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Benchmark Report

Job Properties

Commit: JuliaLang/julia@e0c5e9604888a833a0360d54c079b728a9491c25

Comparison Range: link

Triggered By: link

Tag Predicate: ALL

Daily Job: 2022-03-24 vs 2022-03-22

Results

Note: If Chrome is your browser, I strongly recommend installing the Wide GitHub extension, which makes the result table easier to read.

Below is a table of this job's results, obtained by running the benchmarks found in JuliaCI/BaseBenchmarks.jl. The values listed in the ID column have the structure [parent_group, child_group, ..., key], and can be used to index into the BaseBenchmarks suite to retrieve the corresponding benchmarks.

The percentages accompanying time and memory values in the below table are noise tolerances. The "true" time/memory value for a given benchmark is expected to fall within this percentage of the reported value.

A ratio greater than 1.0 denotes a possible regression (marked with ❌), while a ratio less than 1.0 denotes a possible improvement (marked with ✅). Only significant results - results that indicate possible regressions or improvements - are shown below (thus, an empty table means that all benchmark results remained invariant between builds).

ID time ratio memory ratio
["array", "accumulate", ("cumsum!", "Float64", "dim2")] 1.09 (5%) ❌ 1.00 (1%)
["array", "cat", "4467"] 0.77 (5%) ✅ 1.00 (1%)
["array", "cat", ("hcat_setind", 5)] 1.06 (5%) ❌ 1.00 (1%)
["array", "cat", ("vcat_setind", 5)] 0.93 (5%) ✅ 1.00 (1%)
["array", "equality", ("==", "BitArray")] 0.92 (5%) ✅ 1.00 (1%)
["array", "equality", ("==", "UnitRange{Int64}")] 0.91 (5%) ✅ 1.00 (1%)
["array", "equality", ("isequal", "UnitRange{Int64}")] 1.22 (5%) ❌ 1.00 (1%)
["array", "equality", ("isequal", "Vector{Int64} isequal Vector{Int64}")] 1.07 (5%) ❌ 1.00 (1%)
["array", "reductions", ("sumabs2", "Float64")] 1.13 (5%) ❌ 1.00 (1%)
["array", "setindex!", ("setindex!", 3)] 0.93 (5%) ✅ 1.00 (1%)
["broadcast", "dotop", ("Float64", "(1000, 1000)", 2)] 1.14 (5%) ❌ 1.00 (1%)
["broadcast", "mix_scalar_tuple", (10, "scal_tup_x3")] 0.92 (5%) ✅ 1.00 (1%)
["broadcast", "mix_scalar_tuple", (10, "tup_tup")] 0.93 (5%) ✅ 1.00 (1%)
["broadcast", "mix_scalar_tuple", (3, "scal_tup")] 0.83 (5%) ✅ 1.00 (1%)
["broadcast", "mix_scalar_tuple", (3, "scal_tup_x3")] 0.95 (5%) ✅ 1.00 (1%)
["broadcast", "mix_scalar_tuple", (5, "scal_tup")] 1.10 (5%) ❌ 1.00 (1%)
["broadcast", "mix_scalar_tuple", (5, "scal_tup_x3")] 1.09 (5%) ❌ 1.00 (1%)
