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Benchmark Report

Job Properties

Commit(s): JuliaLang/julia@85a0f141d4a2a23d25328071dc1ac86aac2bc0f0

Triggered By: link

Tag Predicate: ALL

Daily Job: 2020-09-13 vs 2020-09-12

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\", \"dim1\")"] 0.94 (5%) ✅ 1.00 (1%)
["array", "accumulate", "(\"cumsum\", \"Int\")"] 1.08 (5%) ❌ 1.00 (1%)
["array", "any/all", "(\"all\", \"BitArray\")"] 1.11 (5%) ❌ 1.00 (1%)
["array", "cat", "(\"hvcat_setind\", 5)"] 1.06 (5%) ❌ 1.00 (1%)
["array", "equality", "(\"==\", \"Vector{Bool}\")"] 0.91 (5%) ✅ 1.00 (1%)
["array", "equality", "(\"==\", \"Vector{Int64} == UnitRange{Int64}\")"] 0.88 (5%) ✅ 1.00 (1%)
["array", "equality", "(\"==\", \"Vector{Int64}\")"] 0.94 (5%) ✅ 1.00 (1%)
["array", "equality", "(\"isequal\", \"Vector{Int64} isequal Vector{Float64}\")"] 0.90 (5%) ✅ 1.00 (1%)
["array", "growth", "(\"append!\", 2048)"] 1.06 (5%) ❌ 1.00 (1%)
["array", "growth", "(\"prerend!\", 256)"] 1.10 (5%) ❌ 1.00 (1%)
["array", "index", "(\"sumcartesian_view\", \"SubArray{Int32, 2, Base.ReshapedArray{Int32, 2, SubArray{Int32, 3, Array{Int32, 3}, Tuple{Base.Slice{Base.OneTo{Int64}}, Base.Slice{Base.OneTo{Int64}}, Base.Slice{Base.OneTo{Int64}}}, true}, Tuple{}}, Tuple{Base.Slice{Base.OneTo{Int64}}, UnitRange{Int64}}, true}\")"] 1.64 (50%) ❌ 1.00 (1%)
["array", "setindex!", "(\"setindex!\", 1)"] 0.94 (5%) ✅ 1.00 (1%)
["array", "setindex!", "(\"setindex!\", 2)"] 1.07 (5%) ❌ 1.00 (1%)
["array", "setindex!", "(\"setindex!\", 5)"] 1.07 (5%) ❌ 1.00 (1%)
["broadcast", "mix_scalar_tuple", "(3, \"scal_tup_x3\")"] 0.94 (5%) ✅ 1.00 (1%)
["broadcast", "sparse", "((1000, 1000), 1)"] 1.13 (5%) ❌ 1.00 (1%)
["collection", "queries & updates", "(\"Dict\", \"String\", \"getindex\")"] 1.29 (25%) ❌ 1.00 (1%)
["collection", "queries & updates", "(\"IdDict\", \"Int\", \"push!\", \"overwrite\")"] 1.25 (25%) ❌ 1.00 (1%)
["collection", "queries & updates", "(\"IdDict\", \"Int\", \"setindex!\", \"new\")"] 1.29 (25%) ❌ 1.00 (1%)
["collection", "queries & updates", "(\"Set\", \"Int\", \"in\", \"true\")"] 0.69 (25%) ✅ 1.00 (1%)
["collection", "set operations", "(\"BitSet\", \"Int\", \"intersect\", \"Vector\")"] 1.64 (25%) ❌ 1.00 (1%)
["collection", "set operations", "(\"BitSet\", \"Int\", \"intersect\", \"Vector\", \"Vector\")"] 1.48 (25%) ❌ 1.00 (1%)
["dates", "accessor", "hour"] 1.08 (5%) ❌ 1.00 (1%)
["dates", "parse", "(\"DateTime\", \"RFC1123Format\", \"Lowercase\")"] 1.07 (5%) ❌ 1.00 (1%)
["dates", "parse", "(\"DateTime\", \"RFC1123Format\", \"Titlecase\")"] 1.07 (5%) ❌ 1.00 (1%)
["find", "findall", "(\"Vector{Bool}\", \"10-90\")"] 1.23 (5%) ❌ 1.00 (1%)
["find", "findall", "(\"Vector{Bool}\", \"50-50\")"] 1.07 (5%) ❌ 1.00 (1%)
["find", "findnext", "(\"ispos\", \"Vector{UInt64}\")"] 1.10 (5%) ❌ 1.00 (1%)
