292 lines
8.0 KiB
Julia
292 lines
8.0 KiB
Julia
using DataFrames, RCall, Statistics, Plots, Base.Threads, CSV, CategoricalArrays
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prefix = "barite_fgcs"
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input_dir = "barite/"
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timing_file = "timings.qs2"
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ema_file = "ema.out.csv"
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relevant_timings = ["Chemistry", "Phreeqc", "PHT_read", "PHT_write", "DHT_gather", "Interpolation", "Energy"]
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grid_size = 400 * 400
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iterations = 5000
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calculations = grid_size * iterations
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output_dir = "timings"
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if !isdir(output_dir)
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mkdir(output_dir)
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end
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function read_timings(timing_file)
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res = nothing
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@rput timing_file
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R"res <- qs2::qs_read(timing_file)"
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@rget res
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return res
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end
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function read_ema(ema_file)
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if (!isfile(ema_file))
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return DataFrame()
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end
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return CSV.read(ema_file, DataFrame)
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end
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function eval_timings_interp(timings)
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chemistry = timings[:chemistry]
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time_vec = [chemistry[:simtime],
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sum(chemistry[:phreeqc_time]),
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sum(chemistry[:interp_w]),
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sum(chemistry[:interp_r]),
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sum(chemistry[:interp_g]),
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sum(chemistry[:interp_fc])]
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interp_df = DataFrame(Calls=chemistry[:interp_calls],
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Cached=chemistry[:interp_cached])
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return time_vec, interp_df
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end
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function eval_ema(ema_df)
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if (nrow(ema_df) == 0)
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return 0.0
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end
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only_package = filter(row -> occursin(r"package-\d+", row[:device_name]), ema_df)
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return sum(only_package[!, :energy]) * 1e-6 * (1 / 3600)
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end
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function eval_timings_ref(timings)
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chemistry = timings[:chemistry]
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time_vec = [chemistry[:simtime],
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sum(chemistry[:phreeqc_time]),
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0, 0, 0, 0]
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return time_vec
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end
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function find_all_directories(input_dir, prefix)
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result_dirs = readdir(input_dir, join=true)
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result_dirs = filter(x -> occursin(prefix, x), result_dirs)
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return result_dirs
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end
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function workhorse_loop(dirs)
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df_timings = DataFrame()
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df_interp = Dict{Tuple{Int,Int},DataFrame}()
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for dir in dirs
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metainfo = split(basename(dir), "_")
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type = nothing
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aqueous = 0
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minerals = 0
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energy = eval_ema(read_ema(joinpath(dir, ema_file)))
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if (metainfo[3] == "ref")
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type = "Reference"
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timings = read_timings(joinpath(dir, timing_file))
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time_vec = eval_timings_ref(timings)
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else
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aqueous = parse(Int, metainfo[3])
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minerals = parse(Int, metainfo[4])
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type = "(" * string(aqueous) * "," * string(minerals) * ")"
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timings = read_timings(joinpath(dir, timing_file))
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time_vec, interp_df = eval_timings_interp(timings)
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df_interp[(aqueous, minerals)] = interp_df
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end
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push!(time_vec, energy)
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new_df = DataFrame(relevant_timings .=> time_vec)
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local_df = DataFrame(Type=type, Aqueous=aqueous, Minerals=minerals)
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local_df = hcat(local_df, new_df)
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append!(df_timings, local_df)
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end
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return df_timings, df_interp
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end
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function eval_additional_info(df, interp_dict, grid_size, iterations)
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interp_full = Vector()
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for row in eachrow(df)
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if (row[:Type] != "Reference")
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interp_count = sum(interp_dict[(row[:Aqueous], row[:Minerals])][!, :Calls])
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push!(interp_full, interp_count)
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else
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push!(interp_full, 0)
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end
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end
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calculations = grid_size * iterations
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phreeqc_calls = calculations .- interp_full
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time_per_phreeqc = (df[!, :Phreeqc] ./ phreeqc_calls)
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df = hcat(df, DataFrame(Phreeqc_calls=phreeqc_calls, Time_per_Phreeqc=time_per_phreeqc, Interp_calls=interp_full))
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return df
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end
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function color_from_aqueous!(df)
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my_colors = palette(:viridis, 4)
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df[!, :Type] = CategoricalArray(df[!, :Type])
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color_vec = Vector()
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for row in eachrow(df)
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if (row[:Type] == "Reference")
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push!(color_vec, my_colors[1])
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else
