This is a reference implementation of the Mind Mappings Framework. Mind Mappings performs a gradient-based mapping space search for hardware accelerators.

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To Get started, follow the below steps:

• Install the Mind Mappings package: python3 -m pip install -e .
• Install PyTorch using instruction from here.
• Install Timeloop. Follow instructions.
• Update parameters.py:
  • Point to a temporary path and set self.SCRATCH
  • Set self.TIMELOOP_PATH to point to timeloop path.
• To test if timeloop and mind mappings setup are fine, run python3 costModel/timeloop/model_timeloop.py. This should randomly choose a valid mapping and print its cost.

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Now, everything is setup. Take some time to explore the mind mappings package. costModel directory has models.py, which describes the mandatory functions that any cost model should implement. example/ directory contains a simple example cost model for finding minimum of a quadratic equation. timeloop/ directory shows the mind mappings and timeloop interface.

Everything related to performing mapping space search is handled with optimize.py. As understood from the paper, here are two key phases:

1. Train a Surrogate
2. Use Surrogate for Search

For convenience, two trained surrogate models are already provided for you (in gradSearch/saved_models/): model_CNN-layer.save and model_MTTKRP.save. Each of them are specific to the architecture described in the paper and the related algorithm. In case, you want to target a different architecture/algorithm, they need to be re-trained (steps are provided later).

To perform mapping space search, run:

python3 optimize.py --command search --algorithm CNN-layer --problem 16 512 256 3 3 14 14 --maxsteps 1000

--algorithm can be set to CNN-layer or MTTKRP, --problem should be set to the problem shape (N C K R S P Q/I J K L, see paper for description), --maxsteps can be set to the maximum number of steps you would like the search to run.

This prints out the best mapping and its predicted cost.

• Check the parameters.py file, which controls most of the parameters for the run. Play around with them.

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In case, you would like to train a different surrogate model, follow the steps shown below:

1. Generate Surrogate Dataset: python3 optimize.py --command datagen --path <PATH> --algorithm <ALG> --costmodel <your new cost model>
2. Process the Dataset: python3 optimize.py --command dataprocess --path <PATH> --algorithm <ALG> --costmodel <your new cost model>
3. Train the surrogate model: python3 optimize.py --command train --path <PATH> --algorithm <ALG>
4. Mapping Space Search: python3 optimize.py --command search --algorithm <ALG> --problem <DIMS> --maxsteps <STEPS>

NOTE: To get the best out of mind mappings, you will need to tune search related parameters listed in parameters.py file.

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If you would like to reproduce the results from the paper, you can run:

python3 optimize.py --command reproduce

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If this was useful in your research, please cite:

Hegde, Kartik, Po-An Tsai, Sitao Huang, Vikas Chandra, Angshuman Parashar, and Christopher W. Fletcher. "Mind mappings: enabling efficient algorithm-accelerator mapping space search." In Proceedings of the 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, pp. 943-958. 2021.