["broadcast", "mix_scalar_tuple", (5, "tup_tup")] 0.91 (5%) ✅ 1.00 (1%)
["collection", "iteration", ("Vector", "String", "iterate")] 1.33 (25%) ❌ 1.00 (1%)
["collection", "optimizations", ("Vector", "concrete", "Nothing")] 6.89 (25%) ❌ 1.00 (1%)
["collection", "queries & updates", ("BitSet", "Int", "push!", "new")] 1.29 (25%) ❌ 1.00 (1%)
["collection", "queries & updates", ("BitSet", "Int", "push!", "overwrite")] 1.28 (25%) ❌ 1.00 (1%)
["find", "findall", ("> q0.5", "Vector{Int8}")] 1.08 (5%) ❌ 1.00 (1%)
["find", "findall", ("> q0.8", "Vector{Float32}")] 1.14 (5%) ❌ 1.00 (1%)
["find", "findall", ("> q0.8", "Vector{Float64}")] 1.11 (5%) ❌ 1.00 (1%)
["find", "findall", ("> q0.8", "Vector{Int8}")] 1.13 (5%) ❌ 1.00 (1%)
["find", "findall", ("> q0.8", "Vector{UInt64}")] 1.09 (5%) ❌ 1.00 (1%)
["find", "findall", ("> q0.95", "Vector{Float32}")] 1.09 (5%) ❌ 1.00 (1%)
["find", "findall", ("> q0.95", "Vector{Int8}")] 1.08 (5%) ❌ 1.00 (1%)
["find", "findnext", ("ispos", "Vector{UInt8}")] 1.06 (5%) ❌ 1.00 (1%)
["find", "findprev", ("BitVector", "10-90")] 1.10 (5%) ❌ 1.00 (1%)
["find", "findprev", ("ispos", "Vector{Int64}")] 1.05 (5%) ❌ 1.00 (1%)
["find", "findprev", ("ispos", "Vector{UInt64}")] 1.14 (5%) ❌ 1.00 (1%)
["inference", "abstract interpretation", "println(::QuoteNode)"] 1.00 (5%) 1.05 (1%) ❌
["inference", "optimization", "println(::QuoteNode)"] 0.90 (5%) ✅ 1.00 (1%)
["inference", "optimization", "sin(42)"] 0.91 (5%) ✅ 1.00 (1%)
["inference", "println(::QuoteNode)"] 1.01 (5%) 1.04 (1%) ❌
["io", "read", "readstring"] 1.07 (5%) ❌ 1.00 (1%)
["io", "serialization", ("deserialize", "Matrix{Float64}")] 0.95 (5%) ✅ 1.00 (1%)
["micro", "parseint"] 0.92 (5%) ✅ 1.00 (1%)
["misc", "bitshift", ("Int", "Int")] 1.09 (5%) ❌ 1.00 (1%)
["misc", "bitshift", ("Int", "UInt")] 1.09 (5%) ❌ 1.00 (1%)
["misc", "bitshift", ("UInt", "UInt")] 0.92 (5%) ✅ 1.00 (1%)
["misc", "foldl", "foldl(+, filter(...))"] 0.64 (5%) ✅ 1.00 (1%)
["misc", "iterators", "zip(1:1000)"] 0.93 (5%) ✅ 1.00 (1%)
["misc", "iterators", "zip(1:1000, 1:1000, 1:1000)"] 1.05 (5%) ❌ 1.00 (1%)
["problem", "monte carlo", "euro_option_vec"] 1.06 (5%) ❌ 1.00 (1%)
["problem", "simplex", "simplex"] 1.10 (5%) ❌ 1.00 (1%)
["random", "collections", ("rand!", "ImplicitRNG", "large Vector")] 1.65 (25%) ❌ 1.00 (1%)
["random", "collections", ("rand!", "ImplicitRNG", "small Vector")] 1.60 (25%) ❌ 1.00 (1%)
["random", "ranges", ("rand!", "ImplicitRNG", "Int", "1:1000")] 1.40 (25%) ❌ 1.00 (1%)