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push!(color_vec, my_colors[row[:Aqueous]])
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end
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end
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df[!, :Colors] = color_vec
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end
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dirs = find_all_directories("barite/", "barite_fgcs")
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df_timings, df_interp = workhorse_loop(dirs)
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group = groupby(df_timings, [:Type, :Aqueous, :Minerals])
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combine_df = combine(group,
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relevant_timings .=> mean .=> relevant_timings,
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)
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df_timings = eval_additional_info(combine_df, df_interp, grid_size, iterations)
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CSV.write("$output_dir/timings_barite.csv", df_timings)
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for (key, value) in df_interp
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CSV.write("$output_dir/interp_barite_$(key[1])_$(key[2]).csv", value)
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end
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color_from_aqueous!(df_timings)
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# plot with different colors and shapes in bars
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bar(df_timings[!, :Type],
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df_timings[!, :Chemistry],
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color=df_timings[!, :Colors],
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xguidefontsize=8,
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yguidefontsize=8,
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xtickfontsize=6,
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ytickfontsize=6,
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legend=false,
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ylabel="Time [s]",
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xlabel="Simulation Type",
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formatter=:plain,
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yminorgrid=true,
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ylimits=(0, 60000))
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savefig("$output_dir/timings_barite.pdf")
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function get_count_of_id2(initqsfile)
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res = nothing
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@rput initqsfile
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R"res <- qs2::qs_read(initqsfile)"
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@rget res
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id = res[!, :ID]
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return count(x -> x == 2.0, id)
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end
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bar(df_timings[!, :Type], df_timings[!, :Energy],
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color=df_timings[!, :Colors],
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xguidefontsize=8,
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yguidefontsize=8,
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xtickfontsize=6,
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ytickfontsize=6,
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legend=false,
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formatter=:plain,
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yminorgrid=true,
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ylimits=(0, 12000),
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yticks=[0, 2000, 4000, 6000, 8000, 10000, 12000],
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yminorticks=4,
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ylabel="Energy [Wh]", xlabel="Simulation Type")
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savefig("$output_dir/energy_barite.pdf")
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max_count = get_count_of_id2("barite/barite_fgcs_ref_128_3/iter_0000.qs2")
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interp_3_1 = df_interp[(3, 1)]
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plot(interp_3_1[!, :Calls],
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color=:black,
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xguidefontsize=8,
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yguidefontsize=8,
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xtickfontsize=6,
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ytickfontsize=6,
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legend=false,
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ylimits=(68000, 82000),
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yminorgrid=true,
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yminorticks=4,
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formatter=:plain,
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ylabel="Interpolation Calls",
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xlabel="Iteration")
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hline!([max_count],
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color=:red,
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linestyle=:dash,
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label="Mean")
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savefig("$output_dir/interp_calls_3_1_clipped.pdf")
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prefix = "dolo_inter"
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input_dir = "dolomite/"
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dirs = find_all_directories("dolomite/", "dolo_inter")
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df_timings, df_interp = workhorse_loop(dirs)
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group = groupby(df_timings, [:Type, :Aqueous, :Minerals])
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combine_df = combine(group,
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relevant_timings .=> mean .=> relevant_timings,
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)
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df_timings = eval_additional_info(combine_df, df_interp, grid_size, iterations)
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CSV.write("$output_dir/timings_dolomite.csv", df_timings)
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for (key, value) in df_interp
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CSV.write("$output_dir/interp_dolomite_$(key[1])_$(key[2]).csv", value)
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end
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color_from_aqueous!(df_timings)
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bar(df_timings[!, :Type], df_timings[!, :Chemistry],
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color=df_timings[!, :Colors],
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xguidefontsize=8,
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yguidefontsize=8,
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xtickfontsize=6,
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ytickfontsize=6,
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legend=false,
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formatter=:plain,
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yminorgrid=true,
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ylim=(0, 15000),
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yticks=0:2500:15000,
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ylabel="Time [s]", xlabel="Type")
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savefig("$output_dir/timings_dolomite.pdf")
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bar(df_timings[!, :Type], df_timings[!, :Energy],
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color=df_timings[!, :Colors],
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xguidefontsize=8,
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yguidefontsize=8,
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xtickfontsize=6,
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ytickfontsize=6,
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legend=false,
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formatter=:plain,
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yminorgrid=true,
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ylim=(0, 3000),
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ylabel="Energy [Wh]", xlabel="Type")
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savefig("$output_dir/energy_dolomite.pdf")
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