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analysis of the original IOTA IRI implementation for cumulative weight calculation

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A Practical Implementation of the Depth-First Search Algorithm in Tip Selection for the IOTA Distributed Ledger

to run the snapshot:

git clone https://[email protected]/andrasferenczi7/dfs_iota_research.git
cd dfs_iota_research
npm install

To save a tangle snashot, run:

npm run tangle

Running the unit/integration tests:

npm run test`

Test harness:

npm run stats3

To generate reports, you need to have python 3.7x installed along with mathplotlib, scipy and numpy

cd reports/
python report_iota_snapshot.py

For performance benchmarks:

// ALGO variable must be set. can have values: calculateRatingBfs, calculateRatingDfs

ALGO=calculateRatingBfs npm run stats0

For performance benchmarks build docker:

docker build -t my_image:v1 .
docker run -e "ALGO_NAME=BFS" my_image:v1
docker ps
docker exec -it <container id> /bin/bash

To clean up:

docker rm $(docker ps -a -q)
docker rmi $(docker images -q)

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