["scalar", "acos", ("small", "negative argument", "Float32")] 0.95 (5%) ✅ 1.00 (1%)
["scalar", "acos", ("small", "positive argument", "Float32")] 0.95 (5%) ✅ 1.00 (1%)
["scalar", "acos", ("zero", "Float32")] 0.95 (5%) ✅ 1.00 (1%)
["scalar", "asin", ("0.5 <= abs(x) < 0.975", "positive argument", "Float64")] 0.80 (5%) ✅ 1.00 (1%)
["scalar", "asin", ("0.975 <= abs(x) < 1.0", "negative argument", "Float64")] 0.70 (5%) ✅ 1.00 (1%)
["scalar", "asin", ("0.975 <= abs(x) < 1.0", "positive argument", "Float64")] 0.69 (5%) ✅ 1.00 (1%)
["scalar", "asinh", ("2 <= abs(x) < 2^28", "negative argument", "Float32")] 1.40 (5%) ❌ 1.00 (1%)
["scalar", "asinh", ("2 <= abs(x) < 2^28", "positive argument", "Float32")] 1.38 (5%) ❌ 1.00 (1%)
["scalar", "asinh", ("2^-28 <= abs(x) < 2", "negative argument", "Float32")] 1.39 (5%) ❌ 1.00 (1%)
["scalar", "asinh", ("2^-28 <= abs(x) < 2", "positive argument", "Float32")] 1.40 (5%) ❌ 1.00 (1%)
["scalar", "asinh", ("very small", "negative argument", "Float32")] 0.95 (5%) ✅ 1.00 (1%)
["scalar", "asinh", ("very small", "positive argument", "Float32")] 0.95 (5%) ✅ 1.00 (1%)
["scalar", "asinh", ("zero", "Float32")] 0.95 (5%) ✅ 1.00 (1%)
["scalar", "atan", ("very large", "negative argument", "Float32")] 0.95 (5%) ✅ 1.00 (1%)
["scalar", "atan", ("very large", "negative argument", "Float64")] 0.94 (5%) ✅ 1.00 (1%)
["scalar", "atan", ("very large", "positive argument", "Float32")] 0.95 (5%) ✅ 1.00 (1%)
["scalar", "atan", ("very large", "positive argument", "Float64")] 0.95 (5%) ✅ 1.00 (1%)
["scalar", "atan", ("very small", "negative argument", "Float32")] 0.93 (5%) ✅ 1.00 (1%)
["scalar", "atan", ("very small", "positive argument", "Float32")] 0.93 (5%) ✅ 1.00 (1%)
["scalar", "atan", ("zero", "Float32")] 0.93 (5%) ✅ 1.00 (1%)
["scalar", "atan2", ("abs(y/x) high", "y infinite", "y negative", "x finite", "x negative", "Float64")] 0.92 (5%) ✅ 1.00 (1%)
["scalar", "atan2", ("abs(y/x) high", "y negative", "x negative", "Float64")] 0.92 (5%) ✅ 1.00 (1%)
["scalar", "atan2", ("abs(y/x) high", "y positive", "x positive", "Float32")] 0.94 (5%) ✅ 1.00 (1%)
["scalar", "atan2", ("abs(y/x) safe (large)", "y positive", "x negative", "Float32")] 0.92 (5%) ✅ 1.00 (1%)
["scalar", "atan2", ("abs(y/x) small", "y positive", "x positive", "Float32")] 0.94 (5%) ✅ 1.00 (1%)
["scalar", "atan2", ("x one", "Float32")] 0.94 (5%) ✅ 1.00 (1%)
["scalar", "atan2", ("x one", "Float64")] 0.90 (5%) ✅ 1.00 (1%)
