Alberto Cabrera

The growing demand for more computing resources has increased the overall energy consumption of computer systems. To support this increasing demand, power and energy consumption must be considered as a constraint on software execution. Modern architectures provide tools for managing the power constraints of a system directly. The Intel Power Cap is a relatively new tool developed to give users fine-grained control over power usage at the central processing unit (CPU) level. The complexity of these tools, in addition to the high variety of modern heterogeneous architectures, hinders predictions of the energy consumption and the performance of any target software. The application of power capping technologies usually leads to the bi-objective optimization problem for energy efficiency and execution time but optimal power constraints could also produce exceeding performance losses. Thus, methods and tools are needed to calculate the proper parameters for power capping technologies, and to optimize energy efficiency. We propose a methodology to analyze the performance and the energy efficiency trade-offs using this power cap technology for a given application. A Pareto front is extracted for the multi-objective performance and energy problem, which represents multiple feasible configurations for both objectives. An extensive experimentation is carried out to categorize the different applications to determine the overall optimal power cap configurations. We propose the use of machine learning (ML) clustering techniques to categorize each application in the target architecture. The use of ML allows us to automate the process and simplifies the effort required to solve the optimization problem. A practical case is presented where we categorize the applications using ML techniques, with the possibility of adding a new application into an existing categorization.

Rapid growth of demand for remote computational power, along with high energy costs and infrastructure limits, has led to treating power usage as a primary constraint in data centers. Especially, recent challenges related to development of exascale systems or autonomous edge systems require tools that will limit power usage and energy consumption. This paper presents a power capping method that allows operators to quickly adjust the power usage to external conditions and, at the same time, to reduce energy consumption and negative impact on performance of applications. We propose an optimization model and both heuristic and exact methods to solve this problem. We present an evaluation of power capping approaches supported by results of application benchmarks and experiments performed on new heterogeneous servers.

Modern standards in High Performance Computing (HPC) have started to consider energy consumption and power draw as a limiting factor. New and more complex architectures have been introduced in HPC systems to afford these new restrictions, and include coprocessors such as GPGPUs for intensive computational tasks. As systems increase in heterogeneity, workload distribution becomes a more core problem to achieve the maximum efficiency in every computational component. We present a Multi-Objective Dynamic Load Balancing (DLB) approach where several objectives can be applied to tune an application. These objectives can be dynamically exchanged during the execution of an algorithm to better adapt to the resources available in a system. We have implemented the Multi-Objective DLB together with a generic heuristic engine, designed to perform multiple strategies for DLB in iterative problems. We also present Ull Multiobjective Framework (UllMF), an open-source tool that implements the Multi-Objective generic approach. UllMF separates metric gathering, objective functions to be optimized and load balancing algorithms, and improves code portability using a simple interface to reduce the costs of new implementations. We illustrate how performance and energy consumption are improved for the implemented techniques, and analyze their quality using different DLB techniques from the literature.

With energy efficiency one of the main challenges on the way towards ultrascale systems, there is great need for access to high-quality energy consumption data. Such data would enable researchers and designers to pinpoint energy inefficiencies at all levels of the computing stack, from whole nodes down to critical regions of code. However, measurement capabilities are often missing, and significantly differ between platforms where they exist. A standard is yet to be established. To that end, this paper attempts an extensive survey of energy measurement tools currently available at both the hardware and software level, comparing their features with respect to energy monitoring.

Energy consumption and efficiency analysis has raised as an interesting topic to address for high performance computing researchers. Exascale computers, with current capabilities, would require a huge amount of power to operate at full capacity. This has attracted the interest of many researchers in the field contributing to elaborate multiple libraries and tools to measure energy and power consumption. In spite of the efforts, there is no current standard for energy measurements. It is known that the accuracy of the measurements is highly dependent on measurement tools (power meters, performance counters, ). A standard library should offer independence between hardware and software and portability for the instrumented code between different architectures. Additionally, the overhead of the library should be low enough to achieve accurate measurements. We proposed EML (Energy Measurement Library) and now we present insights on the usage and the overhead of the library, so that it could contribute to the establishment of an standard for energy measurement.