Accelerating GPU accelerators through neural algorithmic transformation
A. Yazdanbakhsh1; J. Park1; H. Sharma1; P. Lotfi-Kamran2; H. Esmaeilzadeh1 1 Georgia Institute of Technology, Atlanta, GA, United States2 Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
Abstract
Graphics processing units (GPUs) are many-core architectures that provide high performance by exploiting large degrees of data-level parallelism and employing the single instruction, multiple threads (SIMT) execution model. GPU can accelerate diverse classes of applications, including recognition, gaming, data analytics, weather prediction, and multimedia. Many of these applications are amenable to approximate execution. This application characteristic ...
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