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Benchmark Report

Job Properties

Commit(s): JuliaLang/julia@5fafb36a8da892f51ee32d886f8f3995719f97cc

Triggered By: link

Tag Predicate: ALL

Daily Job: 2017-12-22 vs 2017-12-21

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", "any/all", "(\"all\", \"BitArray\")"] 1.21 (15%) ❌ 1.00 (1%)
["array", "any/all", "(\"any\", \"Array{Float32,1}\")"] 0.80 (15%) ✅ 1.00 (1%)
["array", "any/all", "(\"any\", \"Array{Float64,1} generator\")"] 0.79 (15%) ✅ 1.00 (1%)
["array", "convert", "(\"Int\", \"Complex{Float64}\")"] 2.48 (15%) ❌ 1.00 (1%)
["array", "equality", "(\"==\", \"Array{Int64,1} == UnitRange{Int64}\")"] 686.20 (15%) ❌ 1.00 (1%)
["array", "equality", "(\"isequal\", \"Array{Int64,1} isequal UnitRange{Int64}\")"] 685.73 (15%) ❌ 1.00 (1%)
["array", "growth", "(\"prerend!\", 256)"] 1.17 (15%) ❌ 1.00 (1%)
["broadcast", "typeargs", "(\"tuple\", 10)"] 1.25 (15%) ❌ 1.00 (1%)
["collection", "queries & updates", "(\"Set\", \"String\", \"in\", \"true\")"] 1.29 (25%) ❌ 1.00 (1%)
["collection", "set operations", "(\"Set\", \"Int\", \"==\", \"self\")"] 0.70 (25%) ✅ 1.00 (1%)
["find", "findnext", "(\"Array{Bool,1}\", \"50-50\")"] 1.49 (15%) ❌ 1.00 (1%)
["find", "findnext", "(\"ispos\", \"Array{Float64,1}\")"] 0.81 (15%) ✅ 1.00 (1%)
["find", "findnext", "(\"ispos\", \"Array{Int64,1}\")"] 1.17 (15%) ❌ 1.00 (1%)
["find", "findprev", "(\"ispos\", \"Array{UInt8,1}\")"] 1.17 (15%) ❌ 1.00 (1%)
["linalg", "factorization", "(\"svd\", \"Diagonal\", 1024)"] 0.99 (45%) 0.85 (1%) ✅
["linalg", "factorization", "(\"svd\", \"Diagonal\", 256)"] 0.98 (45%) 0.85 (1%) ✅
["linalg", "factorization", "(\"svdfact\", \"Diagonal\", 1024)"] 0.99 (45%) 0.86 (1%) ✅
["linalg", "factorization", "(\"svdfact\", \"Diagonal\", 256)"] 0.97 (45%) 0.85 (1%) ✅
["micro", "fib"] 1.17 (15%) ❌ 1.00 (1%)
["micro", "parseint"] 1.24 (15%) ❌ 1.17 (1%) ❌
["random", "ranges", "(\"RangeGenerator\", \"BigInt\", \"1:170141183460469231731687303715884105728\")"] 1.33 (25%) ❌ 1.00 (1%)
["random", "ranges", "(\"RangeGenerator\", \"Int128\", \"1:4294967295\")"] 1.29 (25%) ❌ 1.00 (1%)
["random", "ranges", "(\"RangeGenerator\", \"Int128\", \"1:4294967297\")"] 1.26 (25%) ❌ 1.00 (1%)
["random", "ranges", "(\"rand\", \"MersenneTwister\", \"BigInt\", \"RangeGenerator(1:2^10000)\")"] 0.71 (25%) ✅ 1.00 (1%)
["random", "types", "(\"rand!\", \"MersenneTwister\", \"Complex{Float64}\")"] 1.53 (25%) ❌ 1.00 (1%)
["random", "types", "(\"rand!\", \"MersenneTwister\", \"Complex{Int128}\")"] 1.26 (25%) ❌ 1.00 (1%)
["random", "types", "(\"rand!\", \"MersenneTwister\", \"Complex{Int64}\")"] 1.38 (25%) ❌ 1.00 (1%)
["random", "types", "(\"rand!\", \"MersenneTwister\", \"Complex{UInt128}\")"] 1.25 (25%) ❌ 1.00 (1%)
["random", "types", "(\"rand!\", \"MersenneTwister\", \"Complex{UInt64}\")"] 1.38 (25%) ❌ 1.00 (1%)
