Improving Cache Performance: Techniques to Enhance CPU Cache Locality

Effective use of CPU cache can significantly enhance the performance of your Go applications. CPU caches are small, fast memory locations that store copies of frequently accessed data to reduce latency. Improving cache locality—both spatial and temporal—helps in better cache utilization, leading to faster execution. This guide provides techniques to improve cache performance in Go applications.

Understanding Cache Locality

Spatial Locality: Accessing data elements that are close to each other in memory. This reduces cache misses as nearby data is likely to be in the same cache line.
Temporal Locality: Accessing the same data element multiple times within a short period. This ensures that the data remains in the cache for faster subsequent access.

Techniques to Improve Cache Performance

Data Layout Optimization

Optimize the layout of data structures to enhance spatial locality. Arrange data that is frequently accessed together to be contiguous in memory.

go
type Point struct {
    X, Y float64
}

// Avoid:
type LargeStruct struct {
    Data1 [1000]int
    Data2 [1000]int
}

// Better:
type InterleavedStruct struct {
    Data1 [1000]int
    Data2 [1000]int
}

Structure of Arrays (SoA) vs. Array of Structures (AoS)

Depending on access patterns, prefer SoA or AoS. SoA can improve cache performance when processing large arrays of data.

go
// Array of Structures (AoS)
type ParticleAoS struct {
    Position [3]float64
    Velocity [3]float64
}
particlesAoS := make([]ParticleAoS, 1000)

// Structure of Arrays (SoA)
type ParticleSoA struct {
    Positions [][3]float64
    Velocities [][3]float64
}
particlesSoA := ParticleSoA{
    Positions: make([][3]float64, 1000),
    Velocities: make([][3]float64, 1000),
}

Prefetching
Prefetch data that will be accessed soon to reduce cache misses. Manual prefetching can be complex and is usually handled by the compiler or CPU. However, understanding access patterns helps the CPU prefetch efficiently.
```
go
// Ensure sequential access patterns for better prefetching
for i := 0; i < len(array); i++ {
    process(array[i])
}
```

Loop Interchange

Reorder nested loops to access memory in a cache-friendly manner. Access elements in the innermost loop to match the memory layout.

go
// Avoid:
for j := 0; j < cols; j++ {
    for i := 0; i < rows; i++ {
        process(matrix[i][j])
    }
}

// Better:
for i := 0; i < rows; i++ {
    for j := 0; j < cols; j++ {
        process(matrix[i][j])
    }
}

Blocking (Loop Tiling)

Break down large loops into smaller blocks that fit into the cache to improve spatial locality.

go
blockSize := 64
for ii := 0; ii < rows; ii += blockSize {
    for jj := 0; jj < cols; jj += blockSize {
        for i := ii; i < ii+blockSize && i < rows; i++ {
            for j := jj; j < jj+blockSize && j < cols; j++ {
                process(matrix[i][j])
            }
        }
    }
}

Padding to Avoid False Sharing
Align data structures to cache line boundaries to prevent false sharing. False sharing occurs when threads on different processors modify variables that reside on the same cache line.
```
go
type PaddedStruct struct {
    Value int
    _     [cacheLineSize - 4]byte // Padding
}
```
Minimize Pointer Chasing
Pointer chasing involves following pointers scattered throughout memory, which can lead to cache misses. Use contiguous memory blocks to reduce pointer chasing.
```
go
// Avoid:
type Node struct {
    Value int
    Next  *Node
}

// Better:
values := make([]int, 1000)
```

Use Cache-Friendly Algorithms

Choose algorithms that maximize cache hits. For example, use iterative algorithms instead of recursive ones to improve locality.

go
// Avoid:
func recursiveSum(arr []int, n int) int {
    if n <= 0 {
        return 0
    }
    return arr[n-1] + recursiveSum(arr, n-1)
}

// Better:
func iterativeSum(arr []int) int {
    sum := 0
    for _, value := range arr {
        sum += value
    }
    return sum
}

By understanding and applying these techniques, you can significantly improve the cache performance of your Go applications, leading to faster and more efficient code execution.