["io", "array_limit", "(\"display\", \"Matrix{Float64}(10000, 10000)\")"] 0.91 (5%) ✅ 0.90 (1%) ✅
["io", "serialization", "(\"serialize\", \"Vector{String}\")"] 0.94 (5%) ✅ 1.00 (1%)
["misc", "afoldl", "Complex{Float64}"] 0.77 (5%) ✅ 1.00 (1%)
["misc", "allocation elision view", "conditional"] 0.88 (5%) ✅ 1.00 (1%)
["misc", "bitshift", "(\"UInt\", \"UInt\")"] 1.07 (5%) ❌ 1.00 (1%)
["misc", "fastmath many args"] 1.09 (5%) ❌ 1.00 (1%)
["misc", "issue 12165", "Float64"] 1.46 (5%) ❌ 1.00 (1%)
["misc", "iterators", "zip(1:1)"] 1.10 (5%) ❌ 1.00 (1%)
["misc", "iterators", "zip(1:1000, 1:1000)"] 1.09 (5%) ❌ 1.00 (1%)
["misc", "iterators", "zip(1:1000, 1:1000, 1:1000, 1:1000)"] 1.06 (5%) ❌ 1.00 (1%)
["random", "collections", "(\"rand\", \"MersenneTwister\", \"large String\")"] 1.30 (25%) ❌ 1.00 (1%)
["random", "collections", "(\"rand\", \"MersenneTwister\", \"small String\")"] 1.30 (25%) ❌ 1.00 (1%)
["random", "ranges", "(\"RangeGenerator\", \"BigInt\", \"1:4294967295\")"] 0.62 (25%) ✅ 1.00 (1%)
["random", "ranges", "(\"rand\", \"MersenneTwister\", \"BigInt\", \"RangeGenerator(1:18446744073709551615)\")"] 0.73 (25%) ✅ 1.00 (1%)
["random", "ranges", "(\"rand\", \"MersenneTwister\", \"BigInt\", \"RangeGenerator(1:2^10000)\")"] 1.36 (25%) ❌ 1.00 (1%)
["random", "ranges", "(\"rand\", \"MersenneTwister\", \"BigInt\", \"RangeGenerator(1:4294967295)\")"] 1.48 (25%) ❌ 1.00 (1%)
["scalar", "acosh", "(\"very large\", \"positive argument\", \"Float64\")"] 1.08 (5%) ❌ 1.00 (1%)
["scalar", "asin", "(\"0.5 <= abs(x) < 0.975\", \"negative argument\", \"Float64\")"] 1.25 (5%) ❌ 1.00 (1%)
["scalar", "asin", "(\"abs(x) < 0.5\", \"negative argument\", \"Float64\")"] 1.09 (5%) ❌ 1.00 (1%)
["scalar", "asin", "(\"abs(x) < 0.5\", \"positive argument\", \"Float64\")"] 1.06 (5%) ❌ 1.00 (1%)
["scalar", "asin", "(\"small\", \"positive argument\", \"Float64\")"] 0.85 (5%) ✅ 1.00 (1%)
["scalar", "asinh", "(\"very large\", \"negative argument\", \"Float32\")"] 0.87 (5%) ✅ 1.00 (1%)
["scalar", "atan", "(\"0 <= abs(x) < 7/16\", \"positive argument\", \"Float64\")"] 1.07 (5%) ❌ 1.00 (1%)
["scalar", "atan", "(\"7/16 <= abs(x) < 11/16\", \"negative argument\", \"Float32\")"] 1.08 (5%) ❌ 1.00 (1%)
["scalar", "atan2", "(\"abs(y/x) high\", \"y negative\", \"x positive\", \"Float64\")"] 1.07 (5%) ❌ 1.00 (1%)
["scalar", "atan2", "(\"abs(y/x) small\", \"y positive\", \"x positive\", \"Float32\")"] 0.94 (5%) ✅ 1.00 (1%)
["scalar", "atan2", "(\"abs(y/x) small\", \"y positive\", \"x positive\", \"Float64\")"] 1.13 (5%) ❌ 1.00 (1%)
["scalar", "atan2", "(\"x one\", \"Float32\")"] 1.45 (5%) ❌ 1.00 (1%)
["scalar", "atan2", "(\"x one\", \"Float64\")"] 1.28 (5%) ❌ 1.00 (1%)
["scalar", "atan2", "(\"y infinite\", \"y negative\", \"x finite\", \"x negative\", \"Float64\")"] 1.23 (5%) ❌ 1.00 (1%)
