gpu register file optimization

**GPU Register Optimization** addresses the **critical trade-off between register availability for instruction-level parallelism and kernel occupancy, directly impacting throughput and latency hiding in GPU applications.** **Register File Architecture and Limits** - **Register File Size**: Per-SM registers (Ampere: 256 KB, Volta: 128 KB). Shared across all resident warps. Maximum per thread varies by GPU model (255 registers Ampere, 255 registers Volta). - **Register Banking**: 32 banks (one per thread in warp). Concurrent register access for all 32 threads requires bank conflict-free address patterns. Same-bank concurrent accesses serialize. - **Register Allocation**: Compiler allocates registers to variables. Scalar variables (float) 1 register; double 2 registers; arrays need consecutive registers. - **Allocation Pressure**: More live variables → more registers. Compiler optimizes to minimize register count (without harming ILP). **Register Spilling to Local Memory** - **Spilling Mechanism**: When register count exceeds budget (--maxrregcount), excess data spilled to local memory (on-chip cache hierarchy: L1 → L2 → HBM). - **Performance Impact**: Local memory ~100-500 cycles latency (vs ~10 cycles for register). Spilled values effectively become cache misses. Throughput drops 10-100x. - **Spill Detection**: Profiler reports spill rate (registers spilled per thread). Nonzero spill rate indicates register pressure. Target: 0 spills for performance-critical kernels. - **Reduce Spilling**: Decrease --maxrregcount (fewer blocks resident, less parallelism), rewrite code to reduce register pressure (reorganize loops, fuse operations). **Occupancy-Register Tradeoff** - **Occupancy Definition**: Percentage of SM warp slots filled. More registers per thread → fewer resident warps → lower occupancy. - **Occupancy Curve**: Register count vs occupancy (inverse relationship). Register count = N → occupancy = (SM_reg_size / (N × threads_per_warp × warps_per_block)). - **Latency Hiding**: High occupancy (many resident warps) hides memory latency. Low occupancy (few register, few warps) relies on few warps to hide latency. - **Optimal Point**: Often exists between extremes. Too low register (occupancy 100%) = stalls on memory. Too high register (occupancy 25%) = stalls on latency. **PTX ISA Register Model** - **PTX Register Classes**: %r (32-bit register), %rd (64-bit), %p (predicate), %f (float), %d (double). Abstract model (not tied to specific GPU architecture). - **Virtual Registers**: PTX compilation assigns unlimited virtual registers; target-specific compiler (NVCC, PTXAS) maps to physical registers. - **Physical Constraints**: Target GPU (SM 7.0, 8.0, 9.0) determines physical register count per warp, occupancy implications. Same PTX code → different occupancy on different GPUs. - **ISA Compatibility**: PTX forward/backward compatible within reason. Code compiled to PTX can target multiple GPU architectures (with occupancy variation). **Compiler Register Allocation Strategies** - **Register Pressure Analysis**: Compiler builds interference graph (variables live simultaneously). Graph coloring assigns registers; chromatic number = min registers needed. - **Spilling Decision**: When variables exceed registers, spill to local memory. Decisions impact performance; algorithm heuristic-based (not optimal). - **Loop Unrolling Effect**: Unrolling increases register count (multiple loop iterations's variables live simultaneously). Trade-off: faster loop (fewer branches) vs higher register pressure. - **Optimization Passes**: Multiple passes refine allocation. LICM (loop-invariant code motion), CSE (common subexpression elimination), dead code elimination reduce register pressure. **Kernel Register Count Reduction Techniques** - **Refactor Loops**: Break long loops into smaller loops (reduce simultaneous live variables). Example: Process array in 256-element chunks instead of full array. - **Array Privatization**: Private arrays (private to thread) expensive (registers). Replace with scalars, iterate instead of bulk allocation. - **Use Functions**: Inline functions increase register pressure; non-inlined functions transfer data via memory (cheaper than spilling). Trade-off: function call overhead vs register savings. - **Reduce Precision**: float (1 register) vs double (2 registers). Use float where possible; promote to double only when necessary. **Warp-Level Register Sharing and Limits** - **Warp Register Pool**: All threads in warp share 32 registers (Ampere) conceptually. Thread i gets registers r_{i*N}, r_{i*N+1}, ..., r_{i*N+N-1} (N = registers per thread). - **Cross-Warp Sharing**: Register file shared among multiple warps (SM occupancy). Warp 0 occupies registers 0-4095, Warp 1 occupies 4096-8191, etc. - **Bank Conflict Minimization**: Register accesses within warp sequential (thread i accesses bank i). Careful allocation avoids conflicts. **Profiling and Optimization Workflow** - **Nsight Metrics**: "Register per Thread" metric shows allocation. "Registers per Inst Executed" indicates spilling (>4 typical, >8 severe). - **Occupancy Analysis**: Nsight reports occupancy-limiting factor (registers, shared memory, threads-per-block). - **Optimization Priority**: Eliminate spilling first (highest impact). Then reduce registers if occupancy < 50% (may improve performance).