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Benchmark Report

Job Properties

Commits: JuliaLang/julia@65ae6a53b45dc5358aef41e4a5956babe747c023 vs JuliaLang/julia@1f36c06eaf51a828e5ee8ee475454ac9d34ad474

Comparison Diff: link

Triggered By: link

Tag Predicate: !"scalar"

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
["alloc", "structs"] 0.94 (5%) ✅ 1.00 (1%)
["array", "cat", ("catnd", 5)] 1.07 (5%) ❌ 1.00 (1%)
["array", "comprehension", ("collect", "StepRangeLen{Float64, Base.TwicePrecision{Float64}, Base.TwicePrecision{Float64}, Int64}")] 1.77 (5%) ❌ 1.00 (1%)
["array", "comprehension", ("collect", "Vector{Float64}")] 4.06 (5%) ❌ 1.00 (1%)
["array", "comprehension", ("comprehension_collect", "StepRangeLen{Float64, Base.TwicePrecision{Float64}, Base.TwicePrecision{Float64}, Int64}")] 1.77 (5%) ❌ 1.00 (1%)
["array", "comprehension", ("comprehension_collect", "Vector{Float64}")] 2.27 (5%) ❌ 1.00 (1%)
["array", "comprehension", ("comprehension_indexing", "Vector{Float64}")] 1.37 (30%) ❌ 1.00 (1%)
["array", "comprehension", ("comprehension_iteration", "StepRangeLen{Float64, Base.TwicePrecision{Float64}, Base.TwicePrecision{Float64}, Int64}")] 1.23 (5%) ❌ 1.00 (1%)
["array", "comprehension", ("comprehension_iteration", "Vector{Float64}")] 1.40 (5%) ❌ 1.00 (1%)
["array", "equality", ("==", "UnitRange{Int64}")] 0.91 (5%) ✅ 1.00 (1%)
["array", "equality", ("==", "Vector{Int64} == UnitRange{Int64}")] 1.13 (5%) ❌ 1.00 (1%)
["array", "equality", ("==", "Vector{Int64}")] 0.87 (5%) ✅ 1.00 (1%)
["array", "equality", ("isequal", "UnitRange{Int64}")] 0.82 (5%) ✅ 1.00 (1%)
["array", "equality", ("isequal", "Vector{Int64} isequal Vector{Float64}")] 0.90 (5%) ✅ 1.00 (1%)
["array", "equality", ("isequal", "Vector{Int64} isequal Vector{Int64}")] 1.07 (5%) ❌ 1.00 (1%)
["array", "reductions", ("perf_reduce", "Int64")] 1.06 (5%) ❌ 1.00 (1%)
["broadcast", "dotop", ("Float64", "(1000, 1000)", 2)] 1.14 (5%) ❌ 1.00 (1%)
["broadcast", "mix_scalar_tuple", (10, "scal_tup_x3")] 1.07 (5%) ❌ 1.00 (1%)
["broadcast", "mix_scalar_tuple", (10, "tup_tup")] 1.21 (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")] 0.85 (5%) ✅ 1.00 (1%)
["broadcast", "mix_scalar_tuple", (5, "scal_tup")] 0.91 (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")] 1.09 (5%) ❌ 1.00 (1%)
["broadcast", "sparse", ("(1000, 1000)", 2)] 0.87 (5%) ✅ 1.00 (1%)
["broadcast", "typeargs", ("tuple", 10)] 1.09 (5%) ❌ 1.00 (1%)
["dates", "accessor", "millisecond"] 1.09 (5%) ❌ 1.00 (1%)
["dates", "arithmetic", ("Date", "Day")] 0.90 (5%) ✅ 1.00 (1%)
["dates", "arithmetic", ("DateTime", "Day")] 1.11 (5%) ❌ 1.00 (1%)
["dates", "arithmetic", ("DateTime", "Hour")] 1.11 (5%) ❌ 1.00 (1%)
["dates", "arithmetic", ("DateTime", "Millisecond")] 1.11 (5%) ❌ 1.00 (1%)
["dates", "arithmetic", ("DateTime", "Minute")] 1.11 (5%) ❌ 1.00 (1%)
["dates", "arithmetic", ("DateTime", "Second")] 0.90 (5%) ✅ 1.00 (1%)
["dates", "conversion", "Date -> DateTime"] 1.11 (5%) ❌ 1.00 (1%)
["dates", "parse", "Date"] 1.07 (5%) ❌ 1.00 (1%)
["find", "findall", ("> q0.5", "Vector{Float32}")] 1.09 (5%) ❌ 1.00 (1%)
["find", "findall", ("> q0.8", "Vector{Float32}")] 0.94 (5%) ✅ 1.00 (1%)
["find", "findall", ("> q0.8", "Vector{Int8}")] 0.90 (5%) ✅ 1.00 (1%)
["find", "findall", ("> q0.95", "Vector{Float32}")] 1.06 (5%) ❌ 1.00 (1%)
["find", "findall", ("> q0.99", "Vector{UInt8}")] 1.06 (5%) ❌ 1.00 (1%)
["find", "findall", ("ispos", "Vector{Int8}")] 1.07 (5%) ❌ 1.00 (1%)
["find", "findnext", ("Vector{Bool}", "50-50")] 1.05 (5%) ❌ 1.00 (1%)
["find", "findprev", ("ispos", "Vector{Float32}")] 0.85 (5%) ✅ 1.00 (1%)
["inference", "optimization", "quadratic"] 1.08 (5%) ❌ 1.00 (1%)
["io", "serialization", ("serialize", "Matrix{Float64}")] 1.11 (5%) ❌ 1.00 (1%)
["micro", "fib"] 1.13 (5%) ❌ 1.00 (1%)
["misc", "23042", "ComplexF32"] 1.36 (5%) ❌ 1.00 (1%)
