GPU Hardware Scheduling for Dynamic Buffer Prioritization
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Solution Overview
Problem
Conventional systems relying on CPUs to prioritize and schedule commands for GPUs do not fully utilize GPU resources efficiently, as they lack dynamic and local prioritization capabilities, leading to suboptimal allocation of processing power.
Innovation Solution
Implementing a system where the GPU selects and prioritizes a subset of buffers for execution based on its workload profile and received priority ordering, allowing for dynamic and efficient resource allocation, including the use of ring buffers and a ring list controller to coordinate memory access and scheduling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If CPU-based prioritization and scheduling is used for GPU commands, then system control and coordination are simplified, but GPU resource utilization efficiency deteriorates
Solution Approach 1:
The GPU is equipped with hardware-based scheduling logic that enables it to autonomously prioritize and select command buffers for execution based on its own workload profile and internal state, rather than relying solely on CPU directives. This self-service capability allows the GPU to make real-time scheduling decisions that optimize its resource utilization while maintaining simplified CPU control architecture.
2Productivity
If dynamic and local prioritization capabilities are implemented on GPU, then resource allocation efficiency improves, but device complexity increases
Solution Approach 1:
The patent replaces software-based prioritization mechanisms with hardware-based scheduling logic directly integrated into the GPU architecture. This substitution enables dynamic and local prioritization decisions to be made in hardware, achieving high resource allocation efficiency without proportionally increasing overall system complexity, as the decision-making capability is built into the GPU's execution pipeline rather than requiring complex external control software.
Data Source
AI summary
An apparatus and methods for scheduling and executing commands issued by a first processor, such as a CPU, on a second processor, such as a GPU, are disclosed. In one embodiment, a method of executing processes on a graphics processing unit (GPU) includes monitoring one or more buffers in a memory, selecting a first subset from the one or more buffers for execution on the GPU based on a workload profile of the GPU, and executing the first subset on the GPU. The GPU may also receive a priority ordering of the one or more buffers, where the selecting is further based on the received priority ordering. By performing prioritization and scheduling of commands in the GPU, system performance is enhanced.