文件列表:

Dockerfile (840, 2022-10-31)
LICENSE (1068, 2022-10-31)
lisa.yml (3866, 2022-10-31)
lisa_gnn (0, 2022-10-31)
lisa_mapper.patch (488154, 2022-10-31)
morpher_mapper (0, 2022-10-31)
overview.jpg (453436, 2022-10-31)

# LISA LISA, **L**earning **I**nduced mapping for **S**patial **A**ccelerators, is a portable framework to map DFG (dataflow graph, representing an application) on spatial accelerators. For a new spatial accelerator, LISA can automatically tune its parameters to adapt to the accelerator characteristics to generate high quality mapping. Please find the [paper](https://www.comp.nus.edu.sg/~tulika/HPCA_LISA_2022.pdf) for more rerefence. ## Table of contents 1. [Overview](#overview) 1. [Directory Structure](#directory-structure) 2. [API and scripts](#api-and-scripts) 2. [Getting Started](#getting-started) 1. [Requirement](#requirement) 2. [How to install](#how-to-install) 3. [Running Example](#running-example) 3. [Portability- The workflow to use LISA for a new accelerator](#portability) 3. [Reference](#publication) # Overview This picture shows the whole frameowork: \ Overview of LISA framework: 1. Generate training data 2. Build GNN Model. 3. GNN-based label-aware mapping. ### Directory Structure We implment the mapper component in [CGRA-ME](https://cgra-me.ece.utoronto.ca/). GNN-related stuff is stored in `lisa_gnn` folder. ``` lisa │ README.md │ lisa_mapper.patch │ overview.jpg └───lisa_gnn (GNN related stuff: train dataset, GNN model, and DFG generator) │ │ │ │───data (graph data and label data) │ │ │ label_filter.py │ │ │─── cgra_me (graph data for CGRA-ME) │ │ │ |─── cgra_me (random graphs in CGRA-ME format) │ │ │ |─── graph (random graphs generated by DFG_generator, it is a standarded format) │ │ │ └─── transformered_graph (graph attributes generated by Attributes Generator) │ │ │ │ │ │─── labels (traning label data. We provide some examples.) │ │ │ |─── cgra_me_3_3 (3x3 CGRA label, and CGRA is modeled by CGRA-ME) │ │ │ └─── cgra_me_4_4 (4x4 CGRA label, and CGRA is modeled by CGRA-ME) │ │ │ │ │ │─── infer (the directory to store temporary DFGs.) │ │ │ │ │───***_generator (Generate train set) │ │ │ │ └───lisa_gnn_model (GNN models) │ │ │─── gnn_inference.py │ │ │─── run_training.sh │ │ └───cgra_me (Not included in the original github repo. Need to download it) | call_gnn.sh | run_exper.py ``` ### API and scripts * gnn_inference.py: This python file has two arguments: graph name and architecture name. Example: ``python gnn_inference.py 0VIHB3KrVi cgra_me_4_4``. The grpah is stored in ``lisa/lisa_gnn_model/data/infer/0VIHB3KrVi.txt``. And ``cgra_me_4_4`` represents the arch name. * run_training.sh: This shell file builds all the GNN models for a given accelerator. For example: ``bash run_training.sh cgra_me_4_4`` * call_gnn.sh (in ``cgra_me``): The script to call GNN inference through invoking gnn_inference.py. It has two arguments: graph name and architecture name. * run_exper.py (in ``cgra_me``): ``python run_exper.py method target_arch ***_type cpu_core_number`` \ -method: gnn_lisa, gnn_training_data, baseline. 'gnn_lisa' represents LISA mapping (gnn-based label-aware mapping), gnn_training_data means generate labesl for train set, baseline incldues ILP and SA. \ -target_arch: We have evaluated LISA on 6 accelerators as mentioned in Section VI, page 9. We use index 0-6 to represent these accelerators. 0: 4x4 CGRA; 1: 3x3 CGRA; 2: 4x4 CGRA with less routing resource; 3: 4x4 CGRA with less memory connectivity; 4: 8x8 CGRA; 5: systolic array. \ -***_type: 0 or 1. O reprsents original DFGs, and 1 is unrolled DFGs. \ -cpu_core_number: if you are running gnn_lisa, make sure at least 13 CPU cores are available. For baseline, make sure at least 26 CPU cores are available. # Getting started ## Requirement: * Ubuntu (we have tested Ubuntu 16.04 and 18.04) * We provide two ways to install LISA: 1) Build on your machine. You need to install Anaconda and [CGRA-ME](https://cgra-me.ece.utoronto.ca/) related dependencies. 