GPU Scheduling
GPU scheduling is a system-level concept that involves managing and allocating GPU (Graphics Processing Unit) resources among multiple processes or tasks to optimize performance, fairness, and utilization. It determines how GPU compute units, memory, and other hardware components are shared across applications, such as in high-performance computing, machine learning, or graphics rendering. This includes handling task prioritization, context switching, and load balancing to prevent bottlenecks and ensure efficient execution.
Developers should learn GPU scheduling when working in environments with shared GPU resources, such as data centers, cloud platforms, or multi-user systems, to optimize application performance and resource efficiency. It is crucial for use cases like training large machine learning models, running parallel scientific simulations, or managing real-time graphics in gaming and VR, where improper scheduling can lead to slowdowns or resource contention. Understanding this helps in designing scalable systems and troubleshooting performance issues in GPU-accelerated workflows.