https://github.com/cornell-zhang/graphlily
A graph linear algebra overlay
https://github.com/cornell-zhang/graphlily
fpga-programming graph-processing high-level-synthesis open-source-hardware
Last synced: 5 days ago
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A graph linear algebra overlay
- Host: GitHub
- URL: https://github.com/cornell-zhang/graphlily
- Owner: cornell-zhang
- License: bsd-3-clause
- Created: 2021-06-18T04:50:37.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2023-04-26T17:30:30.000Z (about 3 years ago)
- Last Synced: 2025-09-10T05:42:33.532Z (8 months ago)
- Topics: fpga-programming, graph-processing, high-level-synthesis, open-source-hardware
- Language: C++
- Homepage:
- Size: 117 MB
- Stars: 51
- Watchers: 13
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
GraphLily: A Graph Linear Algebra Overlay on HBM-Equipped FPGAs
===============================================================
GraphLily is the first FPGA overlay for graph processing.
GraphLily supports a rich set of graph algorithms by adopting the GraphBLAS programming interface, which formulates graph algorithms as sparse linear algebra kernels.
GraphLily effectively utilizes the high bandwidth of HBM to accelerate SpMV and SpMSpV, the two widely-used kernels in GraphBLAS, by co-designing the data layout and the accelerator architecture.
GraphLily further builds a middleware to provide runtime support, enabling users to easily port existing GraphBLAS programs from CPUs/GPUs to FPGAs.
For more information, refer to our [ICCAD'21 paper](https://www.csl.cornell.edu/~zhiruz/pdfs/graphlily-iccad2021.pdf).
```
@article{hu2021graphlily,
title={GraphLily: Accelerating Graph Linear Algebra on HBM-Equipped FPGAs},
author={Hu, Yuwei and Du, Yixiao and Ustun, Ecenur and Zhang, Zhiru},
journal={International Conference On Computer Aided Design},
year={2021}
}
```
## Prerequisites
- Platform: Xilinx Alveo U280
- Tool: Xilinx Vitis 2019.2
## Run Benchmarking
### Clone the repo
```
git clone git@github.com:cornell-zhang/GraphLily.git
export GRAPHLILY_ROOT_PATH=/path/to/GraphLily
```
### Get the bitstream
- A pre-compiled bitstream (166 MHz) is provided [here](https://drive.google.com/file/d/1OGry0OtbvmGiSirhJy3tCPz51VMeV1HM/view?usp=sharing).
- To generate a new bitstream:
```
cd GraphLily/generate_bitstream
make synthesize
```
### Prepare datasets
The input is an adjacency matrix in csr format stored as a scipy npz file. Please install [cnpy](https://github.com/rogersce/cnpy), which is required for data loading.
Our ICCAD'21 paper evaluated the following six graph datasets:
- [googleplus](https://drive.google.com/file/d/1Wv9C7s0lK0KdrRPUsTqjlENvbMMKfykg/view?usp=sharing)
- [ogbl-ppa](https://drive.google.com/file/d/189Qp9h4BxXR8dAiQdmJWkW89y08eU5qR/view?usp=sharing)
- [hollywood](https://drive.google.com/file/d/1irBTVuYdJaMXQTUGQh7AerBjs784ykeO/view?usp=sharing)
- [pokec](https://drive.google.com/file/d/1UEwsIYgNWmm3ucBfatjg_lmG25oXWWI-/view?usp=sharing)
- [ogbn-products](https://drive.google.com/file/d/1yBJjW5aRpJt2if32gOWSmaYcI10KDQj0/view?usp=sharing)
- [orkut](https://drive.google.com/file/d/1Am0hPLhGNAwjYWt5nd_-XsIaKBiWcwqt/view?usp=sharing)
### Run
Go to the GraphLily/benchmark folder, modify the cnpy path in Makefile, modify the bitstream path and the datasets path in run_bfs.sh, then:
```
bash run_bfs.sh
```