https://github.com/MachineLearningSystem/spada-sim

The simulator for SPADA, an SpGEMM accelerator with adaptive dataflow
https://github.com/MachineLearningSystem/spada-sim

Last synced: about 2 months ago
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The simulator for SPADA, an SpGEMM accelerator with adaptive dataflow

• Host: GitHub
• URL: https://github.com/MachineLearningSystem/spada-sim
• Owner: MachineLearningSystem
• Fork: true (tsinghua-ideal/spada-sim)
• Created: 2023-02-04T02:17:59.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2023-01-26T02:15:09.000Z (almost 2 years ago)
• Last Synced: 2024-08-02T19:36:45.998Z (5 months ago)
• Size: 572 KB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-AI-system - Spada: Accelerating Sparse Matrix Multiplication with Adaptive Dataflow ASPLOS'23

README

        
# Spada simulator

## Install

Please first install the [Rust toolchain](https://www.rust-lang.org/tools/install).

The simulator interacts with [python3](https://www.python.org/downloads/) for parsing sparse matrices:

```bash

$ python3 -m venv spadaenv

$ source spadaenv/bin/activate

$ pip install -U pip numpy scipy

```

## Build

```bash

$ cargo build --no-default-features

```

## Workload

The simulator accepts both MatrixMarket (.mtx) and numpy formatted matrices, with the latter ones packed as a pickle file (.pkl). The folder containing these matrices is specified in the config file under `config`.

## Simulate

First ensure the created python virtual environment is activated. The following command simulates SpGEMM of [cari](https://sparse.tamu.edu/Meszaros/cari) on Spada with the configuration specified in `config/config_1mb_row1.json`.

```bash

(spadaenv) $ ./target/debug/spada-sim accuratesimu spada ss cari config/config_1mb_row1.json

```

## Reference

If you use this tool in your research, please kindly cite the following paper.

Zhiyao Li, Jiaxiang Li, Taijie Chen, Dimin Niu, Hongzhong Zheng, Yuan Xie, and Mingyu Gao.

Spada: Accelerating Sparse Matrix Multiplication with Adaptive Dataflow.

In *Proceedings of the 28th International Conference on Architectural Support for Programming Languages and Operating Systems* (ASPLOS), 2023.