Adaptive Pixel Hashing for GPU Load Balancing
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Solution Overview
Problem
Current graphics processing units (GPUs) face performance inefficiencies due to load imbalance and improper data locality, leading to idle compute nodes and increased communication overhead, which results in decreased performance.
Innovation Solution
The implementation of adaptive pixel hashing, which uses a table-based scheduling mechanism that dynamically maps pixel blocks to compute clusters based on execution profiling data, allowing for adaptable, scalable, and flexible hashing algorithms to optimize load balancing and data locality across varying application phases and hardware configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional pixel hashing is used, then implementation is simple, but load balance is poor causing idle compute nodes
Solution Approach 1:
The patent implements dynamic pixel hashing where the hashing function adapts based on execution profiling data collected during different phases of application execution. The system transitions from static to dynamic scheduling by adjusting hash table configurations and parameters based on observed workload characteristics, thereby optimizing load balance across compute clusters while maintaining manageable complexity through phased adaptation.
Solution Approach 2:
The system incorporates feedback mechanisms by collecting execution profiling data from compute clusters and using this information to adjust hashing parameters and table configurations. This closed-loop approach allows the scheduling mechanism to learn from actual execution patterns and improve load distribution over time, resolving the contradiction between performance optimization and system complexity.
2Productivity
If adaptive pixel hashing with execution profiling is implemented, then load balance improves, but computational overhead increases
Solution Approach 1:
The system implements periodic execution profiling at specific phases or intervals during application execution rather than continuously. By periodically collecting profiling data and adjusting hashing parameters at designated checkpoints, the system achieves improved load balance while minimizing the time overhead associated with profiling and adaptation activities.
Solution Approach 2:
The patent applies adaptive hashing optimization selectively to specific compute clusters or pixel block regions that exhibit suboptimal load balance, rather than uniformly applying profiling and adaptation across the entire system. This partial application reduces the overall computational overhead while still achieving significant improvements in work distribution efficiency.
3Productivity
If fixed hashing tables are used, then data locality is poor, but implementation is straightforward
Solution Approach 1:
The patent implements different hashing strategies and table configurations for different regions or types of compute clusters based on their specific characteristics and workload patterns. By tailoring the hashing function to local requirements of different compute nodes or clusters, the system improves data locality and access efficiency while managing complexity through localized optimization rather than universal complexity.
Data Source
AI summary
An apparatus and method for adaptive pixel hashing. For example, one embodiment of a method comprises: determining X and Y coordinates for a pixel block to be processed; performing a first lookup in a first data structure to identify a second data structure; performing a second lookup in the second data structure using the X and Y coordinates for the pixel block to identify a third data structure; performing a third lookup in a third data structure indexed based on the X and Y coordinates of the pixel block, the third lookup identifying an entry in the third data structure corresponding to the X and Y coordinates of the pixel block; reading information from the entry identifying an execution cluster to process the pixel block; and processing the pixel block by the execution cluster.