Improving reliability of embedded systems through dynamic memory manager optimization using grammatical evolution

Microprocessors and Microsystems

Atienza

et al. 2011

Self Cite

For the last 30 years, a large variety of memory allocators have been proposed. Since performance, memory usage and energy consumption of each memory allocator differs, software engineers often face difficult choices in selecting the most suitable approach for their applications. To this end, custom allocators are developed from scratch, which is a difficult and error-prone process. This issue has special impact in the field of portable consumer embedded systems, that must execute a limited amount of multimedia applications, demanding high performance and extensive memory usage at a low energy consumption. This paper presents a flexible and efficient simulator to study Dynamic Memory Managers (DMMs), a composition of one or more memory allocators. This novel approach allows programmers to simulate custom and general DMMs, which can be composed without incurring any additional runtime overhead or additional programming cost. We show that this infrastructure simplifies DMM construction, mainly because the target application does not need to be compiled every time a new DMM must be evaluated and because we propose a structured method to search and build DMMs in an object-oriented fashion. Within a search procedure, the system designer can choose the ''best'' allocator by simulation for a particular target application and embedded system. In our evaluation, we show that our scheme delivers better performance, less memory usage and less energy consumption than single memory allocators.

Section: Experimental Methodsmentioning

confidence: 99%

Section: Dmm Simulation Frameworkmentioning

confidence: 99%

Simulation of high-performance memory allocators

Risco‐Martín

Microprocessors and Microsystems

Atienza

et al. 2011

Self Cite

“…Among all the previous works about DMM design, we focus our approach in the proposal followed by [1], [2], [14] and [7]. Hence, we consider a DMM as a set of free-blocks lists where different policies can be implemented.…”

Section: Design Space Of Dmmsmentioning

confidence: 99%

“…In our implementation, selection consists on a tournament where dominance is considered and, in case of mutual dominance between two individuals, crowding distance is applied. After that, the crossover and mutation operators are applied in the same way as the authors of [14] and [7] propose. Next, the objective evaluation is performed by using the simulator proposed by the authors of [15].…”

Section: New Moge Optimizationmentioning

confidence: 99%

“…Based on these proposals, several recent works proposed optimization methods to automatically obtain optimal custom DMMs through grammatical evolution [14,13,7]. In these works, the authors describe different implementations of optimization algorithms and contribute with several approaches like parallel implementations or reliability-aware optimizations.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multi-objective optimization of dynamic memory managers using grammatical evolution

Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation

Risco‐Martín

Atienza

et al. 2011

Self Cite

The dynamic memory manager (DMM) is a key element whose customization for a target application reports great benefits in terms of execution time, memory usage and energy consumption. Previous works presented algorithms to automatically obtain custom DMMs for a given application. Nevertheless, those approaches are based on grammatical evolution where the fitness is built as an aggregate objective function, which does not completely exploit the search space, returning the designer the DMM solution with best fitness. However, this approach may not find solutions that could fit in a concrete hardware platform due to a very low value of one of the objectives while the others remain high, which may represent a high fitness. In this work we present the first multi-objective optimization methodology applied to DMM optimization where the Pareto dominance is considered, thus providing the designer with a set of non-dominated DMM implementations on each optimization run. Our results show that the multi-objective optimization provides Pareto-optimal alternatives due to a better exploitation of the search space obtaining better hypervolume values than the aggregate objective function approach.

Structured grammatical evolution for glucose prediction in diabetic patients

Lourenço

Proceedings of the Genetic and Evolutionary Computation Conference

Hidalgo

et al. 2019

Self Cite

Structured grammatical evolution is a recent grammar-based genetic programming variant that tackles the main drawbacks of Grammatical Evolution, by relying on a one-to-one mapping between each gene and a non-terminal symbol of the grammar. It was applied, with success, in previous works with a set of classical benchmarks problems. However, assessing performance on hard real-world problems is still missing. In this paper, we fill in this gap, by analyzing the performance of SGE when generating predictive models for the glucose levels of diabetic patients. Our algorithm uses features that take into account the past glucose values, insulin injections, and the amount of carbohydrate ingested by a patient. The results show that SGE can evolve models that can predict the glucose more accurately when compared with previous grammarbased approaches used for the same problem. Additionally, we also show that the models tend to be more robust, since the behavior in the training and test data is very similar, with a small variance. CCS CONCEPTS • Computing methodologies → Artificial intelligence; • Applied computing → Health informatics; • Theory of computation → Algorithm design techniques;