SparseFlow v0.7.1 – SPA + CPU Runtime + Python CLI (Dev Preview)

This release packages the current SparseFlow stack into a usable, reproducible demo
for researchers, compiler engineers, and hardware partners.

Highlights

SPA v0.6 (2D Sparsity)
- Propagates row + column sparsity through a small MLIR pipeline
- Exports JSON metadata with row/col masks and sparsity stats
- Proven on structured sparse matmuls with 50% row + 50% col sparsity (≈75% FLOP reduction)
C++ CPU Runtime (OpenMP)
- Consumes SPA JSON masks and skips zero rows/cols
- Blocked matmul kernel with OpenMP parallelism
- Achieves ≈3–5× speedup on larger matmuls (512–1024) at ~75% sparsity
- Honest behavior: small sizes (<512) can be 1–3× due to OpenMP and cache overhead
End-to-End Demo Script
- ./spa-runner.sh:
  - Rebuilds passes/runtime if needed
  - Runs SPA on test MLIR
  - Exports spa_sparsity.json
  - Benchmarks dense vs sparse CPU matmuls

Python CLI (Developer Preview)
Install:

cd sparseflow_package
pip install -e .

Tools:

sparseflow-demo
sparseflow-analyze tests/test_spa_v6_full_2d.mlir
sparseflow-benchmark

git clone https://github.com/MapleSilicon/SparseFlow.git
cd SparseFlow
./spa-runner.sh