BitArray Support

AdaptiveArrayPools.jl includes specialized support for BitArray (including BitVector and N-dimensional BitArray{N}), enabling ~8x memory savings for boolean arrays compared to standard Vector{Bool}.

The Bit Sentinel Type

To distinguish between standard boolean arrays (Vector{Bool}, 1 byte/element) and bit-packed arrays (BitVector, 1 bit/element), use the Bit sentinel type.

Usage

1D Arrays (BitVector)

For 1D arrays, acquire! returns a native BitVector backed by pooled storage. This design enables full SIMD optimization, making operations like count, any, and bitwise ops significantly faster than views.

@with_pool pool begin
    # Acquire a BitVector of length 1000
    bv = acquire!(pool, Bit, 1000)

    # Use like normal
    bv .= true
    bv[1] = false

    # Supports standard operations with full SIMD acceleration
    count(bv)
end

N-D Arrays (BitArray)

For multi-dimensional arrays, acquire! returns a native BitArray{N} (e.g., BitMatrix for 2D) that shares chunks with pooled storage. This preserves the packed memory layout and SIMD benefits while providing N-D indexing.

@with_pool pool begin
    # 100x100 bit matrix (returns BitMatrix)
    mask = zeros!(pool, Bit, 100, 100)

    mask[5, 5] = true

    # 3D BitArray
    volume = acquire!(pool, Bit, 10, 10, 10)
end

Convenience Functions

For specific BitVector operations, prefer trues! and falses! which mirror Julia's standard functions:

@with_pool pool begin
    # Filled with false (equivalent to `falses(256)`)
    mask = falses!(pool, 256)

    # Filled with true (equivalent to `trues(256)`)
    flags = trues!(pool, 256)

    # Multidimensional
    grid = trues!(pool, 100, 100)

    # Similar to existing BitArray
    A = BitVector(undef, 50)
    B = similar!(pool, A)  # Reuses eltype(A) -> Bool

    # To explicit get Bit-packed from pool irrespective of source
    C = similar!(pool, A, Bit)
end

Note: zeros!(pool, Bit, ...) and ones!(pool, Bit, ...) are also supported (aliased to falses! and trues!).

Performance & Safety

Why Native BitArray?

The pool returns native BitVector/BitArray types instead of SubArray views for performance. Operations like count(), sum(), and bitwise broadcasting are 10x~100x faster on native bit arrays because they utilize SIMD instructions on packed 64-bit chunks.

N-D Caching & Zero Allocation

The pool reuses BitArray{N} wrapper instances via setfield!-based in-place mutation (Julia 1.11+) or N-way cache (Julia 1.10 / CUDA):

On Julia 1.11+, BitArray fields (len, dims, chunks) are mutated in-place via setfield!, achieving zero allocation on all repeated calls regardless of dimension pattern.

@with_pool pool begin
    # First call: allocates BitMatrix wrapper (~944 bytes)
    m1 = acquire!(pool, Bit, 100, 100)

    # Rewind to reuse the same slot
    rewind!(pool)

    # Same dims: 0 allocation (cached wrapper reused)
    m2 = acquire!(pool, Bit, 100, 100)

    rewind!(pool)

    # Different dims but same ndims: 0 allocation (fields updated in-place)
    m3 = acquire!(pool, Bit, 50, 200)
end

⚠️ Important: Do Not Resize

While the returned arrays are standard BitVector types, they share their underlying memory chunks with the pool.