["scalar", "atanh", "(\"0.5 <= abs(x) < 1\", \"negative argument\", \"Float64\")"] 0.82 (5%) ✅ 1.00 (1%)
["scalar", "atanh", "(\"very small\", \"negative argument\", \"Float32\")"] 0.94 (5%) ✅ 1.00 (1%)
["scalar", "atanh", "(\"very small\", \"negative argument\", \"Float64\")"] 1.06 (5%) ❌ 1.00 (1%)
["scalar", "atanh", "(\"very small\", \"positive argument\", \"Float32\")"] 1.06 (5%) ❌ 1.00 (1%)
["scalar", "atanh", "(\"very small\", \"positive argument\", \"Float64\")"] 1.06 (5%) ❌ 1.00 (1%)
["scalar", "cos", "(\"argument reduction (easy) abs(x) < 2π/4\", \"negative argument\", \"Float32\", \"sin_kernel\")"] 1.08 (5%) ❌ 1.00 (1%)
["scalar", "cos", "(\"argument reduction (easy) abs(x) < 2π/4\", \"negative argument\", \"Float64\", \"sin_kernel\")"] 1.08 (5%) ❌ 1.00 (1%)
["scalar", "cos", "(\"argument reduction (easy) abs(x) < 3π/4\", \"negative argument\", \"Float64\", \"sin_kernel\")"] 1.08 (5%) ❌ 1.00 (1%)
["scalar", "cos", "(\"no reduction\", \"negative argument\", \"Float32\", \"cos_kernel\")"] 1.10 (5%) ❌ 1.00 (1%)
["scalar", "cos", "(\"no reduction\", \"negative argument\", \"Float64\", \"cos_kernel\")"] 1.10 (5%) ❌ 1.00 (1%)
["scalar", "cos", "(\"no reduction\", \"positive argument\", \"Float32\", \"cos_kernel\")"] 1.10 (5%) ❌ 1.00 (1%)
["scalar", "cos", "(\"no reduction\", \"positive argument\", \"Float64\", \"cos_kernel\")"] 1.10 (5%) ❌ 1.00 (1%)
["scalar", "cos", "(\"no reduction\", \"zero\", \"Float32\")"] 1.06 (5%) ❌ 1.00 (1%)
["scalar", "cos", "(\"no reduction\", \"zero\", \"Float64\")"] 1.13 (5%) ❌ 1.00 (1%)
["scalar", "cosh", "(\"0 <= abs(x) < 2.7755602085408512e-17\", \"negative argument\", \"Float64\")"] 0.94 (5%) ✅ 1.00 (1%)
["scalar", "cosh", "(\"0 <= abs(x) < 2.7755602085408512e-17\", \"positive argument\", \"Float64\")"] 0.94 (5%) ✅ 1.00 (1%)
["scalar", "cosh", "(\"0.00024414062f0 <= abs(x) < 9f0\", \"positive argument\", \"Float32\")"] 1.22 (5%) ❌ 1.00 (1%)
["scalar", "cosh", "(\"very small\", \"negative argument\", \"Float64\")"] 0.94 (5%) ✅ 1.00 (1%)
["scalar", "cosh", "(\"very small\", \"positive argument\", \"Float64\")"] 1.07 (5%) ❌ 1.00 (1%)
["scalar", "exp2", "(\"small\", \"negative argument\", \"Float32\")"] 0.88 (5%) ✅ 1.00 (1%)
["scalar", "exp2", "(\"small\", \"negative argument\", \"Float64\")"] 0.93 (5%) ✅ 1.00 (1%)
["scalar", "expm1", "(\"huge\", \"positive argument\", \"Float64\")"] 1.07 (5%) ❌ 1.00 (1%)
["scalar", "mod2pi", "(\"argument reduction (easy) abs(x) < 2.0^20π/4\", \"negative argument\", \"Float64\")"] 0.86 (5%) ✅ 1.00 (1%)
["scalar", "sin", "(\"argument reduction (easy) abs(x) < 6π/4\", \"positive argument\", \"Float32\", \"cos_kernel\")"] 0.84 (5%) ✅ 1.00 (1%)
