The need for increased memory capacity, which also needs to be affordable and sustainable, leads to the adoption of heterogeneous memory hierarchies, combining DRAM and NVM technologies. This work proposes a memory management methodology that relies on multi-objective optimization in terms of performance, energy consumption and impact on NVM’s lifetime, for applications deployed on heterogeneous (i.e., DRAM/NVM) memory systems. We propose a scalable and lightweight data structure exploration flow for supporting data type refinement based on access pattern analysis, enhanced with a weighted-based data placement decision support for multi-objective exploration and optimization. The evaluation of the methodology was performed both on emulated and real DRAM/NVM hardware for different applications and data placement algorithms. The experimental results show up to 58.7% lower execution time and 48.3% less energy consumption compared with the results obtained by the initial versions of the applications. Moreover, we observed 72.6% less NVM write operations, which can significantly extend the lifetime of the NVM memory. Finally, thorough evaluation shows that the methodology is flexible and scalable, as it can integrate different data placement algorithms and NVM technologies and requires reasonable exploration time.

In order to perform cutting-edge research like AI model training, a large amount of data needs to be accessed. However, data providers are often reluctant to share their data with researchers as these might contain personal data and thereby sharing may introduce serious risks with significant personal, institutional or societal impacts. Apart from the need to control these risks, data providers must also comply with regulations like GDPR, which creates an additional overhead that makes data sharing even less appealing to data providers. Technologies like anonymization can play a critical role when sharing data that may contain personal information by offering privacy preservation measures like face or license plate anonymization. Therefore, we propose a framework to support data sharing of personal data for research by integrating anonymization, risk assessment and automatic licence agreement generation. The framework offers a practical and efficient solution for organisations seeking to enhance data-sharing practices without compromising information security.

The emergence of memory systems that combine multiple memory technologies with alternative performance and energy characteristics are becoming mainstream. Existing data placement strategies evolve to map application requirements to the underlying heterogeneous memory systems. In this work, we propose a memory management methodology that leverages a data structure refinement approach to improve data placement results, in terms of execution time and energy consumption. The methodology is evaluated on three machine learning algorithms deployed on various NVM technologies, both on emulated and on real DRAM/NVM systems. Results show execution time improvement up to 57% and energy consumption gains up to 41%.

The Intel Optane DC Persistent Memory (DCPM) is an attractive novel technology for building storage systems for data intensive HPC applications, as it provides lower cost per byte, low standby power and larger capacities than DRAM, with comparable latency. This work provides an in-depth evaluation of the energy consumption of the Optane DCPM, using well-established indexes specifically designed to address the challenges and constraints of the persistent memories. We study the energy efficiency of the Optane DCPM for several indexing data structures and for the LevelDB key-value store, under different types of YCSB workloads. By integrating an Optane DCPM in a memory system, the energy drops by 71.2% and the throughput increases by 37.3% for the LevelDB experiments, compared to a typical SSD storage solution.

Modern embedded devices require highly optimized code in order to efficiently run the wide range of applications they are designed for. However, most modern applications are getting more and more dynamic, which at the software level, translates in the use of dynamic data structures like dynamic arrays and lists. State of the art solutions for the optimization of these dynamic structures operate with code written in C++ or higher level languages. This work presents an automatic framework for the dynamic data structure optimization of applications written in C. The major advantages of this framework are the rich set of ready-to-use data structures in C that a developer can use to focus on the application itself and the fact that it targets applications in C rather than a higher level language. Moreover, the communication with existing state of the art optimization mechanisms in C++ provides the flexibility in optimization and the customization in the final solutions, needed for modern applications from many domains. The real world applicability of the proposed framework is proved by integrating it with well-known benchmarks written in C. Experimental results show possible reduction of data accesses by 7% and memory footprint by 33%.

Embedded consumer devices are increasing their capabilities and can now implement new multimedia applications reserved only for powerful desktops a few years ago. These applications share complex and intensive dynamic memory use. Thus, dynamic memory optimizations are a requirement when porting these applications. Within these optimizations, the refinement of the Dynamically (de)allocated Data Type (or DDT) implementations is one of the most important and difficult parts for an efficient mapping onto low-power embedded devices. In this paper, we describe a new automatic optimization approach for the DDTs of object-oriented multimedia applications. It is based on an analytical pre-characterization of the possible elementary DDT blocks, and a multi-objective genetic algorithm to explore the design space and to select the best implementation according to different optimization criteria (i.e., memory accesses, memory footprint and energy consumption). Our results in real-life multimedia applications show that the best implementations of DDTs can be obtained in an automated way in few hours, while typically designers would require days to find a suitable implementation, achieving important savings in exploration time with respect to other state-of-the-art heuristics-based optimization methods for this task.