Exploiting Dependency and Parallelism: Real-Time Scheduling and Analysis for GPU Tasks

2026-02-24

AI Summary

This work addresses the unpredictability of GPU kernel execution times caused by inter-kernel data dependencies and resource contention, which undermines real-time guarantees. Focusing on DAG-structured GPU tasks, the paper proposes a scheduling approach that does not rely on kernel priority assumptions. By explicitly modeling kernel dependencies and co-scheduling kernel-level parallelism, the method derives tight and safe worst-case execution time (WCET) bounds. Built upon standard CUDA APIs, the approach requires only DAG task modeling, parallel kernel scheduling, and response time analysis, without additional hardware or software support. Experimental results on both synthetic and real-world benchmarks demonstrate that the proposed method reduces WCET and measured execution time by up to 32.8% and 21.3%, respectively, compared to existing techniques.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Machine Learning: Kernel Methods

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

With the rapid advancement of Artificial Intelligence, the Graphics Processing Unit (GPU) has become increasingly essential across a growing number of safety-critical application domains. Applying a GPU is indispensable for parallel computing; however, the complex data dependencies and resource contention across kernels within a GPU task may unpredictably delay its execution time. To address these problems, this paper presents a scheduling and analysis method for Directed Acyclic Graph (DAG)-structured GPU tasks. Given a DAG representation, the proposed scheduling scales the kernel-level parallelism and establishes inter-kernel dependencies to provide a reduced and predictable DAG response time. The corresponding timing analysis yields a safe yet nonpessimistic makespan bound without any assumption on kernel priorities. The proposed method is implemented using the standard CUDA API, requiring no additional software or hardware support. Experimental results under synthetic and real-world benchmarks demonstrate that the proposed approach effectively reduces the worst-case makespan and measured task execution time compared to the existing methods up to 32.8% and 21.3%, respectively.

Problem

• GPU tasks
• real-time scheduling
• data dependencies
• resource contention
• DAG

Innovation

• GPU scheduling
• DAG tasks
• real-time analysis
• kernel-level parallelism
• makespan bound