["scalar", "sin", "(\"argument reduction (hard) abs(x) < 4π/4\", \"negative argument\", \"Float64\", \"sin_kernel\")"] 1.23 (5%) ❌ 1.00 (1%)
["scalar", "sincos", "(\"argument reduction (easy) abs(x) < 2.0^20π/4\", \"negative argument\", \"Float64\")"] 0.82 (5%) ✅ 1.00 (1%)
["scalar", "tan", "(\"very small\", \"positive argument\", \"Float64\")"] 0.94 (5%) ✅ 1.00 (1%)
["shootout", "binary_trees"] 1.07 (5%) ❌ 1.00 (1%)
["sparse", "constructors", "(\"Bidiagonal\", 100)"] 1.33 (5%) ❌ 1.00 (1%)
["sparse", "constructors", "(\"IV\", 1000)"] 1.10 (5%) ❌ 1.00 (1%)
["sparse", "index", "(\"spvec\", \"integer\", 1000)"] 2.42 (30%) ❌ 1.00 (1%)
["sparse", "matmul", "(\"A_mul_B\", \"sparse 50x500, dense 500x5 -> dense 50x5\")"] 1.31 (30%) ❌ 1.00 (1%)
["string", "repeat", "repeat str len 16"] 1.07 (5%) ❌ 1.00 (1%)
["tuple", "reduction", "(\"sumabs\", (4,))"] 1.07 (5%) ❌ 1.00 (1%)
["union", "array", "(\"map\", *, Float32, (false, true))"] 1.06 (5%) ❌ 1.00 (1%)
["union", "array", "(\"map\", abs, Bool, true)"] 0.89 (5%) ✅ 1.00 (1%)
["union", "array", "(\"map\", abs, Float32, true)"] 1.24 (5%) ❌ 1.00 (1%)
["union", "array", "(\"map\", abs, Float64, true)"] 1.18 (5%) ❌ 1.00 (1%)
["union", "array", "(\"map\", identity, BigInt, true)"] 0.95 (5%) ✅ 1.00 (1%)
["union", "array", "(\"map\", identity, Bool, true)"] 0.94 (5%) ✅ 1.00 (1%)
["union", "array", "(\"map\", identity, Float32, true)"] 1.12 (5%) ❌ 1.00 (1%)
["union", "array", "(\"perf_binaryop\", *, Int8, (false, false))"] 0.91 (5%) ✅ 1.00 (1%)
["union", "array", "(\"perf_countequals\", \"Float32\")"] 1.05 (5%) ❌ 1.00 (1%)
["union", "array", "(\"perf_simplecopy\", Float64, false)"] 0.93 (5%) ✅ 1.00 (1%)
["union", "array", "(\"perf_simplecopy\", Int64, false)"] 0.94 (5%) ✅ 1.00 (1%)
["union", "array", "(\"perf_sum2\", Bool, true)"] 1.37 (5%) ❌ 1.00 (1%)
["union", "array", "(\"perf_sum2\", Float64, true)"] 1.41 (5%) ❌ 1.00 (1%)
["union", "array", "(\"perf_sum3\", BigFloat, true)"] 0.95 (5%) ✅ 1.00 (1%)
["union", "array", "(\"perf_sum3\", Float64, true)"] 0.87 (5%) ✅ 1.00 (1%)
["union", "array", "(\"perf_sum3\", Int64, false)"] 0.90 (5%) ✅ 1.00 (1%)
["union", "array", "(\"perf_sum3\", Int64, true)"] 0.89 (5%) ✅ 1.00 (1%)
["union", "array", "(\"perf_sum4\", BigInt, false)"] 0.91 (5%) ✅ 1.00 (1%)
["union", "array", "(\"perf_sum\", Bool, false)"] 1.07 (5%) ❌ 1.00 (1%)
["union", "array", "(\"perf_sum\", Bool, true)"] 0.73 (5%) ✅ 1.00 (1%)
["union", "array", "(\"perf_sum\", Float32, true)"] 0.71 (5%) ✅ 1.00 (1%)
["union", "array", "(\"perf_sum\", Int8, false)"] 1.06 (5%) ❌ 1.00 (1%)
["union", "array", "(\"skipmissing\", collect, Int64, false)"] 1.09 (5%) ❌ 1.00 (1%)
["union", "array", "(\"skipmissing\", sum, Union{Missing, Bool}, true)"] 0.77 (5%) ✅ 1.00 (1%)
["union", "array", "(\"skipmissing\", sum, Union{Missing, Float64}, true)"] 1.33 (5%) ❌ 1.00 (1%)