2) Use Docker. ## How to install ### Build on your machine * Download source code: ``$ git clone --recurse-submodules https://github.com/ecolab-nus/lisa.git``. * Make sure you have installed **Anaconda**. * Go to ``lisa`` directory. Create LISA environment with: ``$ conda env create -f lisa.yml``. * Download [CGRA-ME](https://cgra-me.ece.utoronto.ca/) into ``lisa/`` (the location of github repo) and rename as ``cgra_me``. Please follow the tutorial in CGRA-ME to install dependencies, build it, and run the example. * Apply our mapper patch for CGRA-ME (in ``lisa/cgra_me``): ``$ patch -p1<../lisa_mapper.patch``. Rebuild cgra_me. ### Build with Docker * Download the [docker file](https://github.com/ecolab-nus/lisa/blob/main/Dockerfile) and [conda envireonment](https://github.com/ecolab-nus/lisa/blob/main/lisa.yml). Create an empty folder and put the above two files into the folder. * Build lisa image: ``$ docker build ./ -t lisa``. This takes around 15 minutes. * Initalize: ``$ docker run --name lisa_ae -it lisa`` * Start the container: ``$ docker start lisa_ae`` * Get into the container: ``docker exec -it lisa_ae /bin/bash`` * Download [CGRA-ME](https://cgra-me.ece.utoronto.ca/), decompress it into ``/home/lzy/lisa/``, and rename as ``cgra_me``. As we have installed all the software dependencies in the docker image, downloading CGRA-ME is enough. * Apply our mapper patch for CGRA-ME (in ``lisa/cgra_me``): ``$ patch -p1<../lisa_mapper.patch`` * Build new CGRA-ME (in ``lisa/cgra_me``): 1) Activate environment: ``$ ./cgrame_env``. 2) Build: ``$ make -j`` ## Running Example: We provide an example to map DFG using LISA. \ To reproduce the results, please check the appendix of our paper: **LISA: Graph Neural Network based Portable Mapping on Spatial Accelerators**. ### Map DFG using LISA We use CGRA_ME and 4x4 CGRA for the follwing examples. Here, we show how to map one DFG with LISA, i.e., GNN-based label-aware mapping, assuming we have generated the training data set and build GNN model. 1. Go to cgra_me directory (lisa/cgra_me). Activate environment by ``./cgrame_env``. 2. Activate environment: ``conda activate lisa`` 3. Run the mapper. ``$CGRA_MAPPER -m 2 --II 20 --inef --arch_name cgra_me_4_4 -g ./benchmarks/polybench/cholesky/my_graph_loop.dot --xml ./arch/simple/target_arch/arch-homo-orth_4_4.xml --cgra_x 6 --cgra_y 6`` (Note, as the outmost PEs in this arch are I/O ports, we mark the CGRA as 4x4 CGRA though it has 6x6 size.) # Portability **The workflow to use LISA for a new accelerator.** Let us say we have a new accelerator- 8x8 CGRA in CGRA-ME. And we name it as cgra_me_8_8. We have created this arch in cgra_me ( ``cgra_me/arch/simple/target_arch/arch-homo-orth_8_8.xml ``). ## Generate GNN train set for LISA. * Generate training graph (lisa/lisa_gnn/***_generator): ``$ python ***_generator.py -n 1000 ``. You can skip this step, as all the architectures in cgra-me share the same graphs. * Generate training labels (lisa/cgra_me): ``$ python run_exper.py gnn_training_data 1 1000``. All the labels will be generated under folder ``lisa_gnn/data/labels/cgra_me_8_8/``. This step is very time-consuming, where each label can take around hours. See [API and Scripts](#api-and-scripts) to utilize more cores and set which DFGs to train. * Filter labels and generate train set(in lisa/lisa_gnn/data): ``$ python label_filter.py -r cgra_me/transformered_graph/ -l cgra_me_8_8``. This will generate a train set ``cgra_me_8_8`` in ``data/training_dataset/`` directory ## Build LISA GNN Models. * Activate lisa environment: ``$ conda activate lisa``. * Train GNN model (lisa/lisa_gnn/lisa_gnn_model): ``$ bash run_training.sh cgra_me_8_8``. GNN model will be saved in each label directory. ## Map using GNN-derived label * Activate the environment and run the mapper: ``$CGRA_MAPPER -m 2 --II 20 --inef --arch_name cgra_me_8_8 -g ./benchmarks/microbench/conv2/my_graph_loop.dot --xml ./arch/simple/target_arch/arch-homo-orth_8_8.xml --cgra_x 10 --cgra_y 10`` # Publication ``` @inproceedings{li2022lisa, title={LISA: Graph Neural Network based Portable Mapping on Spatial Accelerators }, author={Li, Zhaoying and Wu, Dan and Wijerathne, Dhananjaya and Mitra, Tulika}, booktitle={2022 IEEE International Symposium on High-Performance Computer Architecture (HPCA)}, year={2022}, organization={IEEE} } ```

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