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Benchmark Report

Job Properties

Commit(s): JuliaLang/julia@a23a4ff08da5b6d95e9a35eee96e3260a452c02b

Triggered By: link

Tag Predicate: ALL

Daily Job: 2020-07-12 vs 2020-07-11

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\", \"Int\")"] 0.72 (5%) ✅ 1.00 (1%)
["array", "any/all", "(\"all\", \"BitArray\")"] 0.90 (5%) ✅ 1.00 (1%)
["array", "cat", "(\"hcat\", 5)"] 1.10 (5%) ❌ 1.00 (1%)
["array", "cat", "(\"vcat\", 5)"] 0.73 (5%) ✅ 1.00 (1%)
["array", "convert", "(\"Complex{Float64}\", \"Int\")"] 1.05 (5%) ❌ 1.00 (1%)
["array", "equality", "(\"==\", \"Vector{Float32}\")"] 1.08 (5%) ❌ 1.00 (1%)
["array", "equality", "(\"==\", \"Vector{Int64}\")"] 0.90 (5%) ✅ 1.00 (1%)
["array", "equality", "(\"isequal\", \"Vector{Int64} isequal Vector{Int64}\")"] 0.93 (5%) ✅ 1.00 (1%)
["array", "growth", "(\"append!\", 2048)"] 1.05 (5%) ❌ 1.00 (1%)
["array", "growth", "(\"prerend!\", 2048)"] 1.07 (5%) ❌ 1.00 (1%)
["array", "growth", "(\"prerend!\", 256)"] 1.07 (5%) ❌ 1.00 (1%)
["array", "growth", "(\"push_single!\", 2048)"] 1.09 (5%) ❌ 1.00 (1%)
["array", "index", "2d"] 1.17 (5%) ❌ 1.00 (1%)
["collection", "iteration", "(\"IdDict\", \"String\", \"iterate second\")"] 1.29 (25%) ❌ 1.00 (1%)
["collection", "iteration", "(\"Set\", \"Int\", \"iterate\")"] 0.37 (25%) ✅ 1.00 (1%)
["dates", "parse", "Date"] 0.94 (5%) ✅ 1.00 (1%)
["find", "findall", "(\"BitVector\", \"50-50\")"] 0.92 (5%) ✅ 1.00 (1%)
["find", "findnext", "(\"Vector{Bool}\", \"50-50\")"] 1.06 (5%) ❌ 1.00 (1%)
["find", "findnext", "(\"ispos\", \"Vector{Bool}\")"] 1.05 (5%) ❌ 1.00 (1%)
["find", "findprev", "(\"Vector{Bool}\", \"50-50\")"] 0.87 (5%) ✅ 1.00 (1%)
["find", "findprev", "(\"ispos\", \"Vector{Float64}\")"] 0.93 (5%) ✅ 1.00 (1%)
["find", "findprev", "(\"ispos\", \"Vector{Int8}\")"] 1.07 (5%) ❌ 1.00 (1%)
["io", "array_limit", "(\"display\", \"Matrix{Float64}(10000, 10000)\")"] 1.02 (5%) 1.04 (1%) ❌
["micro", "fib"] 1.15 (5%) ❌ 1.00 (1%)
["misc", "20517"] 1.09 (5%) ❌ 1.00 (1%)
["misc", "iterators", "zip(1:1000)"] 0.92 (5%) ✅ 1.00 (1%)
["misc", "iterators", "zip(1:1000, 1:1000, 1:1000, 1:1000)"] 1.06 (5%) ❌ 1.00 (1%)
["problem", "json", "parse_json"] 0.94 (5%) ✅ 1.00 (1%)
["random", "collections", "(\"rand\", \"MersenneTwister\", \"small Set\")"] 1.26 (25%) ❌ 1.00 (1%)
["random", "ranges", "(\"RangeGenerator\", \"BigInt\", \"1:170141183460469231731687303715884105728\")"] 0.65 (25%) ✅ 1.00 (1%)
["scalar", "arithmetic", "(\"rem type\", \"Int64\", \"BigInt\")"] 1.60 (40%) ❌ 1.00 (1%)
["scalar", "asin", "(\"0.975 <= abs(x) < 1.0\", \"negative argument\", \"Float64\")"] 1.43 (5%) ❌ 1.00 (1%)
