Photorealistic Imaging Workload Distribution via Server Band Partitioning
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Solution Overview
Problem
Current ray tracing approaches fail to fully utilize cache-aware task distribution, leading to performance inefficiencies due to additional workload management and cache flushing, which mitigates gains from spatial locality in photorealistic imaging.
Innovation Solution
A system and method that partitions frames into server bands based on load balancing and prospective rendering factors, distributing them to compute servers for processing, allowing dynamic adjustment of workload and maintaining cache relevance through proactive partitioning and load balancing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If work-stealing systems are used to distribute tiles, then workload distribution is simplified, but spatial locality is lost and cache hit rates decrease
Solution Approach 1:
The image is divided into tiles that are distributed to multiple processing elements. Each tile is further segmented into sub-tiles that can be independently processed. This segmentation allows systematic distribution while preserving spatial locality by ensuring contiguous tiles are assigned to adjacent PEs.
Solution Approach 2:
Different regions of the image are assigned to different processing elements based on their spatial location. Each PE receives tiles that are locally contiguous, ensuring that PEs working on adjacent regions process data with similar spatial characteristics. This maintains spatial locality and improves cache hit rates.
2Reliability
If express distribution systems are used, then spatial locality is maintained, but additional workload management inhibits performance
Solution Approach 1:
Each processing element autonomously manages its own tile assignments and processing tasks. The system automatically assigns tiles to PEs based on their spatial location without requiring complex centralized management. Each PE independently processes its assigned tiles and handles cache management, reducing overall system complexity.
Solution Approach 2:
Tiles are pre-assigned to specific processing elements based on their spatial location before processing begins. This preliminary assignment eliminates the need for dynamic workload management during execution, as each PE knows in advance which tiles it will process. This reduces management overhead while maintaining spatial locality.
3Productivity
If PEs grab tiles from a general pool, then workload balancing is achieved, but cache flushing occurs and performance is reduced
Solution Approach 1:
The image is divided into tiles that are systematically distributed to multiple processing elements. Each tile is further segmented into sub-tiles that can be independently processed. This segmentation allows systematic distribution while preserving spatial locality by ensuring contiguous tiles are assigned to adjacent PEs.
Solution Approach 2:
Different regions of the image are assigned to different processing elements based on their spatial location. Each PE receives tiles that are locally contiguous, ensuring that PEs working on adjacent regions process data with similar spatial characteristics. This maintains spatial locality and improves cache hit rates.
Data Source
AI summary
A graphics client receives a frame, the frame comprising scene model data. A server load balancing factor is set based on the scene model data. A prospective rendering factor is set based on the scene model data. The frame is partitioned into a plurality of server bands based on the server load balancing factor and the prospective rendering factor. The server bands are distributed to a plurality of compute servers. Processed server bands are received from the compute servers. A processed frame is assembled based on the received processed server bands. The processed frame is transmitted for display to a user as an image.