analysis.jl

using BenchmarkTools
using DataFrames
using VegaLite

results = only(BenchmarkTools.load(joinpath(@__DIR__, "results.json")))

function parse_problem_label(label)
    m = match(
        r"""
        ^([^-]+)
        (?:- (?:clustersize=([^-]+)))?
        - (?:ncounters=([^-]+))
        - (?:nindices=([^-]+))
        """x,
        label,
    )
    @assert m !== nothing
    problem = Symbol(m[1])
    if m[2] === nothing
        clustersize = missing
    else
        clustersize = parse(Int, m[2])
    end
    ncounters = parse(Int, m[3])
    nindices = parse(Int, m[4])
    return (; problem, clustersize, ncounters, nindices)
end
@assert parse_problem_label("uniform-ncounters=1-nindices=2") ===
        (problem = :uniform, clustersize = missing, ncounters = 1, nindices = 2)
@assert parse_problem_label("cluster-clustersize=0-ncounters=1-nindices=2") ===
        (problem = :cluster, clustersize = 0, ncounters = 1, nindices = 2)

function parsenamed(T, prefix, str)
    @assert startswith(str, prefix)
    return parse(T, str[length(prefix)+1:end])
end

begin
    rawtable = map(leaves(results)) do (labels, trial)
        plabel = parse_problem_label(labels[1])
        ntries = nbatches = missing
        if labels[2] == "seq"
            impl = :seq
            ntasks = 1
        else
            ntasks = parsenamed(Int, "ntasks=", labels[2])
            impl = Symbol(labels[3])
            if impl === :tsx_static
                ntries = parsenamed(Int, "ntries=", labels[4])
                nbatches = parsenamed(Int, "nbatches=", labels[5])
            end
        end
        return (; plabel..., ntasks, impl, ntries, nbatches, trial)
    end

    rawdf = DataFrame(rawtable)
end

begin
    df1 = select(rawdf, Not(:trial))
    df1[!, :time] = map(trial -> minimum(trial).time ./ 1000_000, rawdf.trial)
    df1
end

function saveplot(; plt_...)
    name, plt = only(plt_)
    save(joinpath(@__DIR__, "$name.png"), plt)
    save(joinpath(@__DIR__, "$name.vegalite"), plt)
end

config = let
    fontconfig = @vlfrag(labelFontSize = 20, titleFontSize = 20)
    @vlfrag(
        axis = fontconfig,
        legend = fontconfig,
        header = fontconfig,
        title = fontconfig,
        # mark = ...
    )
end

plt_raw = @vlplot(
    mark = {type = :line, point = true},
    x = {
        field = :ncounters,
        title = "length(counters)",
        axis = {format = "e"},
        scale = {type = :log},
        type = :quantitative,
    },
    y = {field = :time, title = "time [ms]", type = :quantitative},
    color = {field = :impl, title = ""},
    column = :problem,
    row = {field = :ntasks, title = "Number of parallel tasks"},
    data = df1,
    width = 200,
    height = 100,
    config = config,
)
saveplot(; plt_raw)

function add_density!(df)
    df[!, :density_value] = df.nindices ./ df.ncounters
    df[!, :Density] = map(df.nindices ./ df.ncounters) do d
        if d >= 1
            string(round(Int, d))
        else
            "1/$(round(Int, 1/d))"
        end
    end
    return df
end

let idx_seq = df1.impl .== :seq
    cols = [:problem, :ncounters, :nindices]
    global df_seq = df1[idx_seq, [cols..., :time]]
    rename!(df_seq, :time => :time_seq)
    global df_speedup =
        leftjoin(df1[.!idx_seq, :], df_seq, on = cols, validate = (false, true))
    df_speedup[!, :speedup] = df_speedup.time_seq ./ df_speedup.time
    add_density!(df_speedup)
    df_speedup
end

plt_speedup = @vlplot(
    facet = {
        column = {field = :problem, title = "Problems"},
        row = {field = :impl, title = "Implementations"},
    },
    spec = {
        layer = [
            {mark = {type = :rule}, encoding = {y = {datum = 1}}},
            {
                mark = {type = :line, point = true},
                encoding = {
                    x = {field = :ntasks, type = :quantitative, title = "#Tasks"},
                    y = {field = :speedup, type = :quantitative},
                    color = {
                        field = :Density,
                        type = :ordinal,
                        sort = {field = :density_value},
                        scale = {scheme = :inferno}
                    },
                },
            },
        ],
        width = 120,
        height = 120,
    },
    data = df_speedup,
    config = config,
)
saveplot(; plt_speedup)