["scalar", "atan", "(\"19/16 <= abs(x) < 39/16\", \"negative argument\", \"Float64\")"] 0.82 (5%) ✅ 1.00 (1%)
["scalar", "atan", "(\"7/16 <= abs(x) < 11/16\", \"negative argument\", \"Float64\")"] 0.78 (5%) ✅ 1.00 (1%)
["scalar", "atan", "(\"7/16 <= abs(x) < 11/16\", \"positive argument\", \"Float64\")"] 0.80 (5%) ✅ 1.00 (1%)
["scalar", "atan", "(\"very large\", \"negative argument\", \"Float32\")"] 1.13 (5%) ❌ 1.00 (1%)
["scalar", "atan2", "(\"abs(y/x) safe (small)\", \"y negative\", \"x negative\", \"Float32\")"] 0.79 (5%) ✅ 1.00 (1%)
["scalar", "atan2", "(\"x one\", \"Float32\")"] 0.93 (5%) ✅ 1.00 (1%)
["scalar", "atan2", "(\"x one\", \"Float64\")"] 1.25 (5%) ❌ 1.00 (1%)
["scalar", "atanh", "(\"0.5 <= abs(x) < 1\", \"negative argument\", \"Float64\")"] 1.22 (5%) ❌ 1.00 (1%)
["scalar", "atanh", "(\"0.5 <= abs(x) < 1\", \"positive argument\", \"Float64\")"] 1.18 (5%) ❌ 1.00 (1%)
["scalar", "cos", "(\"argument reduction (hard) abs(x) < 2π/4\", \"negative argument\", \"Float64\", \"sin_kernel\")"] 0.82 (5%) ✅ 1.00 (1%)
["scalar", "cos", "(\"argument reduction (hard) abs(x) < 6π/4\", \"positive argument\", \"Float32\", \"sin_kernel\")"] 1.25 (5%) ❌ 1.00 (1%)
["scalar", "cos", "(\"argument reduction (hard) abs(x) < 6π/4\", \"positive argument\", \"Float64\", \"sin_kernel\")"] 0.85 (5%) ✅ 1.00 (1%)
["scalar", "cos", "(\"argument reduction (hard) abs(x) < 8π/4\", \"negative argument\", \"Float64\", \"cos_kernel\")"] 1.13 (5%) ❌ 1.00 (1%)
["scalar", "cosh", "(\"0.00024414062f0 <= abs(x) < 9f0\", \"negative argument\", \"Float32\")"] 0.80 (5%) ✅ 1.00 (1%)
["scalar", "cosh", "(\"0.00024414062f0 <= abs(x) < 9f0\", \"positive argument\", \"Float32\")"] 0.78 (5%) ✅ 1.00 (1%)
["scalar", "cosh", "(\"9f0 <= abs(x) < 88.72283f0\", \"negative argument\", \"Float32\")"] 0.94 (5%) ✅ 1.00 (1%)
["scalar", "exp2", "(\"small\", \"positive argument\", \"Float64\")"] 0.93 (5%) ✅ 1.00 (1%)
["scalar", "exp2", "(\"zero\", \"Float32\")"] 0.93 (5%) ✅ 1.00 (1%)
["scalar", "fastmath", "(\"mul\", \"BigFloat\")"] 1.41 (40%) ❌ 1.00 (1%)
["scalar", "mod2pi", "(\"argument reduction (easy) abs(x) < 2.0^20π/4\", \"negative argument\", \"Float64\")"] 1.06 (5%) ❌ 1.00 (1%)
["scalar", "rem_pio2", "(\"argument reduction (paynehanek) abs(x) > 2.0^20*π/2\", \"positive argument\", \"Float64\")"] 1.10 (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", "sincos", "(\"argument reduction (easy) abs(x) < 5π/4\", \"negative argument\", \"Float64\")"] 1.07 (5%) ❌ 1.00 (1%)
["scalar", "tan", "(\"medium\", \"positive argument\", \"Float32\")"] 0.93 (5%) ✅ 1.00 (1%)
