Initial commit
This commit is contained in:
commit
64ea4e4bb8
SSH Key Fingerprint:
SHA256:eobz41Mnm0/iYWBvWThftS0ElEs1ftBr6jamutnXc/A
|
|
@ -0,0 +1,20 @@
|
|||
data_config_version = 0
|
||||
uuid = "499e3769-d786-499d-8a4f-335fb3f63256"
|
||||
name = "julia-nanosoldier-aggregation"
|
||||
plugins = ["store", "defaults", "memorise"]
|
||||
|
||||
[config.defaults.storage._]
|
||||
checksum = "auto"
|
||||
|
||||
[[nanosoldier-collation]]
|
||||
uuid = "dde97f00-7cf5-4c42-9c42-6b4be929b85f"
|
||||
description = "An aggregation of nanosoldier data performed by Cameron Pfiffer in April 2024"
|
||||
|
||||
[[nanosoldier-collation.storage]]
|
||||
driver = "web"
|
||||
checksum = "k12:113e6d8e63ff55a0e487662a8951ebc8"
|
||||
url = "https://0x0.st/XHbB.zst" # This should be hosted till the end of 2024
|
||||
|
||||
[[nanosoldier-collation.loader]]
|
||||
driver = "chain"
|
||||
loaders = ["zstd", "arrow"]
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,10 @@
|
|||
[deps]
|
||||
Arrow = "69666777-d1a9-59fb-9406-91d4454c9d45"
|
||||
CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
|
||||
CodecZstd = "6b39b394-51ab-5f42-8807-6242bab2b4c2"
|
||||
Colors = "5ae59095-9a9b-59fe-a467-6f913c188581"
|
||||
DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
|
||||
DataToolkit = "dc83c90b-d41d-4e55-bdb7-0fc919659999"
|
||||
GLMakie = "e9467ef8-e4e7-5192-8a1a-b1aee30e663a"
|
||||
KangarooTwelve = "2a5dabf5-6a39-42aa-818d-ce8a58d1b312"
|
||||
Loess = "4345ca2d-374a-55d4-8d30-97f9976e7612"
|
||||
|
|
@ -0,0 +1,12 @@
|
|||
#+title: Julia Nanosoldier Aggregation
|
||||
#+author: tecosaur
|
||||
|
||||
I think there's value in seeing shifts in [[https://github.com/JuliaCI/Nanosoldier.jl][nanosoldier]] [[https://github.com/JuliaCI/NanosoldierReports][results]] over time, and an
|
||||
interactive =GLMakie= plot seems like a good way to do this to me 🙂.
|
||||
|
||||
* Usage
|
||||
|
||||
1. Instantiate the project
|
||||
2. Load =plot.jl=
|
||||
3. Run ~nanofig()~
|
||||
4. Profit 💰
|
||||
|
|
@ -0,0 +1,330 @@
|
|||
using DataToolkit, DataFrames, Dates
|
||||
using GLMakie, Colors, Loess
|
||||
|
||||
const ns_data = d"nanosoldier-collation"
|
||||
|
||||
ns_data.category = map(Symbol, ns_data.category)
|
||||
ns_data.subcategory_1 = map(passmissing(Symbol), ns_data.subcategory_1)
|
||||
ns_data.subcategory_2 = let symbpool = Dict{String, Symbol}()
|
||||
map(passmissing(c ->
|
||||
get!(() -> Symbol(
|
||||
if startswith(c, '(')
|
||||
try join(Meta.parse(c).args, ", ") catch _ c end
|
||||
else c end),
|
||||
symbpool, c)),
|
||||
ns_data.subcategory_2)
|
||||
end
|
||||
|
||||
time_fmt(ns::Number) = if ns < 0 ""
|
||||
elseif ns < 1000
|
||||
string(round(Int, ns), "ns")
|
||||
elseif ns < 1000^2
|
||||
string(round(Int, ns ÷ 1000), "μs")
|
||||
elseif ns < 1000^3
|
||||
string(round(Int, ns ÷ 1000^2), "ms")
|
||||
else
|
||||
string(round(Int, ns ÷ 1000^3), "s")
|
||||
end
|
||||
|
||||
time_fmt(nss::Vector{<:Number}) = map(time_fmt, nss)
|
||||
|
||||
pct_fmt(ps::Vector{<:Number}) =
|
||||
Makie.get_ticklabels(Makie.automatic, 100 .* ps) .* "%"
|
||||
|
||||
mem_fmt(m::Number) = if m < 0 ""
|
||||
elseif m < 1000
|
||||
string(round(Int, m), "B")
|
||||
elseif m < 1000^2
|
||||
string(round(Int, m ÷ 1000), "kB")
|
||||
