Heterogeneous Wavefront Execution on GPU Compute Units
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Solution Overview
Problem
The processing efficiency of graphics processing units (GPUs) is limited by the number of in-flight wavefronts due to resource constraints, leading to underutilization of parallel processing resources for service-level operations that require fewer threads, which wastes capacity and consumes slots that could be used for regular GPU kernels.
Innovation Solution
Implementing heterogeneous wavefront execution, where alternative wavefront types such as scalar wavefronts can execute alongside regular wavefronts, allowing for concurrent execution of distinct sets of instructions on different execution units, thereby optimizing resource usage and increasing the number of wavefronts that can be executed simultaneously.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If regular wavefronts are used for service-level operations, then the operations can be executed, but the GPU resources are underutilized and wavefront slots are wasted
Solution Approach 1:
The patent introduces a scalar wavefront type specifically designed for service-level operations that require fewer threads, while maintaining regular wavefronts for compute operations. This local differentiation allows each wavefront type to be optimized for its specific purpose, preventing resource underutilization when executing service-level operations.
Solution Approach 2:
The patent changes the parameter of wavefront thread count by introducing scalar wavefronts with fewer threads (e.g., 1 thread) compared to regular wavefronts (e.g., 64 threads). This parameter change allows service-level operations to consume fewer GPU resources, increasing overall resource utilization efficiency.
2Productivity
If the number of in-flight wavefronts is increased to improve processing efficiency, then more parallel operations can be executed, but the limited GPU resources (execution units and register file size) are exceeded
Solution Approach 1:
The patent changes the resource consumption parameter of wavefronts by introducing heterogeneous types with different thread counts. Scalar wavefronts consume fewer register file resources and execution unit capacity, allowing a greater total number of wavefronts to be maintained in-flight within the same resource constraints.
Solution Approach 2:
The patent segments the wavefront population into different types (scalar and regular) with different resource requirements. This segmentation allows the GPU to maintain a diverse mix of wavefronts in-flight, optimizing the balance between the number of concurrent wavefronts and available resources.
3Adaptability or versatility
If scalar wavefronts are introduced to execute service-level operations, then resource utilization is improved, but the system complexity increases due to multiple wavefront types
Solution Approach 1:
The patent designs the wavefront execution system to be universal by creating a common execution framework that can handle both scalar and regular wavefronts. The compute units and register files can execute either wavefront type, reducing the need for separate dedicated hardware paths and minimizing the increase in system complexity.
Data Source
AI summary
Implementing heterogeneous wavefronts on a graphics processing unit (GPU) is disclosed. A scheduler assigns heterogeneous wavefronts for execution on a compute unit of a processing device. The heterogeneous wavefronts include different types of wavefronts such as vector compute wavefronts and service-level wavefronts that vary in resource requirements and instruction sets. As one example, heterogeneous wavefronts may include scalar wavefronts and vector compute wavefronts that execute on scalar units and vector units, respectively. Distinct sets of instructions are executed for the heterogeneous wavefronts on the compute unit. Heterogeneous wavefronts are processed in the same pipeline of the processing device.