["sort", "issorted", "(\"reverse\", \"random\")"] 1.37 (30%) ❌ 1.00 (1%)
["sparse", "constructors", "(\"SymTridiagonal\", 100)"] 1.06 (5%) ❌ 1.00 (1%)
["sparse", "matmul", "(\"A_mul_B\", \"sparse 50x500, dense 500x5 -> dense 50x5\")"] 1.31 (30%) ❌ 1.00 (1%)
["sparse", "matmul", "(\"A_mul_Bt\", \"sparse 50x500, dense 5x500 -> dense 50x5\")"] 1.30 (30%) ❌ 1.00 (1%)
["sparse", "matmul", "(\"At_mul_B!\", \"sparse 400x4000, dense 400x40 -> dense 4000x40\")"] 1.36 (30%) ❌ 1.00 (1%)
["sparse", "matmul", "(\"At_mul_B!\", \"sparse 40x400, dense 40x400 -> dense 400x400\")"] 1.55 (30%) ❌ 1.00 (1%)
["sparse", "matmul", "(\"At_mul_B!\", \"sparse 40x4000, dense 40x40 -> dense 4000x40\")"] 1.32 (30%) ❌ 1.00 (1%)
["sparse", "matmul", "(\"At_mul_B\", \"sparse 500x500, dense 500x5 -> dense 500x5\")"] 1.41 (30%) ❌ 1.00 (1%)
["sparse", "matmul", "(\"At_mul_B\", \"sparse 50x500, dense 50x5 -> dense 500x5\")"] 1.31 (30%) ❌ 1.00 (1%)
["sparse", "sparse solves", "square system (lu), vector rhs"] 0.95 (5%) ✅ 1.00 (1%)
["string", "repeat", "repeat char 2"] 0.95 (5%) ✅ 1.00 (1%)
["tuple", "reduction", "(\"sum\", (2,))"] 0.88 (5%) ✅ 1.00 (1%)
["tuple", "reduction", "(\"sum\", (4, 4))"] 1.05 (5%) ❌ 1.00 (1%)
["tuple", "reduction", "(\"sumabs\", (2, 2))"] 0.93 (5%) ✅ 1.00 (1%)
["tuple", "reduction", "(\"sumabs\", (4,))"] 1.07 (5%) ❌ 1.00 (1%)
["union", "array", "(\"broadcast\", *, BigFloat, (false, false))"] 1.41 (5%) ❌ 1.00 (1%)
["union", "array", "(\"broadcast\", *, BigFloat, (false, true))"] 1.41 (5%) ❌ 1.00 (1%)
["union", "array", "(\"broadcast\", *, BigFloat, (true, true))"] 1.41 (5%) ❌ 1.00 (1%)
["union", "array", "(\"broadcast\", *, ComplexF64, (false, true))"] 0.87 (5%) ✅ 1.00 (1%)
["union", "array", "(\"broadcast\", *, ComplexF64, (true, true))"] 0.87 (5%) ✅ 1.00 (1%)
["union", "array", "(\"broadcast\", identity, ComplexF64, false)"] 0.65 (5%) ✅ 1.00 (1%)
["union", "array", "(\"broadcast\", identity, ComplexF64, true)"] 0.77 (5%) ✅ 1.00 (1%)
["union", "array", "(\"broadcast\", identity, Float32, false)"] 1.05 (5%) ❌ 1.00 (1%)
["union", "array", "(\"map\", *, BigFloat, (false, false))"] 1.44 (5%) ❌ 1.00 (1%)
["union", "array", "(\"map\", *, BigFloat, (false, true))"] 1.42 (5%) ❌ 1.00 (1%)
["union", "array", "(\"map\", *, BigFloat, (true, true))"] 1.43 (5%) ❌ 1.00 (1%)
["union", "array", "(\"map\", *, ComplexF64, (false, true))"] 0.84 (5%) ✅ 1.00 (1%)
["union", "array", "(\"map\", *, Float32, (false, false))"] 0.89 (5%) ✅ 1.00 (1%)
["union", "array", "(\"map\", *, Float32, (true, true))"] 0.94 (5%) ✅ 1.00 (1%)