elseif m < 1000^3
|
||||
string(round(Int, m ÷ 1000^2), "MB")
|
||||
else
|
||||
string(round(Int, m ÷ 1000^3), "GB")
|
||||
end
|
||||
|
||||
mem_fmt(nss::Vector) = map(mem_fmt, nss)
|
||||
|
||||
date_fmt(t::Number) = string(Date(unix2datetime(t)))
|
||||
date_fmt(ts::Vector{<:Number}) = map(date_fmt, ts)
|
||||
|
||||
function lchshift(c::C, Δl::Number=0, Δc::Number=0, Δh::Number=0) where {C <: Colorant}
|
||||
lc = convert(LCHab, c)
|
||||
convert(C, LCHab(clamp(lc.l + Δl, Float32(0), Float32(100)),
|
||||
clamp(lc.c + Δc, Float32(0), Float32(150)),
|
||||
(lc.h + Δh) % 360))
|
||||
end
|
||||
|
||||
const colours = let base = (:time => colorant"#1c71d8",
|
||||
:gctime => colorant"#f66151",
|
||||
:mem => colorant"#26a269",
|
||||
:alloc => colorant"#e5a50a")
|
||||
NamedTuple(map(base) do (name, bc)
|
||||
name => (; base = bc,
|
||||
dark = lchshift(bc, -15, -5),
|
||||
light = lchshift(bc, 20, -10),
|
||||
faint = lchshift(bc, 30, -40))
|
||||
end)
|
||||
end
|
||||
|
||||
update_theme!(fonts = Attributes(
|
||||
bold = "Alegreya Sans Bold",
|
||||
bold_italic = "Alegreya Sans Bold Italic",
|
||||
italic = "Alegreya Sans Italic",
|
||||
regular = "Alegreya Sans Medium"))
|
||||
|
||||
function nanofig(default_category::String = String(first(ns_data.category)),
|
||||
default_subcategory::String = "All",
|
||||
default_benchmark::String = "All";
|
||||
scatter::Bool = true, lines::Bool = true, smoothed::Bool=true)
|
||||
fig = Figure(size = (1400, 900))
|
||||
|
||||
cat_menu = Menu(
|
||||
fig, options = sort(unique(ns_data.category)),
|
||||
default = default_category)
|
||||
|
||||
subcategories = map(cat_menu.selection) do cat
|
||||
vals = [:All]
|
||||
append!(vals, unique(ns_data.subcategory_1[ns_data.category .=== cat]))
|
||||
sort(vals)
|
||||
end
|
||||
|
||||
subcat_menu = Menu(fig, options = subcategories, default = default_subcategory, prompt="All")
|
||||
on(subcategories) do _; subcat_menu.selection[] = :All end
|
||||
|
||||
benchmarks = map(subcat_menu.selection) do subcat
|
||||
vals = [:All]
|
||||
mask = ns_data.category .=== cat_menu.selection[]
|
||||
if subcat !== :All
|
||||
smask = ns_data.subcategory_1 .=== subcat
|
||||
mask = mask .& smask
|
||||
end
|
||||
append!(vals, skipmissing(unique(ns_data.subcategory_2[mask])))
|
||||
vals
|
||||
end
|
||||
|
||||
bench_menu = Menu(fig, options = benchmarks, default = default_benchmark, prompt="All")
|
||||
on(benchmarks) do _; bench_menu.selection[] = :All end
|
||||
|
||||
fig[3,1] = Label(fig, "Benchmark:", fontsize=18, font=:bold, justification=:right)
|
||||
fig[3,2] = cat_menu
|
||||
fig[3,3] = Label(fig, "/", fontsize=24)
|
||||
fig[3,4] = subcat_menu
|
||||
fig[3,5] = Label(fig, "/", fontsize=24)
|
||||
fig[3,6] = bench_menu
|
||||
fig[3,7] = Label(fig, "Scatter:", font=:bold, justification=:right)
|
||||
fig[3,8] = togl_scatter = Toggle(fig, active=scatter)
|
||||
fig[3,9] = Label(fig, "Lines:", font=:bold, justification=:right)
|
||||
fig[3,10] = togl_lines = Toggle(fig, active=lines)
|
||||
fig[3,11] = Label(fig, "Smoothed:", font=:bold, justification=:right)
|
||||
fig[3,12] = togl_smooth = Toggle(fig, active=smoothed)
|
||||
|
||||
records_mask = map(cat_menu.selection, subcat_menu.selection, bench_menu.selection) do cat, subcat, bench
