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Hardware performance monitoring counters have recently received a lot of attention. They have been used by diverse communities to understand and improve the quality of computing systems: for example, architects use them to extract application characteristics and propose new hardware mechanisms; compiler writers study how generated code behaves on particular hardware; software developers identify critical regions of their applications and evaluate design choices to select the best performing implementation. In this paper, we propose that counters be used by all categories of users, in particular non-experts, and we advocate that a few simple metrics derived from these counters are relevant and useful. For example, a low IPC (number of executed instructions per cycle) indicates that the hardware is not performing at its best; a high cache miss ratio can suggest several causes, such as conflicts between processes in a multicore environment. We also introduce a new simple and flexible user-level tool that collects these data on Linux platforms, and we illustrate its practical benefits through several use cases.
Recorded at SpringOne Platform 2016. Speaker: Adrian Cole Slides: http://www.slideshare.net/SpringCentral/how-to-properly-blame-things-for-causing-latency La...
Jeff Greene and Matt Bernacki are learning scientists in the UNC-Chapel Hill School of Education. They leverage the data that students create when they use digital resources to help them learn.
Researchers and educators have developed computer-based tools, such as automated writing evaluation (AWE) systems, to increase opportunities for students to produce natural language responses in a variety of contexts and subsequently to alleviate some of the pressures facing writing instructors due to growing class sizes