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.

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.

Complexity in parallel computation for current HPC systems has being modeled in order to estimate time and energy consumption, which have been conduced to evaluate real costs. Communication analytical models for time and energy comsumption are an attractive issue as part of this cost analysis in parallel computation. In this paper, we present time and energy analytical models for MPI communications using OSU micro benchmarks under Sandy Bridge-EP computer architecture. RAPL (Running Average Power Limit) interface has been setup to provide mechanisms to enforce real energy consumption measurements for OSU micro benchmarks on this architecture. Our applied methodology defines an experimental framework based on several core configurations and different transmission rates. The RAPL interface allows us to obtain different energy values for the OSU benchmark executions. The measures obtained are used to obtain time and energy consumption parameters of our models, establishing average errors below 1% for almost all configurations in real executions under this computer architecture for an ad-hoc model. Alternative and simpler time and energy models are also proposed with errors up to 3.1% for the worst case.

Comparable to time performance models, it is now possible to estimate performance based upon energy consumption for HPC systems. The predictive ability of the analytical modeling is an interesting feature that motivates us to approach this methodology for the case of energy consumption. In this paper, we present an analytical model for predicting the energy consumption for the High Performance Linpack (HPL). The derived model can be used to know in advance the energy consumed by the HPL over a target architecture, and can be integrated into the schedulers of operating systems or queue managers. We established an experimental setup using a standard metered PDU that allowed us to measure the energy consumption for the HPL benchmark on our cluster. With the monitoring system in place, we can obtain the architectural and algorithmic parameters associated for both performance and energy analytical models. Also this has made possible watts and gflops-per-watt prediction when we execute Linpack executions with concrete algorithm parameters in our cluster.