📊 When to Use Warp Programming
Quick decision guide
✅ Use warp operations when:
- Reduction operations (
sum,max,min) with 32+ elements - Regular memory access patterns (adjacent lanes → adjacent addresses)
- Need cross-architecture portability (NVIDIA/RDNA 32 vs CDNA 64 threads)
- Want simpler, more maintainable code
❌ Use traditional approaches when:
- Complex cross-warp synchronization required
- Irregular/scattered memory access patterns
- Variable work per thread (causes warp divergence)
- Problem
size < WARP_SIZE
Performance characteristics
Problem size scaling
| Elements | Warp Advantage | Notes |
|---|---|---|
| < 32 | None | Traditional better |
| 32-1K | 1.2-1.5× | Sweet spot begins |
| 1K-32K | 1.5-2.5× | Warp operations excel |
| > 32K | Memory-bound | Both approaches limited by bandwidth |
Key warp advantages
- No synchronization overhead: Eliminates barrier costs
- Minimal memory usage: No shared memory allocation needed
- Better scaling: Performance improves with more warps
- Simpler code: Fewer lines, less error-prone
Algorithm-specific guidance
| Algorithm | Recommendation | Reason |
|---|---|---|
| Dot product | Warp ops (1K+ elements) | Single reduction, regular access |
| Matrix row/col sum | Warp ops | Natural reduction pattern |
| Prefix sum | Always warp prefix_sum() | Hardware-optimized primitive |
| Pooling (max/min) | Warp ops (regular windows) | Efficient window reductions |
| Histogram | Traditional | Irregular writes, atomic updates |
Code examples
✅ Perfect for warps
# Reduction operations
from gpu.warp import sum, max
var total = sum(partial_values)
var maximum = max(partial_values)
# Communication patterns
from gpu.warp import shuffle_idx, prefix_sum
var broadcast = shuffle_idx(my_value, 0)
var running_sum = prefix_sum(my_value)
❌ Better with traditional approaches
# Complex multi-stage synchronization
stage1_compute()
barrier() # Need ALL threads to finish
stage2_depends_on_stage1()
# Irregular memory access
var value = input[random_indices[global_i]] # Scattered reads
# Data-dependent work
if input[global_i] > threshold:
result = expensive_computation() # Causes warp divergence
Performance measurement
# Always benchmark both approaches
mojo p21.mojo --benchmark
# Look for scaling patterns:
# traditional_1x: X.XX ms
# warp_1x: Y.YY ms # Should be faster
# warp_32x: Z.ZZ ms # Advantage should increase
Summary
Start with warp operations for:
- Reductions with regular access patterns
- Problems ≥ 1 warp in size
- Cross-platform compatibility needs
Use traditional approaches for:
- Complex synchronization requirements
- Irregular memory patterns
- Small problems or heavy divergence
When in doubt: Implement both and benchmark. The performance difference will guide your decision.