SIMD Optimization Skill

Parallelize tensor and array operations using SIMD.

When to Use

• •Optimizing tensor operations
• •Vectorizing element-wise computations
• •Performance-critical loops (>1000 elements)
• •Benchmark results show optimization potential

Quick Reference

from sys.info import simdwidthof

comptime width = simdwidthof[DType.float32]()

# SIMD vector add
for i in range(0, size, width):
    result.store(i, a.load[width](i) + b.load[width](i))

Workflow

1. •Identify bottleneck - Profile code to find hot loops
2. •Get SIMD width - Use simdwidthof[dtype]()
3. •Vectorize loop - Process width elements per iteration
4. •Handle remainder - Process leftover elements
5. •Benchmark - Verify performance improvement (4x-8x expected)

Mojo-Specific Notes

• •SIMD width varies by CPU and dtype (usually 8-16 for float32)
• •Always handle remainder elements with scalar loop
• •Prefer alias for compile-time SIMD width constants
• •Test on target hardware - SIMD width is platform-specific

Error Handling

References

• •.claude/shared/mojo-guidelines.md - SIMD patterns section
• •Mojo manual: SIMD documentation