Forest: New GPU Memory Management System Uses Access-Aware Prefetching to Optimize Unified Virtual Memory
Key Takeaways
- ▸Forest introduces hardware-based access pattern profiling in GPU MMUs to classify memory access patterns into four distinct categories
- ▸The system customizes tree-based prefetching structures per allocation, with tree sizes ranging from 512KiB to 4MiB depending on access patterns
- ▸Linear/streaming accesses are detected using linear regression and handled with larger prefetch granularities
Summary
Researchers from ISCA'25 have introduced Forest, a novel GPU unified virtual memory (UVM) management system that addresses performance bottlenecks in discrete GPU architectures. The system builds upon existing tree-based neighboring prefetching (TBNp) approaches by adding hardware-based access pattern profiling to customize memory management strategies per allocation. Forest tracks GPU memory accesses through the MMU and classifies them into four categories: linear/streaming, non-linear with high-coverage and high-intensity, non-linear with high-coverage and low-intensity, and non-linear with low-coverage. Based on these classifications, the system dynamically configures prefetching tree structures with varying sizes and leaf node configurations to optimize data movement between CPU and GPU memory.
Unified virtual memory has become increasingly important for GPU programming as it allows complex data structures with pointers to be shared seamlessly between CPU and GPU. However, maintaining this abstraction on systems with discrete GPUs traditionally requires resolving page faults by copying data from host to device memory, creating significant performance overhead. Forest's innovation lies in its ability to profile access patterns in hardware and adjust prefetching strategies accordingly—using larger 4MiB trees with 256KiB leaf nodes for streaming accesses, versus smaller 512KiB trees with 16KiB leaves for sparse access patterns.
Simulation results demonstrate Forest's effectiveness across multiple benchmarks, with a variant called SpecForest showing additional improvements by making intelligent initial guesses about access patterns before profiling data becomes available. The research represents a significant step toward more efficient GPU memory management, though questions remain about whether application-level context could enable even smarter decisions than driver-level profiling alone.
- SpecForest variant improves upon base Forest by making better initial access pattern predictions before profiling data is available
- The research addresses fundamental challenges in unified virtual memory systems with discrete GPUs
Editorial Opinion
Forest represents an important incremental improvement in GPU memory management, but it also highlights the limitations of trying to infer application intent from low-level access patterns. While hardware-based profiling is elegant, the real opportunity may lie in higher-level APIs that allow applications to explicitly communicate their memory access intentions—similar to how prefetch hints work in CPU architectures. As GPU workloads become more diverse and complex, the gap between what applications know about their memory needs and what the hardware can infer will only widen.