["union", "array", "(\"map\", *, Float64, (false, false))"] 1.05 (5%) ❌ 1.00 (1%)
["union", "array", "(\"map\", *, Float64, (true, true))"] 1.08 (5%) ❌ 1.00 (1%)
["union", "array", "(\"map\", *, Int64, (false, true))"] 0.94 (5%) ✅ 1.00 (1%)
["union", "array", "(\"map\", *, Int64, (true, true))"] 0.83 (5%) ✅ 1.00 (1%)
["union", "array", "(\"map\", *, Int8, (false, true))"] 1.06 (5%) ❌ 1.00 (1%)
["union", "array", "(\"map\", abs, Float32, true)"] 0.94 (5%) ✅ 1.00 (1%)
["union", "array", "(\"map\", abs, Float64, true)"] 1.06 (5%) ❌ 1.00 (1%)
["union", "array", "(\"map\", abs, Int8, true)"] 1.11 (5%) ❌ 1.00 (1%)
["union", "array", "(\"map\", identity, BigInt, true)"] 0.75 (5%) ✅ 1.00 (1%)
["union", "array", "(\"map\", identity, ComplexF64, false)"] 0.63 (5%) ✅ 1.00 (1%)
["union", "array", "(\"map\", identity, Float64, true)"] 0.75 (5%) ✅ 1.00 (1%)
["union", "array", "(\"perf_binaryop\", *, BigFloat, (false, false))"] 1.34 (5%) ❌ 1.00 (1%)
["union", "array", "(\"perf_binaryop\", *, BigFloat, (false, true))"] 1.32 (5%) ❌ 1.00 (1%)
["union", "array", "(\"perf_binaryop\", *, BigFloat, (true, true))"] 1.33 (5%) ❌ 1.00 (1%)
["union", "array", "(\"perf_binaryop\", *, ComplexF64, (false, false))"] 0.85 (5%) ✅ 1.00 (1%)
["union", "array", "(\"perf_binaryop\", *, ComplexF64, (true, true))"] 0.88 (5%) ✅ 1.00 (1%)
["union", "array", "(\"perf_countequals\", \"Int8\")"] 1.06 (5%) ❌ 1.00 (1%)
["union", "array", "(\"perf_simplecopy\", ComplexF64, false)"] 0.74 (5%) ✅ 1.00 (1%)
["union", "array", "(\"perf_simplecopy\", Float64, false)"] 0.94 (5%) ✅ 1.00 (1%)
["union", "array", "(\"perf_sum2\", Float64, true)"] 0.71 (5%) ✅ 1.00 (1%)
["union", "array", "(\"perf_sum3\", Float32, true)"] 0.91 (5%) ✅ 1.00 (1%)
["union", "array", "(\"perf_sum\", Bool, false)"] 0.94 (5%) ✅ 1.00 (1%)
["union", "array", "(\"perf_sum\", Int8, false)"] 0.94 (5%) ✅ 1.00 (1%)
["union", "array", "(\"skipmissing\", sum, Union{Missing, Bool}, true)"] 0.77 (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.430
Commit a23a4ff (2020-07-11 14:53 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  149612412 s       5056 s   16875138 s  7054383271 s         37 s
       #2  3501 MHz  1131899714 s        217 s   22722519 s  6076760522 s         28 s
       #3  3501 MHz  134864096 s       3321 s   10038907 s  7085894628 s         38 s
       #4  3501 MHz  126402789 s         43 s   14373327 s  7088398250 s         28 s
       
  Memory: 31.383651733398438 GB (20742.390625 MB free)
  Uptime: 7.2370968e7 sec
  Load Avg:  1.0029296875  1.0146484375  1.04541015625
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-9.0.1 (ORCJIT, haswell)