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updated README, added benchmark and plotting functions

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Johannes Erwerle 2022-09-15 19:48:57 +02:00
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README.md
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@ -20,7 +20,7 @@ Reading the data from an OSM PDF file and converting it to a graph is done in
`src/bin/generate_grid.rs`.
The implementation of the spherical point in polygon test is done in `src/polygon.rs`
with the function `contains()`.
with the function `Polygon::contains`.
There is one polygon in the graph, for which no valid outside polygon can be found.
I did not have the time to investigate this further.
@ -29,8 +29,7 @@ I did not have the time to investigate this further.
The code uses the osmpbfreader crate.
Sadly this module uses ~10GB of memory to extract the data from the PBF file
with all the coastlines. So far I did not have time to look into what happens
there.
with all the coastlines.
### Point in Polygon
@ -50,9 +49,9 @@ Import and Export from/to a file can be done with the `from_fmi_file` and `write
### Dijkstra Benchmarks
The dijkstras algorithm is implenented in `gridgraph.rs`.
Dijkstras algorithm is implenented in `gridgraph.rs` with `GridGraph::shortest_path`.
It uses a Heap to store the nodes.
On details on how to run benchmarks see the benchmarks session at the end.
For details on how to run benchmarks see the benchmarks section at the end.
## Task 6
@ -76,6 +75,9 @@ I implemented ALT, as described in [1].
Additionally A\* is available with a simple, unoptimized haversine distance
as the heuristic.
A\* is implemented in `src/astar.rs` and the heuristics for ALT are implemented
in `src/alt.rs`.
### Landmarks for ALT
currently 3 different landmark generation methods are available
@ -94,7 +96,7 @@ generates landmarks for 4, 8, 16, 32 and 64 landmarks, both greedy and random.
# Running the benchmarks
First a set of queries is needed.
This can be done with the `generate_benchmark_targets --graph <graph> > targets.json`.
These can be generated with `generate_benchmark_targets --graph <graph> > targets.json`.
This generates 1000 random, distinct source and destination pairs.
The `--amount` parameter allows to adjust the number of pairs generated.
@ -110,4 +112,11 @@ are used to answer the query.
The benchmark prints out how many nodes were popped from the heap for
each run and the average time per route.
[1] Computing the Shortest Path: A* meets Graph Theory, A. Goldberg and C. Harrelson, Microsoft Research, Technical Report MSR-TR-2004-24, 2004
`utils/run_benchmarks.py` is a wrapper script that runs the benchmarks for a
big set of parameters.
`utils/plot_results.py` generates several plots of the results.
# References
[1](Computing the Shortest Path: A\* meets Graph Theory, A. Goldberg and C. Harrelson, Microsoft Research, Technical Report MSR-TR-2004-24, 2004)