["scalar", "atan2", ("x zero", "y negative", "Float64")] 0.88 (5%) ✅ 1.00 (1%)
["scalar", "atan2", ("x zero", "y positive", "Float64")] 0.88 (5%) ✅ 1.00 (1%)
["scalar", "atan2", ("y finite", "y negative", "x infinite", "x negative", "Float64")] 0.92 (5%) ✅ 1.00 (1%)
["scalar", "atan2", ("y finite", "y negative", "x infinite", "x positive", "Float64")] 0.92 (5%) ✅ 1.00 (1%)
["scalar", "atan2", ("y finite", "y positive", "x infinite", "x negative", "Float64")] 0.92 (5%) ✅ 1.00 (1%)
["scalar", "atan2", ("y finite", "y positive", "x infinite", "x positive", "Float64")] 0.92 (5%) ✅ 1.00 (1%)
["scalar", "atan2", ("y infinite", "y negative", "x finite", "x negative", "Float64")] 0.92 (5%) ✅ 1.00 (1%)
["scalar", "atan2", ("y infinite", "y negative", "x finite", "x positive", "Float64")] 0.92 (5%) ✅ 1.00 (1%)
["scalar", "atan2", ("y infinite", "y positive", "x finite", "x negative", "Float64")] 0.92 (5%) ✅ 1.00 (1%)
["scalar", "atan2", ("y infinite", "y positive", "x finite", "x positive", "Float64")] 0.92 (5%) ✅ 1.00 (1%)
["scalar", "atan2", ("y zero", "y negative", "x positive", "Float64")] 0.91 (5%) ✅ 1.00 (1%)
["scalar", "atan2", ("y zero", "y positive", "x negative", "Float64")] 0.91 (5%) ✅ 1.00 (1%)
["scalar", "atan2", ("y zero", "y positive", "x positive", "Float64")] 0.91 (5%) ✅ 1.00 (1%)
["scalar", "atanh", ("very small", "negative argument", "Float64")] 0.93 (5%) ✅ 1.00 (1%)
["scalar", "atanh", ("very small", "positive argument", "Float64")] 0.93 (5%) ✅ 1.00 (1%)
["scalar", "cbrt", ("zero", "Float32")] 1.07 (5%) ❌ 1.00 (1%)
["scalar", "cosh", ("2.7755602085408512e-17 <= abs(x) < 22.0", "negative argument", "Float64")] 1.05 (5%) ❌ 1.00 (1%)
["scalar", "cosh", ("2.7755602085408512e-17 <= abs(x) < 22.0", "positive argument", "Float64")] 1.05 (5%) ❌ 1.00 (1%)
["scalar", "cosh", ("22.0 <= abs(x) < 709.7822265633563", "negative argument", "Float64")] 1.05 (5%) ❌ 1.00 (1%)
["scalar", "expm1", ("huge", "positive argument", "Float64")] 0.95 (5%) ✅ 1.00 (1%)
["scalar", "expm1", ("large", "negative argument", "Float64")] 0.94 (5%) ✅ 1.00 (1%)
["scalar", "expm1", ("large", "positive argument", "Float64")] 1.06 (5%) ❌ 1.00 (1%)
["scalar", "expm1", ("medium", "negative argument", "Float64")] 0.94 (5%) ✅ 1.00 (1%)
["scalar", "rem_pio2", ("argument reduction (easy) abs(x) < 2π/4", "negative argument", "Float64")] 0.94 (5%) ✅ 1.00 (1%)
["scalar", "rem_pio2", ("argument reduction (easy) abs(x) < 2π/4", "positive argument", "Float64")] 0.94 (5%) ✅ 1.00 (1%)
