SIMD (Single Instruction, Multiple Data)

SIMD is a type of parallel processing that allows a single instruction to be performed on multiple data points simultaneously. It's a form of data-level parallelism that's particularly useful for tasks that require the same operation to be performed on large sets of data.

Basic Concept

Regular Processing:

Data1 -> Operation -> Result1
Data2 -> Operation -> Result2
Data3 -> Operation -> Result3
Data4 -> Operation -> Result4

SIMD Processing:

Data1, Data2, Data3, Data4 -> Operation -> Result1, Result2, Result3, Result4
(All processed simultaneously)

Common Use Cases

• Graphics processing
• Audio/video processing
• Scientific computations
• Matrix operations
• Image processing

Example in Code

// Without SIMD
for(int i = 0; i < 4; i++) {
    result[i] = a[i] + b[i];
}

// With SIMD (conceptual)
simd_add(result, a, b, 4); // Processes all 4 additions at once

Real-world Implementations

• Intel's SSE and AVX instructions
• ARM's NEON technology
• PowerPC's AltiVec
• WebAssembly SIMD

Benefits

• Improved performance
• Better computational efficiency
• Reduced power consumption
• Optimized memory bandwidth usage

TODOs

1. Additional Vectorized Operations

Subtraction, Multiplication, Division

• Extend from simple addition to other arithmetic operations
• For instance, dst[i] = dst[i] * src[i]

Logical/Bitwise Operations

• Implement AND, OR, XOR on slices of uint64
• Sign/zero extension tricks on int64

Floating-Point Support

• Use NEON instructions for 64-bit (or 32-bit) floating-point vectors
• Example: fadd v0.2d, v0.2d, v1.2d for double-precision floats

2. Larger Unrolling & Prefetching

Unrolling

• Process 4 or 8 elements at a time with multiple NEON registers instead of 2 elements (128 bits)
• Reduce loop overhead and improve performance on larger arrays

Prefetching

• Insert prfm instructions to bring future data into cache
• Reduce memory stall for large arrays

Tuning for Microarchitecture

• Different Apple Silicon (M1, M2, etc.) might have different cache or pipeline characteristics
• Experiment with unroll + prefetch combinations for best throughput

3. More Complex Algorithms

Vectorized Dot Product

• Combine multiplication and addition in a single pass
• Return final sum as a single result (summing partial sums in NEON registers)

Matrix/Vector Routines

• Expand beyond 1D arrays
• Write functions for matrix-vector multiplication using NEON in assembly

Convolution / Filtering

• Implement simple convolution filters in NEON for audio or image processing

4. Data Types & Edge Cases

Multiple Data Types

• Provide separate entry points for int32, int16, or float64
• Each type requires different load/store instructions and lane widths

Non-Equal Slice Lengths

• Offer partial overlap or auto-truncation to minimum length
• Improve current simple length equality check

Alignment & Safety

• Investigate misaligned pointers
• Study performance variations with Apple Silicon unaligned accesses

5. Multi-Architecture Support

x86_64 Implementation

• Provide SSE/AVX version for Intel/AMD processors
• Use cgo approach with .asm or .S for x86
• Use Go's build tags or file naming (_arm64 vs. _amd64)

Fallback in Pure Go

• Include fallback implementation for unsupported architectures or older CPUs

6. Concurrency & Parallelism

Goroutine-Based Parallel

• Process large slices in sub-blocks using multiple goroutines
• Each goroutine calls NEON routine on its portion
• Combine results as needed

Work Stealing / Thread Pool

• Build pool for automatic task division among cores
• Optimize for big data arrays

7. Testing & Benchmarking

Go Benchmarks

• Write Benchmark functions in _test.go files
• Measure throughput against pure-Go or scalar-assembly baseline

Profiling Tools

• Use pprof or perf on Linux
• Identify memory bandwidth or compute bottlenecks

Property-based Testing

• Use libraries like github.com/stretchr/testify/require or gotest.tools/assert
• Implement correctness checks on random data sets

8. Packaging & Distribution

Library / Module

• Create reusable Go module
• Add proper versioning and documentation

Cross-Compilation

• Handle separate .S files or use Docker images
• Support compilation for Apple Silicon and Intel

CI Integration

• Set up GitHub Actions or other CI system
• Build and test on multiple OS/architecture combinations

9. Code Cleanliness & Maintenance

In-Source Documentation

• Add detailed comments to assembly code
• Document register usage and loop unrolling strategy

Error Handling / Panics

• Define behavior for length mismatches and negative lengths

Type-Safe API

• Consider custom types (type Int64Slice []int64)
• Define interface for "vectorizable" data

10. Deeper NEON Exploration

Advanced NEON Instructions

• Utilize mla, umlal, fmla, or fused instructions
• Experiment with shuffles and permutations (tbl, zip1, zip2)

SVE (Scalable Vector Extension)

• Explore SVE for non-Apple Silicon ARM platforms
• Consider future direction for ARM HPC

Summary

The project can be expanded in several ways:

• Extend to more data types and operations (floats, bitwise, matrix ops)
• Optimize with deeper unrolling, prefetching, concurrency
• Generalize to multiple architectures (x86_64 vs. ARM64)
• Polish with better testing, profiling, packaging, and CI

By iterating on these ideas, you'll gain deeper insight into low-level performance, ARM64 NEON capabilities, cgo intricacies, and cross-platform deployment.