let idx_uniform = df1.problem .== :uniform
    cols = [:impl, :ncounters, :nindices, :ntasks]
    global df_uniform = df1[idx_uniform, [cols..., :time]]
    rename!(df_uniform, :time => :time_uniform)
    global df_vs_uniform =
        leftjoin(df1[.!idx_uniform, :], df_uniform, on = cols, validate = (false, true))
    df_vs_uniform[!, :relative_time] = df_vs_uniform.time ./ df_vs_uniform.time_uniform
    add_density!(df_vs_uniform)
    df_vs_uniform
end

plt_vs_uniform_on_ntasks = @vlplot(
    facet = {
        column = {field = :problem, title = "Problems"},
        row = {field = :impl, title = "Implementations"},
    },
    spec = {
        layer = [
            {mark = {type = :rule}, encoding = {y = {datum = 1}}},
            {
                mark = {type = :line, point = true},
                encoding = {
                    x = {field = :ntasks, type = :quantitative, title = "#Tasks"},
                    y = {field = :relative_time, type = :quantitative},
                    color = {
                        field = :Density,
                        type = :ordinal,
                        sort = {field = :density_value},
                    },
                },
            },
        ],
        width = 120,
        height = 120,
    },
    data = df_vs_uniform,
    config = config,
)

plt_vs_uniform = @vlplot(
    facet = {
        column = {field = :problem, title = "Problems"},
        row = {field = :impl, title = "Implementations"},
    },
    spec = {
        layer = [
            {mark = {type = :rule}, encoding = {y = {datum = 1}}},
            {
                mark = {type = :line, point = true},
                encoding = {
                    x = {
                        field = :Density,
                        type = :ordinal,
                        sort = {field = :density_value},
                    },
                    y = {field = :relative_time, type = :quantitative},
                    color = {
                        field = :ntasks,
                        type = :ordinal,
                        title = "#Tasks",
                        scale = {scheme = :viridis},
                    },
                },
            },
        ],
        width = 120,
        height = 120,
    },
    data = df_vs_uniform,
    config = config,
)
saveplot(; plt_vs_uniform)

lscpu.txt

Architecture:        x86_64
CPU op-mode(s):      32-bit, 64-bit
Byte Order:          Little Endian
CPU(s):              40
On-line CPU(s) list: 0-39
Thread(s) per core:  2
Core(s) per socket:  10
Socket(s):           2
NUMA node(s):        2
Vendor ID:           GenuineIntel
CPU family:          6
Model:               85
Model name:          Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
Stepping:            4
CPU MHz:             800.250
CPU max MHz:         3000.0000
CPU min MHz:         800.0000
BogoMIPS:            4400.00
Virtualization:      VT-x
L1d cache:           32K
L1i cache:           32K
L2 cache:            1024K
L3 cache:            14080K
NUMA node0 CPU(s):   0-9,20-29
NUMA node1 CPU(s):   10-19,30-39
Flags:               fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single pti intel_ppin ssbd mba ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req pku ospke md_clear flush_l1d

Manifest.toml

# This file is machine-generated - editing it directly is not advised

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Project.toml

[deps]
BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
TSXPlayground = "4ad7afaa-f16e-4c25-b718-b8b8e54957f2"
TSXPlaygroundBenchmarks = "18b1ebe1-c804-467a-8959-8d2a21eb1e26"
TerminalLoggers = "5d786b92-1e48-4d6f-9151-6b4477ca9bed"
VegaLite = "112f6efa-9a02-5b7d-90c0-432ed331239a"

results.json

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versioninfo.txt

Julia Version 1.7.0-DEV.1078
Commit 43f0383b5b (2021-05-10 01:25 UTC)
Platform Info:
  OS: Linux (x86_64-linux-gnu)
  CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-11.0.1 (ORCJIT, skylake-avx512)
Environment:
  JULIA_EDITOR = vim