th out of 33 teams. In the all treebanks category (LAS and UAS) we ranked 16th and 12th. The gap between the all and big categories is mainly due to the poor performance on four parallel PUD treebanks, suggesting that some `suffixed' treebanks (e.g. Spanish-AnCora) perform poorly on cross-treebank settings, which does not occur with the corresponding `unsuffixed' treebank (e.g. Spanish). By changing that, we obtain the 11th best LAS among all runs (official and unofficial). The code is made available at https://github.com/CoNLL-UD-2017/LyS-FASTPARSEComment: 12 pages, 2 figures, 5 table

Optimization and Training of Generational Garbage Collectors

Garbage collectors are nearly ubiquitous in modern programming languages, and we want to minimize the cost they impose in terms of time and space. Generally, a collector waits until its space is full and then performs a collection to reclaim needed memory. However, this is not the only option; a collection could be performed early when some free space remains. For copying collectors, which are what we consider here, the system must traverse the graph of live objects and copy them, so the cost of a collection is proportional to the volume of objects that are live. Since this value fluctuates during a program\u27s execution, a collector can minimize its cost by carefully choosing the points at which it collects. We help to realize this goal in two ways. First, by developing an algorithm that analyzes after-the-fact traces and computes optimal collection points, we can explore the theoretical limits of garbage collectors. This gives insight into what performance gains are possible, and can guide future collector development into areas that could be most fruitful. Second, we use techniques from machine learning to find improved garbage collection policies that could be implemented in real systems. The optimal collection points provide ground truth from which a model can learn