Energy efficient HPC network topologies with on/off links

[EN] Energy efficiency is a must in today HPC systems. To achieve this goal, a holistic design based on the use of power-aware components should be performed. One of the key components of an HPC system is the high-speed interconnect. In this paper, we compare and evaluate several design options for the interconnection network of an HPC system, including torus, fat-trees and dragonflies. State of the art low power modes are also used in the interconnection networks. The paper does not only consider energy efficiency at the interconnection network level but also at the system as a whole.The analysis is performed by using a simple yet realistic power model of the system. The model has been adjusted using actual power consumption values measured on a real system. Using this model, realistic multi-job trace-based workloads have been used, obtaining the execution time and energy consumed. The results are presented to ease choosing a system, depending on which parameter, performance or energy consumption, receives the most importance.This work has been supported by the Spanish Ministerio de Ciencia e Innovacion (MICINN, formerly MINECO) , and the European Commission (FEDER funds) under the projects PID2019- 105903RB-100 and PID2021-123627OB-C5, and by Junta de Comunidades de Castilla -La Mancha under the project SBPLY/21/180501/000248.Andújar-Muñoz, FJ.; Coll, S.; Alonso Díaz, M.; Martínez-Rubio, J.; López Rodríguez, PJ.; Sánchez García, JL.; Alfaro Cortés, FJ. (2023). Energy efficient HPC network topologies with on/off links. Future Generation Computer Systems. 139:126-138. https://doi.org/10.1016/j.future.2022.09.01212613813

Energy-Aware Data Management on NUMA Architectures

The ever-increasing need for more computing and data processing power demands for a continuous and rapid growth of power-hungry data center capacities all over the world. As a first study in 2008 revealed, energy consumption of such data centers is becoming a critical problem, since their power consumption is about to double every 5 years. However, a recently (2016) released follow-up study points out that this threatening trend was dramatically throttled within the past years, due to the increased energy efficiency actions taken by data center operators. Furthermore, the authors of the study emphasize that making and keeping data centers energy-efficient is a continuous task, because more and more computing power is demanded from the same or an even lower energy budget, and that this threatening energy consumption trend will resume as soon as energy efficiency research efforts and its market adoption are reduced. An important class of applications running in data centers are data management systems, which are a fundamental component of nearly every application stack. While those systems were traditionally designed as disk-based databases that are optimized for keeping disk accesses as low a possible, modern state-of-the-art database systems are main memory-centric and store the entire data pool in the main memory, which replaces the disk as main bottleneck. To scale up such in-memory database systems, non-uniform memory access (NUMA) hardware architectures are employed that face a decreased bandwidth and an increased latency when accessing remote memory compared to the local memory. In this thesis, we investigate energy awareness aspects of large scale-up NUMA systems in the context of in-memory data management systems. To do so, we pick up the idea of a fine-grained data-oriented architecture and improve the concept in a way that it keeps pace with increased absolute performance numbers of a pure in-memory DBMS and scales up on NUMA systems in the large scale. To achieve this goal, we design and build ERIS, the first scale-up in-memory data management system that is designed from scratch to implement a data-oriented architecture. With the help of the ERIS platform, we explore our novel core concept for energy awareness, which is Energy Awareness by Adaptivity. The concept describes that software and especially database systems have to quickly respond to environmental changes (i.e., workload changes) by adapting themselves to enter a state of low energy consumption. We present the hierarchically organized Energy-Control Loop (ECL), which is a reactive control loop and provides two concrete implementations of our Energy Awareness by Adaptivity concept, namely the hardware-centric Resource Adaptivity and the software-centric Storage Adaptivity. Finally, we will give an exhaustive evaluation regarding the scalability of ERIS as well as our adaptivity facilities