["scalar", "rem_pio2", ("argument reduction (easy) abs(x) < 5π/4", "positive argument", "Float64")] 0.95 (5%) ✅ 1.00 (1%)
["scalar", "rem_pio2", ("argument reduction (easy) abs(x) < 7π/4", "positive argument", "Float64")] 0.95 (5%) ✅ 1.00 (1%)
["scalar", "tan", ("medium", "negative argument", "Float64")] 1.09 (5%) ❌ 1.00 (1%)
["simd", ("Linear", "auto_sum_reduce", "Int64", 4095)] 1.25 (20%) ❌ 1.00 (1%)
["sparse", "constructors", ("IJV", 10)] 0.95 (5%) ✅ 1.00 (1%)
["sparse", "constructors", ("IV", 10)] 0.90 (5%) ✅ 1.00 (1%)
["sparse", "constructors", ("Tridiagonal", 100)] 1.06 (5%) ❌ 1.00 (1%)
["string", "==(::AbstractString, ::AbstractString)", "different"] 1.18 (5%) ❌ 1.00 (1%)
["string", "==(::SubString, ::String)", "different length"] 0.91 (5%) ✅ 1.00 (1%)
["string", "==(::SubString, ::String)", "different"] 0.94 (5%) ✅ 1.00 (1%)
["string", "readuntil", "target length 1"] 0.90 (5%) ✅ 1.00 (1%)
["tuple", "linear algebra", ("matmat", "(2, 2)", "(2, 2)")] 0.88 (5%) ✅ 1.00 (1%)
["tuple", "linear algebra", ("matmat", "(4, 4)", "(4, 4)")] 0.94 (5%) ✅ 1.00 (1%)
["tuple", "linear algebra", ("matvec", "(4, 4)", "(4,)")] 1.09 (5%) ❌ 1.00 (1%)
["tuple", "misc", "longtuple"] 0.95 (5%) ✅ 1.00 (1%)
["tuple", "reduction", ("sum", "(2, 2)")] 0.93 (5%) ✅ 1.00 (1%)
["tuple", "reduction", ("sum", "(2,)")] 0.93 (5%) ✅ 1.00 (1%)
["tuple", "reduction", ("sum", "(4, 4)")] 1.10 (5%) ❌ 1.00 (1%)
["tuple", "reduction", ("sumabs", "(8,)")] 0.94 (5%) ✅ 1.00 (1%)
["union", "array", ("broadcast", "*", "Bool", "(false, false)")] 0.92 (5%) ✅ 1.00 (1%)
["union", "array", ("broadcast", "abs", "BigFloat", 0)] 1.05 (5%) ❌ 1.00 (1%)
["union", "array", ("broadcast", "abs", "Int64", 1)] 0.92 (5%) ✅ 1.00 (1%)
["union", "array", ("collect", "filter", "Bool", 0)] 0.95 (5%) ✅ 1.00 (1%)
["union", "array", ("map", "abs", "Bool", 1)] 1.18 (5%) ❌ 1.00 (1%)
["union", "array", ("perf_sum", "Float32", 1)] 1.17 (5%) ❌ 1.00 (1%)
["union", "array", ("perf_sum3", "Bool", 0)] 0.94 (5%) ✅ 1.00 (1%)
["union", "array", ("perf_sum3", "Float64", 1)] 0.93 (5%) ✅ 1.00 (1%)
["union", "array", ("skipmissing", "eachindex", "Union{Missing, Int8}", 1)] 1.07 (5%) ❌ 1.00 (1%)
["union", "array", ("skipmissing", "perf_sumskipmissing", "Int64", 0)] 1.14 (5%) ❌ 1.00 (1%)
["union", "array", ("skipmissing", "perf_sumskipmissing", "Union{Missing, Int64}", 1)] 0.91 (5%) ✅ 1.00 (1%)
["union", "array", ("skipmissing", "sum", "Union{Missing, ComplexF64}", 1)] 0.79 (5%) ✅ 1.00 (1%)

