The research literature to date mainly aimed at reducing energy consumption in HPC environments. In this paper we propose a job power aware scheduling mechanism to reduce HPC's electricity bill without degrading the system utilization. The novelty of our job scheduling mechanism is its ability to take the variation of electricity price into consideration as a means to make better decisions of the timing of scheduling jobs with diverse power profiles. We verified the effectiveness of our design by conducting trace-based experiments on an IBM Blue Gene/P and a cluster system as well as a case study on Argonne's 48-rack IBM Blue Gene/Q system. Our preliminary results show that our power aware algorithm can reduce electricity bill of HPC systems as much as 23%.

In addition to pushing what is possible computationally, state-of-the-art supercomputers are also pushing what is acceptable in terms of power consumption. Despite hardware manufacturers researching and developing efficient system components (e.g., processor, memory, etc.), the power consumption of a complete system remains an understudied research area. Because of the complexity and unpredictable workloads of these systems, estimating the power consumption of a full system is a nontrivial task.In this paper, we provide system-level power usage and temperature analysis of early access to Argonne's latest generation of IBM Blue Gene supercomputers, the Mira Blue Gene/Q system. The analysis is provided from the point of view of jobs running on the system. We describe the important implications these system level measurements have as well as the challenges they present. Using profiling code on benchmarks, we will also look at the new tools this latest generation of supercomputer provides and gauge their usefulness and how well they match up against the environmental data.

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The power consumption of state of the art supercom-puters, because of their complexity and unpredictable workloads, is extremely difficult to estimate. Accurate and precise results, as are now possible with the latest generation of supercomputers, are therefore a welcome addition to the landscape. Only recently have end users been afforded the ability to access the power consumption of their applications. However, just because it's possible for end users to obtain this data does not mean it's a trivial task. This emergence of new data is therefore not only understudied, but also not fully understood. In this paper, we provide detailed power consumption analysis of microbenchmarks running on Argonne's latest generation of IBM Blue Gene supercomputers, Mira, a Blue Gene/Q system. The analysis is done utilizing our power monitoring library, MonEQ, built on the IBM provided Environmental Monitoring (EMON) API. We describe the importance of sub-second polling of various power domains and the implications they present. To this end, previously well understood applications will now have new facets of potential analysis.

h i g h l i g h t s• A colored Petri net was developed for tradeoff analysis of power and performance.• Trace based validation demonstrated that the model is highly accurate and scalable.• The model was used to analyze different power capping methods on petascale systems. a b s t r a c t As high performance computing (HPC) continues to grow in scale and complexity, energy becomes a critical constraint in the race to exascale computing. The days of ''performance at all cost'' are coming to an end. While performance is still a major objective, future HPC will have to deliver desired performance under the energy constraint. Among various power management methods, power capping is a widely used approach. Unfortunately, the impact of power capping on system performance, user jobs, and powerperformance efficiency are not well studied due to many interfering factors imposed by system workload and configurations. To fully understand power management in extreme scale systems with a fixed power budget, we introduce a power-performance modeling tool named PuPPET (Power Performance PETri net). Unlike the traditional performance modeling approaches such as analytical methods or trace-based simulators, we explore a new approach -colored Petri nets -for the design of PuPPET. PuPPET is fast and extensible for navigating through different configurations. More importantly, it can scale to hundreds of thousands of processor cores and at the same time provide high levels of modeling accuracy. We validate PuPPET by using system traces (i.e., workload log and power data) collected from the production 48-rack IBM Blue Gene/Q supercomputer at Argonne National Laboratory. Our trace-based validation demonstrates that PuPPET is capable of modeling the dynamic execution of parallel jobs on the machine by providing an accurate approximation of energy consumption. In addition, we present two case studies of using PuPPET to study power-performance tradeoffs on petascale systems.