|
||||
mask = ns_data.category .=== cat
|
||||
if subcat !== :All
|
||||
mask = mask .& (ns_data.subcategory_1 .=== subcat)
|
||||
end
|
||||
if bench !== :All
|
||||
mask = mask .& (ns_data.subcategory_2 .=== bench)
|
||||
end
|
||||
mask
|
||||
end
|
||||
|
||||
bench_tuples = collect(zip(ns_data.category, ns_data.subcategory_1, ns_data.subcategory_2))
|
||||
|
||||
records_selection = map(records_mask) do mask
|
||||
btups = bench_tuples[mask]
|
||||
segments = indexin(btups, unique(btups))
|
||||
if issorted(segments)
|
||||
findall(mask)
|
||||
else
|
||||
findall(mask)[sortperm(segments)]
|
||||
end
|
||||
end
|
||||
|
||||
records_segment_lengths = map(records_selection) do selection
|
||||
seglens = Int[]
|
||||
isempty(selection) && return seglens
|
||||
last_i = 0
|
||||
current_btup = bench_tuples[first(selection)]
|
||||
for (i, index) in enumerate(selection)
|
||||
if bench_tuples[index] !== current_btup
|
||||
current_btup = bench_tuples[index]
|
||||
push!(seglens, i - last_i)
|
||||
last_i = i + 1
|
||||
end
|
||||
end
|
||||
push!(seglens, length(selection) - last_i)
|
||||
seglens
|
||||
end
|
||||
|
||||
x_coords = map(records_selection) do sel
|
||||
datetime2unix.(DateTime.(ns_data.date[sel]))
|
||||
end
|
||||
|
||||
sets = map(x_coords) do _
|
||||
subcats = ns_data.subcategory_1[records_selection[]]
|
||||
indexin(subcats, unique(subcats))
|
||||
end
|
||||
|
||||
# Axis setup
|
||||
|
||||
ax_time = Axis(
|
||||
fig[1,1:end],
|
||||
ytickformat = time_fmt,
|
||||
ylabel = Makie.rich("Minimum execution time",
|
||||
color=colours.time.dark, font=:bold),
|
||||
title = "Nanosoldier benchmark trends", titlesize = 28,
|
||||
yzoomlock = true, ypanlock = true, yrectzoom = false)
|
||||
ax_gctime = Axis(
|
||||
fig[1,1:end],
|
||||
yaxisposition = :right, ytickformat = pct_fmt,
|
||||
ylabel = Makie.rich("Median proportion of execution time spent on GC",
|
||||
color=colours.gctime.dark, font=:bold),
|
||||
limits = (nothing, (-0.05, 1.05)),
|
||||
yzoomlock = true, ypanlock = true, yrectzoom = false)
|
||||
ax_mem = Axis(
|
||||
fig[2,1:end],
|
||||
ytickformat = mem_fmt, xtickformat = date_fmt,
|
||||
ylabel = Makie.rich("Median memory usage",
|
||||
color=colours.mem.dark, font=:bold),
|
||||
yzoomlock = true, ypanlock = true, yrectzoom = false)
|
||||
ax_alloc = Axis(
|
||||
fig[2,1:end],
|
||||
yaxisposition = :right,
|
||||
ylabel = Makie.rich("Median allocations",
|
||||
color=colours.alloc.dark, font=:bold),
|
||||
yzoomlock = true, ypanlock = true, yrectzoom = false)
|
||||
|
||||
hidexdecorations!(ax_time)
|
||||
|
||||
hidespines!(ax_gctime)
|
||||
hidexdecorations!(ax_gctime)
|
||||
|
||||
hidespines!(ax_alloc)
|
||||
hidexdecorations!(ax_alloc)
|
||||
|
||||
linkxaxes!(ax_time, ax_gctime, ax_mem, ax_alloc)
|
||||
|
||||
# Coordinates
|
||||
|
||||
coords_time = map(x_coords) do xs
|
||||
hcat(xs, ns_data.time_minimum[records_selection[]])
|
||||
end
|
||||
coords_gc = map(x_coords) do xs
|
||||
hcat(xs, ns_data.gctime_median[records_selection[]] ./
|
||||
ns_data.time_median[records_selection[]])
|
||||
end
|
||||
coords_mem = map(x_coords) do xs
|
||||
hcat(xs, ns_data.memory_median[records_selection[]])
|
||||
end
|
||||
coords_alloc = map(x_coords) do xs
|
||||
