A Performance Model for Warp Specialization Kernels

2025-06-12

AI Summary

This work addresses the challenge of predicting execution performance for GPU warp-specialized kernels. We propose the first end-to-end performance model based on differential equations, jointly characterizing key factors including warp size, tiling dimensions, matrix dimensions, memory bandwidth, and thread divergence. The model is rigorously validated through both architectural analysis and empirical CUDA kernel measurements, augmented by a detailed bandwidth model. Its key innovation lies in the first application of differential equations to warp-level performance modeling, enabling quantitative characterization of the mapping between warp-level parallelism structures and performance bottlenecks. Experimental evaluation demonstrates a prediction error of less than 8.2%. The model supports compiler-driven auto-tuning and adaptive parameter configuration, achieving a 17% improvement in energy efficiency for sparse computation and GEMM workloads.

Technology Category

• Machine Learning: Hardware-aware ML
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Computer Vision: Diffusion Models for Vision

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Develops a performance model for warp specialization kernels
• Predicts execution time using differential equations and validation
• Optimizes GPU applications via compiler and kernel tuning

Innovation

• Performance model for warp specialization kernels
• Uses differential equations for execution time
• Optimizes GPU applications via parameter tuning