Dynamic Memory Management Design Methodology for Reduced Memory Footprint in Multimedia and Wireless Network Applications

New portable consumer embedded devices must execute multimedia and wireless network applications that demand extensive memory footprint. Moreover, they must heavily rely on Dynamic Memory (DM) due to the unpredictability of the input data (e.g. 3D streams features) and system behaviour (e.g. number of applications running concurrently defined by the user). Within this context, consistent design methodologies that can tackle efficiently the complex DM behaviour of these multimedia and network applications are in great need. In this paper, we present a new methodology that allows to design custom DM management mechanisms with a reduced memory footprint for such kind of dynamic applications. The experimental results in real case studies show that our methodology improves memory footprint 60% on average over current state-of-the-art DM managers

Automated Exploration of Pareto-optimal Configurations in Parameterized Dynamic Memory Allocation for Embedded Systems

New applications in embedded systems are becoming Increasingly dynamic. In addition to increased dynamism, they have massive data storage needs. Therefore, they rely heavily on dynamic, run-time memory allocation. The design and configuration of a dynamic memory allocation subsystem requires a big design effort, without always achieving the desired results. In this paper, we propose a fully automated exploration of dynamic memory allocation configurations. These configurations are fine tuned to the specific needs of applications with the use of a number of parameters. We assess the effectiveness of the proposed approach in two representative real-life case studies of the multimedia and wireless network domains and show up to 76% decrease in memory accesses and 66% decrease in memory footprint within the Pareto-optimal trade-off space

Template-Based Semi-Automatic Profiling of Multimedia Applications

Modern multimedia applications possess a very dynamic use of the memory hierarchy depending on the actual input, therefore requiring run-time profiling techniques to enable optimizations. Because they can contain hundreds of thousands of lines of complex object-oriented specifications, this constitutes a tedious time-consuming task since the addition of profile code is usually performed manually. In this paper, we present a high-level library-based approach for profiling both statically and dynamically defined variables using templates in C++. Our results in the visual texture coder of the MPEG-4 standard show that using the information it provides, we can easily achieve 70.56% energy savings and 19.22% memory access reduction

Reducing Memory Fragmentation with Performance-optimized Dynamic Memory Allocators in Network Applications

The needs for run-time data storage in modern wired and wireless network applications are increasing. Additionally, the nature of these applications is very dynamic, resulting in heavy reliance to dynamic memory allocation. The most significant problem in dynamic memory allocation is fragmentation, which can cause the system to run out of memory and crash, if it is left unchecked. The available dynamic memory allocation solutions are provided by the real time Operating Systems used in embedded or general-purpose systems. These state-of-the-art dynamic memory allocators are designed to satisfy the run-time memory requests of a wide range of applications. Contrary to most applications, network applications need to allocate too many different memory sizes (e.g. hundreds different sizes for packets) and have an extremely dynamic allocation and de-allocation behavior (e.g. unpredictable web-browsing activity). Therefore, the performance and the de-fragmentation efficiency of these allocators is limited. In this paper, we analyze all the important issues of fragmentation and the ways to reduce it in network applications, while keeping the performance of the dynamic memory allocator unaffected or even improving it. We propose highly customized dynamic memory allocators, which can be configured for specific network needs. We assess the effectiveness of the proposed approach in two representative real-life case studies of wired and wireless network applications. Finally, we show very significant reduction in memory fragmentation and increase in performance compared to state-of-the-art dynamic memory allocators utilized by real-time Operating System

Reducing Memory Fragmentation in Network Applications with Dynamic Memory Allocators Optimized for Performance

The needs for run-time data storage in modern wired and wireless network applications are increasing. Additionally, the nature of these applications is very dynamic, resulting in heavy reliance on dynamic memory allocation. The most significant problem in dynamic memory allocation is fragmentation, which can cause the system to run out of memory and crash, if it is left unchecked. The available dynamic memory allocation solutions are provided by the real-time Operating Systems used in embedded or general-purpose systems. These state-of-the-art dynamic memory allocators are designed to satisfy the run-time memory requests of a wide range of applications. Contrary to most applications, network applications need to allocate too many different memory sizes (e.g., hundreds different sizes for packets) and have an extremely dynamic allocation and de-allocation behavior (e.g., unpredictable web-browsing activity). Therefore, the performance and the de-fragmentation efficiency of these allocators is limited. In this paper, we analyze all the important issues of fragmentation and the ways to reduce it in network applications, while keeping the performance of the dynamic memory allocator unaffected or even improving it. We propose highly customized dynamic memory allocators, which can be configured for specific network needs. We assess the effectiveness of the proposed approach in three representative real-life case studies of wired and wireless network applications. Finally, we show very significant reduction in memory fragmentation and increase in performance compared to state-of-the-art dynamic memory allocators utilized by real-time Operating Systems

Power aware data and memory management for dynamic applications

In recent years, the semiconductor industry has turned its focus towards heterogeneous multiprocessor platforms. They are an economically viable solution for coping with the growing setup and manufacturing cost of silicon systems. Furthermore, their inherent flexibility perfectly supports the emerging market of interactive, mobile data and content services. The platform’s performance and energy depend largely on how well the data-dominated services are mapped on the memory subsystem. A crucial aspect thereby is how efficient data is transferred between the different memory layers. Several compilation techniques have been developed to optimally use the available bandwidth. Unfortunately, they do not take the interaction between multiple threads into account and do not deal with the dynamic behaviour of these novel applications. The main limitations of current techniques are outlined and an approach for dealing with them is introduced

Power-efficient data management for dynamic applications

In recent years, the semiconductor industry has turned its focus towards heterogeneous multi-processor platforms. They are an economically viable solution for coping with the growing setup and manufacturing cost of silicon systems. Furthermore, their inherent flexibility also perfectly supports the emerging market of interactive, mobile data and content services. The platform's performance and energy depend largely on how well the data-dominated services are mapped on the memory subsystem. A crucial aspect thereby is how efficient data is transferred between the different memory layers. Several compilation techniques have been developed to optimally use the available bandwidth. Unfortunately, they do not take the interaction between multiple threads running on the different processors into account, only locally optimize the bandwidth nor deal with the dynamic behavior of these applications. The contributions of this chapter are to outline the main limitations of current techniques and to introduce an approach for dealing with the dynamic multi-threaded of our application domain