Towards hardware acceleration for parton densities estimation
This work tackles performance bottlenecks in high-energy physics computations for researchers, but it is incremental as it adapts existing code to new hardware.
The paper addresses computational challenges in determining parton distribution functions (PDFs) by comparing performance of convolution operations using different hardware instructions, identifying promising configurations to enhance PDF fitting performance with hardware accelerators like GPUs.
In this proceedings we describe the computational challenges associated to the determination of parton distribution functions (PDFs). We compare the performance of the convolution of the parton distributions with matrix elements using different hardware instructions. We quantify and identify the most promising data-model configurations to increase PDF fitting performance in adapting the current code frameworks to hardware accelerators such as graphics processing units.