["misc", "bitshift", ("Int", "UInt")] 0.92 (5%) ✅ 1.00 (1%)
["misc", "foldl", "foldl(+, filter(...))"] 1.05 (5%) ❌ 1.00 (1%)
["misc", "iterators", "zip(1:1, 1:1, 1:1)"] 0.86 (5%) ✅ 1.00 (1%)
["problem", "simplex", "simplex"] 1.09 (5%) ❌ 1.00 (1%)
["random", "ranges", ("rand", "MersenneTwister", "Int128", "RangeGenerator(1:1)")] 1.48 (25%) ❌ 1.00 (1%)
["random", "ranges", ("rand", "MersenneTwister", "Int128", "RangeGenerator(1:18446744073709551615)")] 1.46 (25%) ❌ 1.00 (1%)
["random", "ranges", ("rand", "MersenneTwister", "Int128", "RangeGenerator(1:18446744073709551616)")] 1.46 (25%) ❌ 1.00 (1%)
["random", "ranges", ("rand", "MersenneTwister", "Int128", "RangeGenerator(1:4294967295)")] 1.48 (25%) ❌ 1.00 (1%)
["random", "ranges", ("rand", "MersenneTwister", "UInt128", "RangeGenerator(1:1)")] 1.48 (25%) ❌ 1.00 (1%)
["random", "ranges", ("rand", "MersenneTwister", "UInt128", "RangeGenerator(1:170141183460469231731687303715884105728)")] 1.43 (25%) ❌ 1.00 (1%)
["random", "ranges", ("rand", "MersenneTwister", "UInt128", "RangeGenerator(1:18446744073709551615)")] 1.45 (25%) ❌ 1.00 (1%)
["random", "ranges", ("rand", "MersenneTwister", "UInt128", "RangeGenerator(1:18446744073709551616)")] 1.46 (25%) ❌ 1.00 (1%)
["random", "ranges", ("rand", "MersenneTwister", "UInt128", "RangeGenerator(1:4294967295)")] 1.48 (25%) ❌ 1.00 (1%)
["random", "types", ("rand", "MersenneTwister", "ComplexF64")] 2.09 (25%) ❌ 1.00 (1%)
["random", "types", ("rand", "MersenneTwister", "Complex{Int64}")] 1.47 (25%) ❌ 1.00 (1%)
["random", "types", ("rand", "MersenneTwister", "Complex{UInt64}")] 1.48 (25%) ❌ 1.00 (1%)
["random", "types", ("rand", "MersenneTwister", "Float16")] 0.72 (25%) ✅ 1.00 (1%)
["random", "types", ("rand", "MersenneTwister", "Float32")] 0.72 (25%) ✅ 1.00 (1%)
["random", "types", ("rand", "MersenneTwister", "Float64")] 0.73 (25%) ✅ 1.00 (1%)
["random", "types", ("rand", "MersenneTwister", "ImplicitFloat64")] 0.72 (25%) ✅ 1.00 (1%)
["random", "types", ("rand", "MersenneTwister", "Int128")] 2.12 (25%) ❌ 1.00 (1%)
["random", "types", ("rand", "MersenneTwister", "Int16")] 0.61 (25%) ✅ 1.00 (1%)
["random", "types", ("rand", "MersenneTwister", "Int64")] 1.29 (25%) ❌ 1.00 (1%)
["random", "types", ("rand", "MersenneTwister", "UInt128")] 2.24 (25%) ❌ 1.00 (1%)
["random", "types", ("rand", "MersenneTwister", "UInt8")] 0.61 (25%) ✅ 1.00 (1%)
["random", "types", ("randn", "MersenneTwister", "ComplexF64")] 1.40 (25%) ❌ 1.00 (1%)
["shootout", "binary_trees"] 0.94 (5%) ✅ 1.00 (1%)
["simd", ("CartesianPartition", "conditional_loop!", "Int32", 2, 63)] 0.60 (20%) ✅ 1.00 (1%)
["sparse", "constructors", ("Tridiagonal", 10)] 1.06 (5%) ❌ 1.00 (1%)
["sparse", "matmul", ("A_mul_B", "sparse 50x500, dense 500x5 -> dense 50x5")] 1.37 (30%) ❌ 1.00 (1%)
["sparse", "matmul", ("A_mul_Bt", "sparse 500x500, dense 5x500 -> dense 500x5")] 1.40 (30%) ❌ 1.00 (1%)
["sparse", "matmul", ("A_mul_Bt", "sparse 50x500, dense 5x500 -> dense 50x5")] 1.44 (30%) ❌ 1.00 (1%)
["sparse", "matmul", ("Ac_mul_B!", "sparse 200x2000, dense 200x20 -> dense 2000x20")] 1.88 (30%) ❌ 1.00 (1%)
["sparse", "matmul", ("At_mul_B", "dense 5x500, sparse 5x5 -> dense 500x5")] 1.34 (30%) ❌ 1.00 (1%)
["string", "repeat", "repeat str len 16"] 1.08 (5%) ❌ 1.00 (1%)
["tuple", "linear algebra", ("matmat", "(8, 8)", "(8, 8)")] 0.86 (5%) ✅ 1.00 (1%)
["tuple", "linear algebra", ("matvec", "(2, 2)", "(2,)")] 1.07 (5%) ❌ 1.00 (1%)
["tuple", "linear algebra", ("matvec", "(8, 8)", "(8,)")] 0.94 (5%) ✅ 1.00 (1%)
["tuple", "misc", "11899"] 2.68 (5%) ❌ 1.00 (1%)
["tuple", "reduction", ("sum", "(16, 16)")] 0.93 (5%) ✅ 1.00 (1%)
["tuple", "reduction", ("sum", "(8,)")] 0.91 (5%) ✅ 1.00 (1%)
["tuple", "reduction", ("sumabs", "(2,)")] 0.89 (5%) ✅ 1.00 (1%)
["tuple", "reduction", ("sumabs", "(4,)")] 1.07 (5%) ❌ 1.00 (1%)
