Authors: Daniel Nichols (University of Maryland), Jae-Seung Yeom (Lawrence Livermore National Laboratory), and Abhinav Bhatele (University of Maryland)
Abstract: Applications can experience significant performance differences when run on different architectures. For example, GPUs are often utilized to accelerate an application over its CPU implementation. Understanding how performance changes across platforms is vital to the design of hardware, systems software, and performance critical applications. However, modeling the relationship between systems and performance is difficult as run time data needs to be collected on each platform. In this poster, we present a methodology for predicting the relative performance of an application across multiple systems using profiled performance counters and deep learning.
Best Poster Finalist (BP): no
Poster summary: PDF
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