Remove duplicate code in scaling plots, add debug prints

Update post_processing/plot_gpu_scaling_utils.jl

Co-authored-by: Nat Efrat-Henrici <60049837+nefrathenrici@users.noreply.github.com>

post_processing/plot_gpu_scaling_utils.jl

@@ -0,0 +1,37 @@
+using JLD2
+function get_jld2data(output_dir, job_id, s)
+    FT = Float64
+    nprocs_clima_atmos = Int[]
+    ncols_per_process = Int[]
+    walltime_clima_atmos = FT[]
+    found = false
+    for foldername in readdir(output_dir)
+        if occursin(job_id, foldername) && occursin(s, foldername)
+            nprocs_string = split(split(foldername, s)[end], "process")[1]
+            file = joinpath(
+                output_dir,
+                foldername,
+                "output_active",
+                "scaling_data_$(nprocs_string)_processes.jld2",
+            )
+            if !isfile(file)
+                @show readdir(output_dir)
+                @show readdir(dirname(file))
+            end
+            dict = load(file)
+            push!(nprocs_clima_atmos, Int(dict["nprocs"]))
+            push!(ncols_per_process, Int(dict["ncols_per_process"]))
+            push!(walltime_clima_atmos, FT(dict["walltime"]))
+        else
+            @show occursin(job_id, foldername)
+            @show occursin(s, foldername)
+        end
+    end
+    if !found
+        @show readdir(output_dir)
+    end
+    @show nprocs_clima_atmos
+    @show ncols_per_process
+    @show walltime_clima_atmos
+    return (; nprocs_clima_atmos, ncols_per_process, walltime_clima_atmos)
+end

post_processing/plot_gpu_strong_scaling.jl

@@ -1,6 +1,8 @@
 using CairoMakie
 using JLD2
 
+include("plot_gpu_scaling_utils.jl")
+
 job_id = "gpu_aquaplanet_dyamond_ss"
 output_dir = "./"
 
@@ -16,25 +18,8 @@ nlevels = z_elem + 1
 t_int = string(t_int_days) * " days"
 
 # read ClimaAtmos scaling data
-I, FT = Int, Float64
-nprocs_clima_atmos = I[]
-ncols_per_process = I[]
-walltime_clima_atmos = FT[]
-for foldername in readdir(output_dir)
-    if occursin(job_id, foldername) && occursin("_ss_", foldername)
-        nprocs_string = split(split(foldername, "_ss_")[end], "process")[1]
-        dict = load(
-            joinpath(
-                output_dir,
-                foldername,
-                "scaling_data_$(nprocs_string)_processes.jld2",
-            ),
-        )
-        push!(nprocs_clima_atmos, I(dict["nprocs"]))
-        push!(ncols_per_process, I(dict["ncols_per_process"]))
-        push!(walltime_clima_atmos, FT(dict["walltime"]))
-    end
-end
+(; nprocs_clima_atmos, ncols_per_process, walltime_clima_atmos) =
+    get_jld2data(output_dir, job_id, "_ss_")
 
 order = sortperm(nprocs_clima_atmos)
 nprocs_clima_atmos, ncols_per_process, walltime_clima_atmos =

post_processing/plot_gpu_weak_scaling.jl

@@ -1,6 +1,8 @@
 using CairoMakie
 using JLD2
 
+include("plot_gpu_scaling_utils.jl")
+
 job_id = "gpu_aquaplanet_dyamond_ws"
 output_dir = "./"
 
@@ -16,30 +18,15 @@ nlevels = z_elem + 1
 t_int = string(t_int_days) * " days"
 
 # read ClimaAtmos scaling data
-I, FT = Int, Float64
-nprocs_clima_atmos = I[]
-ncols_per_process = I[]
-walltime_clima_atmos = FT[]
-for foldername in readdir(output_dir)
-    if occursin(job_id, foldername) && occursin("_ws_", foldername)
-        nprocs_string = split(split(foldername, "_ws_")[end], "process")[1]
-        dict = load(
-            joinpath(
-                output_dir,
-                foldername,
-                "scaling_data_$(nprocs_string)_processes.jld2",
-            ),
-        )
-        push!(nprocs_clima_atmos, I(dict["nprocs"]))
-        push!(ncols_per_process, I(dict["ncols_per_process"]))
-        push!(walltime_clima_atmos, FT(dict["walltime"]))
-    end
-end
+(; nprocs_clima_atmos, ncols_per_process, walltime_clima_atmos) =
+    get_jld2data(output_dir, job_id, "_ws_")
+
 order = sortperm(nprocs_clima_atmos)
 nprocs_clima_atmos, ncols_per_process, walltime_clima_atmos =
     nprocs_clima_atmos[order],
     ncols_per_process[order],
     walltime_clima_atmos[order]
+
 # simulated years per day
 sypd_clima_atmos =
     (secs_per_day ./ walltime_clima_atmos) * t_int_days ./ days_per_year