["tuple", "reduction", ("sumabs", "(8,)")] 1.06 (5%) ❌ 1.00 (1%)
["union", "array", ("broadcast", "*", "Bool", "(false, false)")] 0.93 (5%) ✅ 1.00 (1%)
["union", "array", ("broadcast", "*", "Float32", "(true, true)")] 1.06 (5%) ❌ 1.00 (1%)
["union", "array", ("broadcast", "identity", "Bool", 1)] 1.22 (5%) ❌ 1.00 (1%)
["union", "array", ("broadcast", "identity", "Int8", 0)] 1.06 (5%) ❌ 1.00 (1%)
["union", "array", ("collect", "all", "BigFloat", 1)] 0.94 (5%) ✅ 1.00 (1%)
["union", "array", ("collect", "all", "Bool", 0)] 0.95 (5%) ✅ 1.00 (1%)
["union", "array", ("collect", "all", "Bool", 1)] 0.86 (5%) ✅ 1.00 (1%)
["union", "array", ("collect", "all", "Float32", 1)] 1.07 (5%) ❌ 1.00 (1%)
["union", "array", ("collect", "all", "Int8", 1)] 1.19 (5%) ❌ 1.00 (1%)
["union", "array", ("map", "*", "Bool", "(false, true)")] 1.07 (5%) ❌ 1.00 (1%)
["union", "array", ("map", "*", "Bool", "(true, true)")] 1.07 (5%) ❌ 1.00 (1%)
["union", "array", ("map", "*", "ComplexF64", "(false, false)")] 0.95 (5%) ✅ 1.00 (1%)
["union", "array", ("map", "*", "Int8", "(false, true)")] 0.95 (5%) ✅ 1.00 (1%)
["union", "array", ("map", "abs", "BigFloat", 1)] 0.95 (5%) ✅ 1.00 (1%)
["union", "array", ("map", "abs", "Int8", 1)] 1.17 (5%) ❌ 1.00 (1%)
["union", "array", ("map", "identity", "BigFloat", 1)] 0.95 (5%) ✅ 1.00 (1%)
["union", "array", ("map", "identity", "Bool", 0)] 0.95 (5%) ✅ 1.00 (1%)
["union", "array", ("map", "identity", "Bool", 1)] 0.86 (5%) ✅ 1.00 (1%)
["union", "array", ("map", "identity", "Float32", 1)] 1.07 (5%) ❌ 1.00 (1%)
["union", "array", ("map", "identity", "Int8", 0)] 1.06 (5%) ❌ 1.00 (1%)
["union", "array", ("map", "identity", "Int8", 1)] 1.19 (5%) ❌ 1.00 (1%)
["union", "array", ("perf_binaryop", "*", "Bool", "(false, true)")] 0.93 (5%) ✅ 1.00 (1%)
["union", "array", ("perf_countequals", "ComplexF64")] 1.22 (5%) ❌ 1.00 (1%)
["union", "array", ("perf_simplecopy", "BigInt", 1)] 1.05 (5%) ❌ 1.00 (1%)
["union", "array", ("perf_simplecopy", "Bool", 1)] 1.06 (5%) ❌ 1.00 (1%)
["union", "array", ("perf_sum", "BigFloat", 0)] 0.95 (5%) ✅ 1.00 (1%)
["union", "array", ("perf_sum3", "ComplexF64", 1)] 1.07 (5%) ❌ 1.00 (1%)
["union", "array", ("perf_sum3", "Int8", 1)] 0.94 (5%) ✅ 1.00 (1%)
["union", "array", ("skipmissing", "perf_sumskipmissing", "BigFloat", 0)] 0.95 (5%) ✅ 1.00 (1%)
["union", "array", ("skipmissing", "sum", "Union{Missing, BigFloat}", 1)] 0.94 (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", "allinference"]
  • ["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"]
  • ["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.817
Commit 65ae6a53b4 (2022-06-20 15:29 UTC)
Platform Info:
  OS: Linux (x86_64-linux-gnu)
      Ubuntu 20.04.4 LTS
  uname: Linux 5.4.0-113-generic #127-Ubuntu SMP Wed May 18 14:30:56 UTC 2022 x86_64 x86_64
  CPU: Intel(R) Xeon(R) CPU E3-1241 v3 @ 3.50GHz: 
              speed         user         nice          sys         idle          irq
       #1  3524 MHz      57064 s        151 s      15409 s   23373432 s          0 s
       #2  2942 MHz    1922644 s         54 s      93936 s   21435750 s          0 s
       #3  3010 MHz      48030 s         46 s      10560 s   23382776 s          0 s
       #4  3002 MHz      35405 s         62 s      10170 s   23388512 s          0 s
       #5  2941 MHz      41579 s         36 s      10458 s   23317854 s          0 s
       #6  3045 MHz      37547 s         20 s      10246 s   23394849 s          0 s
       #7  3087 MHz      38130 s         32 s      10591 s   23402569 s          0 s
       #8  3014 MHz      44268 s         51 s      10353 s   23391395 s          0 s
  Memory: 31.32082748413086 GB (15907.36328125 MB free)
  Uptime: 2.3470324e6 sec
  Load Avg:  1.13  1.06  1.02
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-14.0.5 (ORCJIT, haswell)
  Threads: 1 on 8 virtual cores

