A Performance Model for Warp Specialization Kernels

TL;DR

This paper introduces a performance model for warp specialization kernels that accurately predicts execution time and aids in optimizing GPU applications by analyzing key parameters like warp size, memory bandwidth, and divergence.

Contribution

It presents a novel performance model for warp specialization kernels that integrates differential equations and is validated through simulations and experiments.

Findings

Model accurately predicts execution time across various parameters.

Insights facilitate optimization of GPU kernels and compiler strategies.

Validated through extensive simulations and real-world experiments.

Abstract

This paper presents a performance model tailored for warp specialization kernels, focusing on factors such as warp size, tilling size, input matrix size, memory bandwidth, and thread divergence. Our model offers accurate predictions of execution time by leveraging differential equations validated through simulations and experiments. The insights gained from this model not only enhance our understanding of warp specialization techniques but also have practical implications for optimizing GPU-accelerated applications through compiler optimizations, kernel parameter tuning, and algorithm design.