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The power consumption of state of the art supercomputers, 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 IBM Blue Gene/Q, 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...
In situ analysis has been proposed as a promising solution to glean faster insights and reduce the amount of data to storage. A critical challenge here is that the reduced dataset is typically located on a subset of the nodes and needs to be written out to storage. Data coupling in multiphysics codes also exhibits a sparse data movement pattern wherein data movement occurs among a subset of nodes...
The high performance computing landscape is filled with diverse hardware components. A large part of understanding how these components compare to others is by looking at the various environmental aspects of these devices such as power consumption, temperature, etc. Thankfully, vendors of these various pieces of hardware have supported this by providing mechanisms to obtain this data. However, differences...
The power consumption of state of the art supercomputers, 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...
Power consumption is becoming a critical factor as we continue our quest toward exascale computing. Yet, actual power utilization of a complete system is an insufficiently studied research area. Estimating the power consumption of a large scale system is a nontrivial task because a large number of components are involved and because power requirements are affected by the (unpredictable) workloads...
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