Comparison Build

Julia Version 1.9.0-DEV.814
Commit 1f36c06eaf (2022-06-20 09:58 UTC)
Platform Info:
  OS: Linux (x86_64-linux-gnu)
      Ubuntu 20.04.4 LTS
  uname: Linux 5.4.0-113-generic #127-Ubuntu SMP Wed May 18 14:30:56 UTC 2022 x86_64 x86_64
  CPU: Intel(R) Xeon(R) CPU E3-1241 v3 @ 3.50GHz: 
              speed         user         nice          sys         idle          irq
       #1  2793 MHz      57659 s        151 s      15565 s   23474212 s          0 s
       #2  3012 MHz    2018215 s         54 s      98093 s   21437723 s          0 s
       #3  2987 MHz      48086 s         46 s      10573 s   23484387 s          0 s
       #4  3165 MHz      35480 s         62 s      10181 s   23490037 s          0 s
       #5  3008 MHz      41650 s         36 s      10468 s   23419365 s          0 s
       #6  3043 MHz      37566 s         20 s      10256 s   23496515 s          0 s
       #7  2987 MHz      39094 s         32 s      10621 s   23503270 s          0 s
       #8  2485 MHz      44728 s         51 s      10372 s   23492613 s          0 s
  Memory: 31.32082748413086 GB (15820.12890625 MB free)
  Uptime: 2.35720274e6 sec
  Load Avg:  1.09  1.04  1.01
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-14.0.3 (ORCJIT, haswell)
  Threads: 1 on 8 virtual cores