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README.md

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To set up dependencies

julia> ] instantiate

To evaluate codes and decoders and store raw data

If you want to make previously executed runs available in the local database run git restore --source evaluation_results_branch codes/results.sqlite. If you want to start from scratch, just disregard this command.

Add the codes and decoders of interest to _0.helpers_and_metadata/code_metadata.jl then

julia> include("wiki_database_passes.jl")
julia> run_evaluations(CodeMetadata.code_metadata)

If you want to run only some codes, e.g. the code family CodeType, you can use run_evaluations(code_metadata; include=[CodeType]).

To generate plots and markdown entries

julia> include("wiki_database_passes.jl")
julia> prep_everything(CodeMetadata.code_metadata)

To generate the website for local viewing

Run the Franklin static website generator

julia> using Franklin
julia> Franklin.serve()

Folder structure

This is a Franklin.jl static website, together with the following extra passes for generating the source of the static pages:

  • _0.helpers_and_metadata - the base metadata about codes and decoders as well as some low-level helper functions for working with that metadata and the sqlite database of results
  • _1.code_benchmark_pass - for running benchmarks and story the performance data to the database
  • _2.markdown_generation_pass - for reading the database and creating figures and raw markdown pages
  • wiki_database_passes.jl - all of the functionality necessary for running the aforementioned capabilities
  • codes - where the generated static website sources are kept
  • database - where the database of benchmarks is stored (in sqlite as master format, and a few other formats for convenient downloading)

ENV variables

  • if ENV["ECCBENCHWIKI_QUICKCHECK"]!="" we will run very few samples per code, useful to check for overall correctness

Running on Slurm Clusters

  1. Set up Julia environment to evoid repeatedly setting up dependencies. You can do this by setting the JULIA_DEPOT_PATH environment variable to a directory on your HPC cluster where you want to store Julia packages. Optionally, set JULIA_NUM_PRECOMPILE_TASKS and JULIA_PKG_PRECOMPILE_AUTO to avoid precompilation overhead. For example, you can add the following lines to your startup script:
export JULIA_DEPOT_PATH="/path/to/your/julia/depot"
export JULIA_NUM_PRECOMPILE_TASKS=1
export JULIA_PKG_PRECOMPILE_AUTO=0
  1. You can set up project environment and instantiate dependencies in Julia REPL before running the benchmarks.