Thread Group Load Balancing via Compute Work Distributor
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Solution Overview
Problem
Modern GPUs face challenges in achieving strong scaling due to inadequate data bandwidth, which hinders the ability to efficiently utilize increased parallel processing capabilities, leading to performance bottlenecks and inefficiencies in data sharing across processing cores.
Innovation Solution
The introduction of Cooperative Group Arrays (CGAs) and a centralized Compute Work Distributor (CWD) enables efficient load balancing and concurrent execution of thread blocks across multiple processing resources, ensuring all CTAs within a CGA launch simultaneously and execute concurrently, thereby optimizing data locality and synchronization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the number of processing core units increases per chip, then parallel processing capability is improved, but data bandwidth becomes inadequate leading to performance bottlenecks
Solution Approach 1:
The patent segments the large dataset into multiple data shards distributed across different memory devices. Each processing core unit accesses only its required shard portion, reducing memory bandwidth requirements per core while maintaining overall parallel processing capability. This segmentation allows the system to scale processing power without proportionally increasing bandwidth demands.
Solution Approach 2:
The patent introduces a hierarchical memory architecture with multiple dimensions: L1 cache, L2 cache, and distributed memory devices. By organizing data access across these dimensional layers, the system enables parallel cores to access data from optimized memory levels, effectively increasing available bandwidth without adding more physical memory interfaces.
2Productivity
If more processing cores are added to increase parallelism, then compute performance is improved, but synchronization efficiency deteriorates
Solution Approach 1:
The patent divides the parallel computation into independent workloads assigned to different processing core units. Each core processes segmented data shards with minimal inter-dependency, reducing the frequency and complexity of synchronization events. This segmentation allows compute performance to scale while keeping synchronization overhead manageable.
Solution Approach 2:
The patent introduces a memory hierarchy with L1 and L2 caches acting as intermediaries between processing cores and main memory. These cache layers buffer data exchanges, allowing cores to proceed with computation using cached data without immediate synchronization, thereby reducing synchronization time while maintaining compute performance.
3Quantity of substance
If data is distributed across multiple memory devices, then memory capacity is increased, but data access time increases due to latency
Solution Approach 1:
The patent segments data into shards distributed across multiple memory devices, with each processing core assigned to access specific shards. This segmentation enables parallel data access patterns where multiple cores simultaneously retrieve data from different memory devices, increasing effective throughput despite individual access latency.
Solution Approach 2:
The patent implements a cache hierarchy where data is pre-loaded into L1 and L2 caches before being accessed by processing cores. This preliminary action of caching frequently accessed data reduces the time cores spend waiting for data from distributed memory devices, effectively masking memory access latency while maintaining increased memory capacity.
Data Source
AI summary
A processor supports new thread group hierarchies by centralizing work distribution to provide hardware-guaranteed concurrent execution of thread groups in a thread group array through speculative launch and load balancing across processing cores. Efficiencies are realized by distributing grid rasterization among the processing cores.