Benchmark Group List

Here's a list of all the benchmark groups executed by this job:

  • ["alloc"]
  • ["array", "accumulate"]
  • ["array", "any/all"]
  • ["array", "bool"]
  • ["array", "cat"]
  • ["array", "comprehension"]
  • ["array", "convert"]
  • ["array", "equality"]
  • ["array", "growth"]
  • ["array", "index"]
  • ["array", "reductions"]
  • ["array", "reverse"]
  • ["array", "setindex!"]
  • ["array", "subarray"]
  • ["broadcast"]
  • ["broadcast", "dotop"]
  • ["broadcast", "fusion"]
  • ["broadcast", "mix_scalar_tuple"]
  • ["broadcast", "sparse"]
  • ["broadcast", "typeargs"]
  • ["collection", "deletion"]
  • ["collection", "initialization"]
  • ["collection", "iteration"]
  • ["collection", "optimizations"]
  • ["collection", "queries & updates"]
  • ["collection", "set operations"]
  • ["dates", "accessor"]
  • ["dates", "arithmetic"]
  • ["dates", "construction"]
  • ["dates", "conversion"]
  • ["dates", "parse"]
  • ["dates", "query"]
  • ["dates", "string"]
  • ["find", "findall"]
  • ["find", "findnext"]
  • ["find", "findprev"]
  • ["frontend"]
  • ["inference", "abstract interpretation"]
  • ["inference"]
  • ["inference", "optimization"]
  • ["io", "array_limit"]
  • ["io", "read"]
  • ["io", "serialization"]
  • ["io"]
  • ["linalg", "arithmetic"]
  • ["linalg", "blas"]
  • ["linalg", "factorization"]
  • ["linalg"]
  • ["micro"]
  • ["misc"]
  • ["misc", "23042"]
  • ["misc", "afoldl"]
  • ["misc", "allocation elision view"]
  • ["misc", "bitshift"]
  • ["misc", "foldl"]
  • ["misc", "issue 12165"]
  • ["misc", "iterators"]
  • ["misc", "julia"]
  • ["misc", "parse"]
  • ["misc", "repeat"]
  • ["misc", "splatting"]
  • ["problem", "chaosgame"]
  • ["problem", "fem"]
  • ["problem", "go"]
  • ["problem", "grigoriadis khachiyan"]
  • ["problem", "imdb"]
  • ["problem", "json"]
  • ["problem", "laplacian"]
  • ["problem", "monte carlo"]
  • ["problem", "raytrace"]
  • ["problem", "seismic"]
  • ["problem", "simplex"]
  • ["problem", "spellcheck"]
  • ["problem", "stockcorr"]
  • ["problem", "ziggurat"]
  • ["random", "collections"]
  • ["random", "randstring"]
  • ["random", "ranges"]
  • ["random", "sequences"]
  • ["random", "types"]
  • ["scalar", "acos"]
  • ["scalar", "acosh"]
  • ["scalar", "arithmetic"]
  • ["scalar", "asin"]
  • ["scalar", "asinh"]
  • ["scalar", "atan"]
  • ["scalar", "atan2"]
  • ["scalar", "atanh"]
  • ["scalar", "cbrt"]
  • ["scalar", "cos"]
  • ["scalar", "cosh"]
  • ["scalar", "exp2"]
  • ["scalar", "expm1"]
  • ["scalar", "fastmath"]
  • ["scalar", "floatexp"]
  • ["scalar", "intfuncs"]
  • ["scalar", "iteration"]
  • ["scalar", "mod2pi"]
  • ["scalar", "predicate"]
  • ["scalar", "rem_pio2"]
  • ["scalar", "sin"]
  • ["scalar", "sincos"]
  • ["scalar", "sinh"]
  • ["scalar", "tan"]
  • ["scalar", "tanh"]
  • ["shootout"]
  • ["simd"]
  • ["sort", "insertionsort"]
  • ["sort", "issorted"]
  • ["sort", "mergesort"]
  • ["sort", "quicksort"]
  • ["sparse", "arithmetic"]
  • ["sparse", "constructors"]
  • ["sparse", "index"]
  • ["sparse", "matmul"]
  • ["sparse", "sparse matvec"]
  • ["sparse", "sparse solves"]
  • ["sparse", "transpose"]
  • ["string", "==(::AbstractString, ::AbstractString)"]
  • ["string", "==(::SubString, ::String)"]
  • ["string", "findfirst"]
  • ["string"]
  • ["string", "readuntil"]
  • ["string", "repeat"]
  • ["tuple", "index"]
  • ["tuple", "linear algebra"]
  • ["tuple", "misc"]
  • ["tuple", "reduction"]
  • ["union", "array"]

Version Info

Primary Build

Julia Version 1.9.0-DEV.241
Commit e0c5e96048 (2022-03-24 03:00 UTC)
Platform Info:
  OS: Linux (x86_64-linux-gnu)
      Ubuntu 20.04.3 LTS
  uname: Linux 5.4.0-94-generic #106-Ubuntu SMP Thu Jan 6 23:58:14 UTC 2022 x86_64 x86_64
  CPU: Intel(R) Xeon(R) CPU E3-1241 v3 @ 3.50GHz: 
              speed         user         nice          sys         idle          irq
       #1  3500 MHz     228473 s        491 s      45101 s   60021651 s          0 s
       #2  3207 MHz    5166269 s        540 s     212574 s   54946739 s          0 s
       #3  3486 MHz     224199 s        440 s      36067 s   60055977 s          0 s
       #4  3500 MHz     150758 s        434 s      34820 s   59868893 s          0 s
  Memory: 31.32097625732422 GB (14084.390625 MB free)
  Uptime: 6.038188e6 sec
  Load Avg:  1.04  1.01  1.0
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-13.0.1 (ORCJIT, haswell)
  Threads: 1 on 4 virtual cores