Nested Recursive Parallelism for Heterogeneous Computing Load Distribution
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Solution Overview
Problem
Current computing systems for computational rendering, particularly in graphics processing units (GPUs), face challenges in efficiently distributing loads across heterogeneous architectures due to limitations in flat parallelism, cache misses, and unpredictable data access patterns, which hinder real-time photorealistic rendering.
Innovation Solution
The implementation of nested recursive parallelism within heterogeneous computing architectures, enabled by a computational rendering model that allows for computation and communication overlap, memory transfer, and dynamic task management, using a context-aware compiler and runtime system to optimize workload and data distribution across CPUs and GPUs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If flat parallelism is used in computational rendering, then simplicity of implementation is maintained, but load distribution efficiency deteriorates and cache misses increase
Solution Approach 1:
The patent implements nested recursive parallelism where rendering tasks are divided into multiple levels of hierarchical sub-tasks. The top-level task divides the scene into major regions, which are further subdivided into sub-regions, and so on. This nested structure enables efficient load distribution across heterogeneous computing resources while maintaining implementation feasibility through systematic decomposition.
Solution Approach 2:
The rendering workload is segmented into independent, manageable units that can be distributed across multiple processors. The scene is divided into spatial regions, and each region is further segmented into sub-regions, creating a hierarchical task structure that improves load distribution efficiency without significantly increasing implementation complexity.
2Device complexity
If flat parallelism is used in computational rendering, then device complexity is reduced, but rendering speed and real-time performance deteriorate
Solution Approach 1:
The patent introduces dynamic load balancing mechanisms that adaptively adjust task distribution based on real-time processor status and workload characteristics. The hierarchical parallelism structure allows dynamic allocation of sub-tasks to available computing resources, enabling real-time rendering performance without requiring overly complex static architectures.
Solution Approach 2:
The patent transitions from two-dimensional flat parallelism to multi-dimensional hierarchical parallelism by adding the temporal dimension and task dependency dimension. This allows rendering operations to be organized in multiple layers with explicit data flow and dependency relationships, improving rendering speed while managing complexity through structured organization.
3Ease of operation
If computation and communication are separated, then task management simplicity is maintained, but performance efficiency deteriorates due to idle processing time
Solution Approach 1:
The patent implements computation-communication overlap where data transfer and processing operations occur simultaneously. While one processor communicates results to another, the receiving processor begins processing the incoming data without waiting for complete transmission. This continuous utilization of processing resources improves performance efficiency while maintaining manageable task management through the hierarchical structure.
Solution Approach 2:
The patent performs preliminary data preparation and pre-computation of intermediate results that can be used by subsequent tasks. By preparing data in advance and organizing it in a hierarchical structure, the system enables overlapping of computation and communication phases, improving efficiency without significantly complicating task management.
Data Source
AI summary
Embodiments for dynamically distributing loads in computational rendering in a computing environment. A computational rendering model on a computational rendering to exploit nested recursive parallelism within a heterogenous computing architecture to enable communication overlap, memory transfer, and data and task management, wherein the computational rendering model is developed for the heterogenous computing architecture.