A Parallel Evolutionary Algorithm to Optimize Dynamic Memory Managers in Embedded Systems

For the last 30 years, several dynamic memory managers (DMMs) have been proposed. Such DMMs include first fit, best fit, segregated fit and buddy systems. Since the performance, memory usage and energy consumption of each DMM differs, software engineers often face difficult choices in selecting the most suitable approach for their applications. This issue has special impact in the field of portable consumer embedded systems, that must execute a limited amount of multimedia applications (e.g., 3D games, video players, signal processing software, etc.), demanding high performance and extensive memory usage at a low energy consumption. Recently, we have developed a novel methodology based on genetic programming to automatically design custom DMMs, optimizing performance, memory usage and energy consumption. However, although this process is automatic and faster than state-of-the-art optimizations, it demands intensive computation, resulting in a time-consuming process. Thus, parallel processing can be very useful to enable to explore more solutions spending the same time, as well as to implement new algorithms. In this paper we present a novel parallel evolutionary algorithm for DMMs optimization in embedded systems, based on the Discrete Event Specification (DEVS) formalism over a Service Oriented Architecture (SOA) framework. Parallelism significantly improves the performance of the sequential exploration algorithm. On the one hand, when the number of generations are the same in both approaches, our parallel optimization framework is able to reach a speed-up of 86.40% when compared with other state-of-the-art approaches. On the other, it improves the global quality (i.e., level of performance, low memory usage and low energy consumption) of the final DMM obtained in a 36.36% with respect to two well-known general-purpose DMMs and two state-of-the-art optimization methodologies

A Customisable Memory Management Framework for C++

Automatic garbage collection relieves programmers from the burden of managing memory themselves and several techniques have been developed that make garbage collection feasible in many situations, including real time applications or within traditional programming languages. However optimal performance cannot always be achieved by a uniform general purpose solution. Sometimes an algorithm exhibits a predictable pattern of memory usage that could be better handled specifically, delaying as much as possible the intervention of the general purpose collector. This leads to the requirement for algorithm specific customisation of the collector strategies. We present a dynamic memory management framework which can be customised to the needs of an algorithm, while preserving the convenience of automatic collection in the normal case. The Customisable Memory Manager (CMM) organises memory in multiple heaps. Each heap is an instance of a C++ class which abstracts and encapsulates a particular sto..

A customisable memory management framework for C++

Automatic garbage collection relieves programmers from the burden of managing memory themselves and several techniques have been developed that make garbage collection feasible in many situations, including real time applications or within traditional programming languages. However, optimal performance cannot always be achieved by a uniform general purpose solution. Sometimes an algorithm exhibits a predictable pattern of memory usage that could be better handled specifically, delaying as much as possible the intervention of the general purpose collector. This leads to the requirement for algorithm specific customisation of the collector strategies. We present a dynamic memory management framework which can be customised to the needs of an algorithm, while preserving the convenience of automatic collection in the normal case. The Customisable Memory Manager (CMM) organises memory in multiple heaps. Each heap is an instance of C++ class which abstracts and encapsulates a particular storage discipline. The default heap for collectable objects uses the technique of mostly copying garbage collection, providing good performance and memory compaction. Customisation of the collector is achieved exploiting object orientation by defining specialised versions of the collector methods for each heap class. The object-oriented interface to the collector enables coexistence and coordination among the various collectors as well as integration with traditional code unaware of garbage collection. The CMM is implemented in C++ without any special support in the language or the compiler. The techniques used in the CMM are general enough to be applicable also to other languages. The performance of the CMM is analysed and compared to other conservative collectors for C/C++ in various configurations