mojo-simd-optimize

Development

Apply SIMD optimizations to Mojo code for parallel computation. Use when optimizing performance-critical tensor and array operations.

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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

Identify bottleneck - Profile code to find hot loops
Get SIMD width - Use simdwidthof[dtype]()
Vectorize loop - Process width elements per iteration
Handle remainder - Process leftover elements
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

Error	Cause	Solution
`Out of bounds`	Remainder not handled	Add scalar remainder loop
`No speedup`	Wrong SIMD width	Use `simdwidthof[dtype]()`
`Compilation fails`	Type mismatch	Check load/store types match
`Segfault`	Misaligned access	Ensure stride is correct

References

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