Control theory for principled heap sizing

We propose a new, principled approach to adaptive heap sizing based on control theory. We review current state-of-the-art heap sizing mechanisms, as deployed in Jikes RVM and HotSpot. We then formulate heap sizing as a control problem, apply and tune a standard controller algorithm, and evaluate its performance on a set of well-known benchmarks. We find our controller adapts the heap size more responsively than existing mechanisms. This responsiveness allows tighter virtual machine memory footprints while preserving target application throughput, which is ideal for both embedded and utility computing domains. In short, we argue that formal, systematic approaches to memory management should be replacing ad-hoc heuristics as the discipline matures. Control-theoretic heap sizing is one such systematic approach

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

Shared-Memory Parallel Maximal Clique Enumeration

We present shared-memory parallel methods for Maximal Clique Enumeration (MCE) from a graph. MCE is a fundamental and well-studied graph analytics task, and is a widely used primitive for identifying dense structures in a graph. Due to its computationally intensive nature, parallel methods are imperative for dealing with large graphs. However, surprisingly, there do not yet exist scalable and parallel methods for MCE on a shared-memory parallel machine. In this work, we present efficient shared-memory parallel algorithms for MCE, with the following properties: (1) the parallel algorithms are provably work-efficient relative to a state-of-the-art sequential algorithm (2) the algorithms have a provably small parallel depth, showing that they can scale to a large number of processors, and (3) our implementations on a multicore machine shows a good speedup and scaling behavior with increasing number of cores, and are substantially faster than prior shared-memory parallel algorithms for MCE.Comment: 10 pages, 3 figures, proceedings of the 25th IEEE International Conference on. High Performance Computing, Data, and Analytics (HiPC), 201

Ada (trademark) projects at NASA. Runtime environment issues and recommendations

Ada practitioners should use this document to discuss and establish common short term requirements for Ada runtime environments. The major current Ada runtime environment issues are identified through the analysis of some of the Ada efforts at NASA and other research centers. The runtime environment characteristics of major compilers are compared while alternate runtime implementations are reviewed. Modifications and extensions to the Ada Language Reference Manual to address some of these runtime issues are proposed. Three classes of projects focusing on the most critical runtime features of Ada are recommended, including a range of immediately feasible full scale Ada development projects. Also, a list of runtime features and procurement issues is proposed for consideration by the vendors, contractors and the government