sparse-convolution 0.4.0

Sparse convolution in python using Toeplitz convolution matrix multiplication.

sparse_convolution

Sparse 2D convolution in Python via Toeplitz matrix methods.

Fast when the kernel is small, the input is sparse, and/or many arrays share the same kernel.

Install

pip install sparse_convolution

Or from source:

git clone https://github.com/RichieHakim/sparse_convolution
cd sparse_convolution
pip install -e .

Usage

Single image

import numpy as np
import scipy.sparse
import sparse_convolution as sc

x = scipy.sparse.random(100, 100, density=0.01)
k = np.random.rand(5, 5)

conv = sc.Toeplitz_convolution2d(x_shape=x.shape, k=k, mode='same')
out = conv(x=x, batching=False).toarray()

Batched

Input: (n_images, H * W) sparse matrix. Output: (n_images, H_out * W_out).

x_batch = scipy.sparse.vstack([
    scipy.sparse.random(100, 100, density=0.01).reshape(1, -1)
    for _ in range(50)
]).tocsr()

conv = sc.Toeplitz_convolution2d(x_shape=(100, 100), k=k, mode='same')
out = conv(x=x_batch, batching=True)

Methods and backends

Four methods, each with selectable backends:

Method	numpy	numba	torch
direct	n/a	yes	n/a
precomputed	yes	yes	yes
lazy	yes	n/a	yes
gather_scatter	yes	yes	yes

• direct (default): Batch-parallel scatter convolution with thread-local dense buffers (numba only). For each image in parallel, scatters kernel-weighted input values into an L2-cache-sized accumulator buffer, then extracts nonzeros into CSR format. Uses a two-phase approach: a lightweight boolean counting pass (1-byte flags, no float arithmetic) determines exact output sizes, then the scatter pass writes directly to right-sized arrays with zero waste. Interior pixels (~92-100%) skip bounds checking entirely via precomputed safe regions. O(nnz × K) per image with no init overhead. Fastest method across nearly all configurations. Requires numba.
• precomputed: Builds a sparse Toeplitz matrix at init; fast batched matmul. Best for large batches with the same kernel when numba is not available.
• lazy: COO broadcasting, no init cost. Best for very sparse inputs with small batches.
• gather_scatter: Per-kernel-position scatter into a dense accumulator. General-purpose method for sparse batched inputs.

Backend selection:

• numpy: scipy/numpy ops. Always available.
• numba: JIT-compiled parallel loops. Fastest on CPU for batched inputs. Requires numba.
• torch: PyTorch ops with optional GPU. Requires torch.

conv = sc.Toeplitz_convolution2d(
    x_shape=(100, 100),
    k=k,
    mode='same',
    method='direct',       # default
    backend='numba',       # auto-selected for direct
)

If backend=None (default), auto-selects numba for direct and gather_scatter (if installed), numpy otherwise.

References

• Toeplitz convolution: stackoverflow.com/a/51865516, alisaaalehi/convolution_as_multiplication
• 1D convolution matrix: scipy.linalg.convolution_matrix

Benchmarks

All benchmarks run on CPU with 1s minimum measurement time per configuration (median reported). Nine method+backend combinations compared across six scaling sweeps.

Scaling overview

Six scaling sweeps varying batch size, density, image size, and kernel size. direct+numba (brown stars) is the fastest method in nearly all regimes.

Grid search: fastest method per configuration

Each cell shows the winning method and total time (init + call) for that batch size × density combination. direct+numba wins 28 of 36 configurations with an average 4.75× speedup over the second-fastest method.

Individual scaling curves

Batch size scaling — 100×100, 5×5, density=0.01

Density scaling — 100×100, 5×5, batch=100

Image size scaling — 5×5, density=0.01, batch=50

Kernel size scaling — 100×100, density=0.01, batch=50

Batch scaling — high density (0.1)

Batch scaling — very sparse (density=0.001, 200×200)