Benchmark Group List

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

  • ["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"]
  • ["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", "issue 12165"]
  • ["misc", "iterators"]
  • ["misc", "julia"]
  • ["misc", "parse"]
  • ["misc", "repeat"]
  • ["misc", "splatting"]
  • ["parallel", "remotecall"]
  • ["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", "findfirst"]
  • ["string"]
  • ["string", "readuntil"]
  • ["string", "repeat"]
  • ["tuple", "index"]
  • ["tuple", "linear algebra"]
  • ["tuple", "misc"]
  • ["tuple", "reduction"]
  • ["union", "array"]

Version Info

Primary Build

Julia Version 1.6.0-DEV.903
Commit 85a0f14 (2020-09-12 21:31 UTC)
Platform Info:
  OS: Linux (x86_64-linux-gnu)
      Ubuntu 14.04.5 LTS
  uname: Linux 3.13.0-85-generic #129-Ubuntu SMP Thu Mar 17 20:50:15 UTC 2016 x86_64 x86_64
  CPU: Intel(R) Xeon(R) CPU E3-1241 v3 @ 3.50GHz: 
              speed         user         nice          sys         idle          irq
       #1  3501 MHz  159158275 s       5069 s   17591244 s  7588032505 s         39 s
       #2  3501 MHz  1211897123 s        220 s   24695394 s  6538815948 s         29 s
       #3  3501 MHz  144726084 s       3325 s   10654932 s  7618621746 s         40 s
       #4  3501 MHz  135412816 s         43 s   15551795 s  7621361169 s         28 s
       
  Memory: 31.383651733398438 GB (14499.3203125 MB free)
  Uptime: 7.7814457e7 sec
  Load Avg:  1.0029296875  1.0146484375  1.04541015625
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-9.0.1 (ORCJIT, haswell)