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UID:submissions.supercomputing.org_SC25_sess198_ws_pmbsf115@linklings.com
SUMMARY:A GPU FFT Wrapper to Co-optimize Floating-Point Precision and Libr
 ary Selection via Predictive Error Modeling
DESCRIPTION:Julius Lehner, Eishi Arima, and Martin Schulz (Technical Unive
 rsity of Munich)\n\nApproximate and low-precision computing are essential 
 for modern applications, and effectively leveraging available precision op
 tions can deliver substantial gains in performance and energy efficiency. 
 \nWe focus on the Fast Fourier Transform (FFT), a representative function 
 used in scientific computing, and propose a wrapper library to exploit the
 se options. \nUsing multiple GPU-accelerated FFT libraries, we observe tha
 t different libraries excel in different regions of the performance–accura
 cy space and that these sweet spots depend on transform size and input con
 tent.\nGuided by these insights, we propose a framework that selects the b
 est kernel (library and precision) on the fly to minimize runtime or energ
 y while satisfying a specified error threshold. \nA lightweight machine le
 arning model predicts per-kernel error at runtime from sampled input featu
 res. Experiments show over 98% selection accuracy and mean speedups exceed
 ing 40\% compared to a double precision baseline. \nThe framework integrat
 es seamlessly with existing workflows as a wrapper library.\n\nRecording: 
 Livestreamed, Recorded\n\nRegistration Category: Technical Program Reg Pas
 s, Workshop Reg Pass\n\nSession Chairs: Steven A. Wright (University of Yo
 rk, England); Simon Hammond (National Nuclear Security Administration (NNS
 A)); and Sascha Hunold (Technical University of Vienna)\n\n
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