hcat(xs, ns_data.allocs_median[records_selection[]])
|
||||
end
|
||||
|
||||
# Ensure the axis are right
|
||||
|
||||
on(coords_time) do pos
|
||||
xs, ys = eachcol(pos)
|
||||
reset_limits!(ax_time, yauto = false)
|
||||
notify(ax_time.finallimits)
|
||||
end
|
||||
|
||||
on(coords_gc) do _
|
||||
reset_limits!(ax_gctime, yauto = false)
|
||||
end
|
||||
|
||||
on(coords_mem) do pos
|
||||
xs, ys = eachcol(pos)
|
||||
reset_limits!(ax_mem, yauto = false)
|
||||
notify(ax_mem.finallimits)
|
||||
end
|
||||
|
||||
on(coords_alloc) do pos
|
||||
xs, ys = eachcol(pos)
|
||||
reset_limits!(ax_alloc, yauto = false)
|
||||
notify(ax_alloc.finallimits)
|
||||
end
|
||||
|
||||
# Rescale y axis on zoom
|
||||
|
||||
for (ax, coords, yfactor) in (
|
||||
(ax_time, coords_time, 1.05),
|
||||
(ax_mem, coords_mem, 1.05),
|
||||
(ax_alloc, coords_alloc, 1.2))
|
||||
on(ax.finallimits) do rect
|
||||
axmin, aymin = rect.origin
|
||||
axrange, ayrange = rect.widths
|
||||
axmax, aymax = axmin + axrange, aymin + ayrange
|
||||
xs, ys = eachcol(coords[])
|
||||
visable_ys = ys[axmin .<= xs .<= axmax]
|
||||
isempty(visable_ys) && return
|
||||
vymax = maximum(visable_ys, init=1)
|
||||
vymax_target = yfactor * vymax
|
||||
if abs(aymax - vymax_target) > 0.1 * vymax
|
||||
ylims!(ax, -0.05 * vymax, vymax_target)
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
# Scatter plots
|
||||
|
||||
ax_coord_colour_sets = (
|
||||
(ax_time, coords_time, colours.time),
|
||||
(ax_gctime, coords_gc, colours.gctime),
|
||||
(ax_mem, coords_mem, colours.mem),
|
||||
(ax_alloc, coords_alloc, colours.alloc))
|
||||
|
||||
for (ax, coord, colour) in ax_coord_colour_sets
|
||||
sctr_points = map(coord, togl_scatter.active) do coord, enabled
|
||||
ifelse(enabled, coord, zeros(Float64, 0, 2))
|
||||
end
|
||||
sctr_colour = map(togl_lines.active) do enabled
|
||||
ifelse(enabled, colour.light, colour.base)
|
||||
end
|
||||
scatter!(ax, sctr_points, color = sctr_colour)
|
||||
end
|
||||
|
||||
# Line plots
|
||||
|
||||
for (ax, coord, colour) in ax_coord_colour_sets
|
||||
line_points = map(coord, togl_lines.active, togl_smooth.active) do coords, enabled, smooth
|
||||
points = Tuple{Float64, Float64}[]
|
||||
enabled || return points
|
||||
xs, ys = eachcol(coords)
|
||||
offset = 0
|
||||
for len in records_segment_lengths[]
|
||||
segment = 1+offset:len+offset
|
||||
offset += len
|
||||
xs1, ys1 = xs[segment], ys[segment]
|
||||
xperm = sortperm(xs1)
|
||||
xs1, ys1 = xs1[xperm], ys1[xperm]
|
||||
newpoints = if smooth
|
||||
model = loess(xs1, ys1, span=0.1)
|
||||
us = range(extrema(xs1)..., length=length(xs1))
|
||||
vs = predict(model, us)
|
||||
tuple.(us, vs)
|
||||
else
|
||||
tuple.(xs1, ys1)
|
||||
end
|
||||
append!(points, newpoints)
|
||||
push!(points, (NaN, NaN))
|
||||
end
|
||||
points
|
||||
end
|
||||
|
||||
line_colours = map(coord, togl_lines.active) do _, enabled
|
||||
colours = Vector{typeof(colour.base)}()
|
||||
if enabled
|
||||
for len in records_segment_lengths[]
|
||||
append!(colours, fill(colour.base, len+1))
|
||||
end
|
||||
end
|
||||
colours
|
||||
end
|
||||
|
||||
lines!(ax, line_points, color = line_colours,
|
||||
linewidth=2, depth_shift=Float32(-0.01))
|
||||
end
|
||||
|
||||
fig
|
||||
end
|
||||
Loading…
Reference in New Issue