📊 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

ElementsWarp AdvantageNotes
< 32NoneTraditional better
32-1K1.2-1.5×Sweet spot begins
1K-32K1.5-2.5×Warp operations excel
> 32KMemory-boundBoth 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

AlgorithmRecommendationReason
Dot productWarp ops (1K+ elements)Single reduction, regular access
Matrix row/col sumWarp opsNatural reduction pattern
Prefix sumAlways warp prefix_sum()Hardware-optimized primitive
Pooling (max/min)Warp ops (regular windows)Efficient window reductions
HistogramTraditionalIrregular 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.