Shader Partitioning via Priority Scheduling
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Solution Overview
Problem
Current graphics hardware limitations restrict the complexity and size of programmable shaders, necessitating inefficient multipass methods for implementing larger shaders, with conventional partitioning methods like Recursive Dominator Split (RDS) being too slow and not accommodating multiple outputs.
Innovation Solution
A method and system for prioritizing and partitioning operations into smaller programs that can be executed in multiple passes, using priority schemes like Sethi-Ullman numbering and depth-first traversal to minimize register usage and accommodate multiple outputs, allowing for faster compilation and more efficient use of graphics hardware resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional partitioning methods like Recursive Dominator Split (RDS) are used, then shader programs can be divided into smaller programs, but the compilation time becomes excessively long (O(N³) runtime)
Solution Approach 1:
The patent segments the shader program into smaller sub-programs by dividing operations into partitions that respect hardware resource constraints. Each partition is a smaller program that can be executed independently in the graphics pipeline, allowing the large original shader to be compiled into multiple manageable units rather than attempting to compile one large program.
Solution Approach 2:
The patent employs dynamic priority assignment to operations based on their resource usage characteristics. Operations are assigned priorities dynamically during the partitioning process, allowing the system to adaptively schedule operations into partitions. This dynamic approach enables faster compilation by making intelligent decisions about operation ordering without requiring exhaustive search algorithms.
2Adaptability or versatility
If shader program size is increased to handle complex computations, then more computational capability is achieved, but hardware resource constraints (registers, instruction length) are violated
Solution Approach 1:
The patent divides the shader program into multiple partitions, each of which individually satisfies hardware resource constraints. By segmenting the computational workload into smaller programs that can be executed in sequence through the graphics pipeline, the system achieves complex computational capability while respecting the limited register and instruction constraints of individual hardware executions.
Solution Approach 2:
The patent implements a nested structure where multiple smaller shader programs are organized within a larger shader program. The smaller programs are nested within the larger program structure and are executed in sequence through multiple passes of the graphics pipeline, allowing complex computations to be achieved through composition of simpler, hardware-compliant units.
3Adaptability or versatility
If conventional partitioning methods are used, then programs can be divided into smaller shaders, but the method does not accommodate multiple outputs per pass
Solution Approach 1:
The patent creates a universal partitioning framework that can handle multiple output types and configurations within the same system. The partitioning method is designed to accommodate various output requirements (single output, multiple outputs, different output types) without requiring separate specialized algorithms, making the system versatile while maintaining high partitioning efficiency through a unified approach.
Data Source
AI summary
Computer operations are partitioned for execution by a processor. A plurality of operations to be partitioned is received. The respective priorities for each of the plurality of operations is determined. Ready operations are identified, where a ready operation is an operation that does not have any predecessor operations that have not yet been added to a partition. A determination is made whether adding the ready operations with the highest priorities to the current partition would violate a hardware resource constraint. Based on this determination, the ready operations are added to the partition. The operations added to the partition are provided to a processor for execution.


