GPU Register Pressure

GPU Register Pressure is the conflict between a kernel's per-thread register demand and the GPU's fixed register file capacity — where each additional register per thread reduces the number of concurrent threads (occupancy), potentially hiding less memory latency, while reducing registers may cause spills to slow local memory, creating a critical optimization tradeoff for GPU kernel performance.

GPU Register File Architecture

• Each NVIDIA SM (Streaming Multiprocessor) has a fixed register file: 65,536 32-bit registers (typical).
• Registers are partitioned among all active threads on the SM.
• More registers per thread = fewer threads per SM = lower occupancy.

Occupancy Example (NVIDIA A100)

• At 255 registers/thread: Only 256 threads active (8 warps) — very little latency hiding.
• At 32 registers/thread: Full 2048 threads (64 warps) — maximum latency hiding potential.

Register Spilling

• When kernel needs more registers than allocated → compiler spills excess to local memory.
• Local memory is actually device DRAM (L1/L2 cached) — 100x slower than register access.
• Spilling causes significant performance degradation: 2-10x slowdown for spill-heavy kernels.

Optimization Strategies

• Limit register count: `__launch_bounds__(maxThreadsPerBlock, minBlocksPerMultiprocessor)` or `--maxrregcount=N` compiler flag.
• Reduce live variables: Recompute values instead of storing them. Reorder operations to reduce simultaneous live registers.
• Use shared memory: Move some per-thread data to shared memory (explicitly managed cache).
• Loop unrolling control: Aggressive unrolling increases register usage — `#pragma unroll` factor tuning.

Profiling Register Usage

• `nvcc --ptxas-options=-v` reports register count per kernel.
• NVIDIA Nsight Compute shows register usage, spills, and occupancy.
• CUDA occupancy calculator: Interactive tool to find optimal register/thread configuration.

Register Pressure vs. ILP

• Some kernels benefit from low occupancy + high ILP (instruction-level parallelism per thread).
• Heavy compute kernels (matrix math): Fewer threads with more registers can outperform many threads with spilling.
• Principle: Occupancy is not the only metric — achieved throughput is what matters.

GPU register pressure is one of the most impactful performance-limiting factors in GPU programming — understanding and managing the register-occupancy-spill tradeoff is essential for extracting peak performance from GPU hardware.