The measured cost of conservative garbage collection
Software—Practice & Experience
Composing high-performance memory allocators
Proceedings of the ACM SIGPLAN 2001 conference on Programming language design and implementation
Reconsidering custom memory allocation
OOPSLA '02 Proceedings of the 17th ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
Dynamic Storage Allocation: A Survey and Critical Review
IWMM '95 Proceedings of the International Workshop on Memory Management
The design and analysis of a quantitative simulator for dynamic memory management
Journal of Systems and Software
Pin: building customized program analysis tools with dynamic instrumentation
Proceedings of the 2005 ACM SIGPLAN conference on Programming language design and implementation
Systematic dynamic memory management design methodology for reduced memory footprint
ACM Transactions on Design Automation of Electronic Systems (TODAES)
ICSEA '06 Proceedings of the International Conference on Software Engineering Advances
DSD '08 Proceedings of the 2008 11th EUROMICRO Conference on Digital System Design Architectures, Methods and Tools
Efficient system-level prototyping of power-aware dynamic memory managers for embedded systems
Integration, the VLSI Journal - Special issue: Low-power design techniques
Data cache-energy and throughput models: design exploration for embedded processors
EURASIP Journal on Embedded Systems - Special issue on design and architectures for signal and image processing
Simulation of High-Performance Memory Allocators
DSD '10 Proceedings of the 2010 13th Euromicro Conference on Digital System Design: Architectures, Methods and Tools
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Modern portable devices execute multimedia applications that exhibit high resource utilization. To efficiently execute these applications in embedded systems, the dynamic memory subsystem needs to be optimized. This complex task can be tackled designing custom dynamic memory management mechanisms. Currently, several automatic methodologies to optimize custom Dynamic Memory Managers (DMMs) have been proposed. However, these approaches are mainly related to improve application performance. In this paper we propose a methodology to automatically evaluate the impact of any DMM into an application considering four different metrics: performance, memory usage, temperature and energy consumption. This methodology is applied to Lea, a well-known general-purpose memory allocator. Our experimental results over five different memory-intensive applications show that, on average, Lea consumes a 43.25% and 22.90% of execution time and memory usage, respectively. In addition, the memory temperature and energy consumed, related only to the memory device, are increased by 0.39% and 0.48%, respectively.