# Masked Matrix Multiplication for Emergent Sparsity

arXiv: https://arxiv.org/abs/2402.14118 · HTML: https://mlsanity.com/paper/2402.14118/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 21 Feb 2024.

## Builds on (most cited first, 5 of 5)

- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity](https://mlsanity.com/paper/2101.03961/) (2021)
- [The Lazy Neuron Phenomenon: On Emergence of Activation Sparsity in Transformers](https://mlsanity.com/paper/2210.06313/) (2022)
- [Sparse is Enough in Scaling Transformers](https://mlsanity.com/paper/2111.12763/) (2021)
- [DISTAL: The Distributed Tensor Algebra Compiler](https://mlsanity.com/paper/2203.08069/) (2022)

## Abstract (arXiv)

Artificial intelligence workloads, especially transformer models, exhibit emergent sparsity in which computations perform selective sparse access to dense data. The workloads are inefficient on hardware designed for dense computations and do not map well onto sparse data representations. We build a vectorized and parallel matrix-multiplication system A X B = C that eliminates unnecessary computations and avoids branches based on a runtime evaluation of sparsity. We use a combination of dynamic code lookup to adapt to the specific sparsity encoded in the B matrix and preprocessing of sparsity maps of the A and B matrices to compute conditional branches once for the whole computation. For a wide range of sparsity, from 60% to 95% zeros, our implementation performs fewer instructions and increases performance when compared with Intel MKL's dense or sparse matrix multiply routines. Benefits can be as large as 2 times speedup and 4 times fewer instructions.