Energy aware approach for HPC systems

International audienceHigh‐performance computing (HPC) systems require energy during their full life cycle from design and production to transportation to usage and recycling/dismanteling. Because of increase of ecological and cost awareness, energy performance is now a primary focus. This chapter focuses on the usage aspect of HPC and how adapted and optimized software solutions could improve energy efficiency. It provides a detailed explanation of server power consumption, and discusses the application of HPC, phase detection, and phase identification. The chapter also suggests that having the load and memory access profiles is insufficient for an effective evaluation of the power consumed by an application. The available leverages in HPC systems are also shown in detail. The chapter proposes some solutions for modeling the power consumption of servers, which allows designing power prediction models for better decision making.These approaches allow the deployment and usage of a set of available green leverages, permitting energy reduction

Adaptive load balancing for HPC applications

One of the critical factors that affect the performance of many applications is load imbalance. Applications are increasingly becoming sophisticated and are using irregular structures and adaptive refinement techniques, resulting in load imbalance. Moreover, systems are becoming more complex. The number of cores per node is increasing substantially and nodes are becoming heterogeneous. High variability in the performance of the hardware components introduces further imbalance. Load imbalance leads to drop in system utilization and degrades the performance. To address the load imbalance problem, many HPC applications employ dynamic load balancing algorithms to redistribute the work and balance the load. Therefore, performing load balancing is necessary to achieve high performance. Different application characteristics warrant different load balancing strategies. We need a variety of high-quality, scalable load balancing algorithms to cater to different applications. However, using an appropriate load balancer is insufficient to achieve good performance because performing load balancing incurs a cost. Moreover, due to the dynamic nature of the application, it is hard to decide when to perform load balancing. Therefore, deciding when to load balance and which strategy to use for load balancing may not be possible a priori. With the ever increasing core counts on a node, there will be a vast amount of on-node parallelism. Due to the massive on-node parallelism, load imbalance occurring at the node level can be mitigated within the node instead of performing a global load balancing. However, having the application developer manage resources and handle dynamic imbalances is inefficient as well as is a burden on the programmer. The focus of this dissertation is on developing scalable and adaptive techniques for handling load imbalance. The dissertation presents different load balancing algorithms for handling inter and intra-node load imbalance. It also presents an introspective run-time system, which will monitor the application and system characteristics and make load balancing decisions automatically

DNA-inspired Scheme for Building the Energy Profile of HPC Systems

International audienceEnergy usage is becoming a challenge for the design of next generation large scale distributed systems. This paper explores an inno- vative approach of profiling such systems. It proposes a DNA-like solution without making any assumptions on the running applications and used hardware. This profiling based on internal counters usage and energy monitoring allows to isolate specific phases during the execution and enables some energy consumption control and energy usage prediction. First experimental validations of the system modeling are presented and analyzed