["scalar", "acos", "(\"0.5 <= abs(x) < 1\", \"negative argument\", \"Float64\")"] 1.20 (15%) ❌ 1.00 (1%)
["scalar", "arithmetic", "(\"add\", \"Int64\", \"Complex{BigInt}\")"] 1.58 (50%) ❌ 1.00 (1%)
["scalar", "arithmetic", "(\"div\", \"Complex{BigFloat}\", \"Complex{BigInt}\")"] 0.98 (50%) 0.97 (1%) ✅
["scalar", "arithmetic", "(\"div\", \"Complex{BigFloat}\", \"Complex{Float32}\")"] 1.03 (50%) 0.97 (1%) ✅
["scalar", "arithmetic", "(\"div\", \"Complex{BigFloat}\", \"Complex{Float64}\")"] 1.04 (50%) 0.97 (1%) ✅
["scalar", "arithmetic", "(\"div\", \"Complex{BigFloat}\", \"Complex{Int64}\")"] 1.02 (50%) 0.97 (1%) ✅
["scalar", "arithmetic", "(\"div\", \"Complex{BigFloat}\", \"Complex{UInt64}\")"] 1.00 (50%) 0.97 (1%) ✅
["scalar", "arithmetic", "(\"div\", \"Complex{BigInt}\", \"Complex{BigFloat}\")"] 0.99 (50%) 0.97 (1%) ✅
["scalar", "arithmetic", "(\"div\", \"Complex{BigInt}\", \"Complex{Int64}\")"] 0.89 (50%) 0.97 (1%) ✅
["scalar", "arithmetic", "(\"div\", \"Complex{BigInt}\", \"Complex{UInt64}\")"] 0.88 (50%) 0.97 (1%) ✅
["scalar", "arithmetic", "(\"div\", \"Complex{Float32}\", \"Complex{BigFloat}\")"] 1.01 (50%) 0.97 (1%) ✅
["scalar", "arithmetic", "(\"div\", \"Complex{Float64}\", \"Complex{BigFloat}\")"] 0.77 (50%) 0.97 (1%) ✅
["scalar", "arithmetic", "(\"div\", \"Complex{Int64}\", \"Complex{BigFloat}\")"] 1.00 (50%) 0.97 (1%) ✅
["scalar", "arithmetic", "(\"div\", \"Complex{Int64}\", \"Complex{BigInt}\")"] 0.87 (50%) 0.97 (1%) ✅
["scalar", "arithmetic", "(\"div\", \"Complex{UInt64}\", \"Complex{BigFloat}\")"] 1.00 (50%) 0.97 (1%) ✅
["scalar", "arithmetic", "(\"div\", \"Complex{UInt64}\", \"Complex{BigInt}\")"] 0.87 (50%) 0.97 (1%) ✅
["scalar", "arithmetic", "(\"mul\", \"BigInt\", \"BigInt\")"] 1.56 (50%) ❌ 1.00 (1%)
["scalar", "asin", "(\"0.5 <= abs(x) < 0.975\", \"negative argument\", \"Float64\")"] 1.16 (15%) ❌ 1.00 (1%)
["scalar", "asin", "(\"0.5 <= abs(x) < 0.975\", \"positive argument\", \"Float64\")"] 1.25 (15%) ❌ 1.00 (1%)
["scalar", "asin", "(\"0.975 <= abs(x) < 1.0\", \"positive argument\", \"Float64\")"] 1.30 (15%) ❌ 1.00 (1%)
["scalar", "atan", "(\"11/16 <= abs(x) < 19/16\", \"positive argument\", \"Float64\")"] 1.25 (15%) ❌ 1.00 (1%)
["scalar", "atan", "(\"19/16 <= abs(x) < 39/16\", \"negative argument\", \"Float64\")"] 0.78 (15%) ✅ 1.00 (1%)
["scalar", "atan", "(\"19/16 <= abs(x) < 39/16\", \"positive argument\", \"Float64\")"] 0.78 (15%) ✅ 1.00 (1%)
["scalar", "atan2", "(\"abs(y/x) safe (small)\", \"y negative\", \"x positive\", \"Float32\")"] 1.22 (15%) ❌ 1.00 (1%)
["scalar", "atan2", "(\"abs(y/x) safe (small)\", \"y positive\", \"x negative\", \"Float32\")"] 0.79 (15%) ✅ 1.00 (1%)
["scalar", "cos", "(\"argument reduction (hard) abs(x) < 6π/4\", \"negative argument\", \"Float64\", \"sin_kernel\")"] 0.79 (15%) ✅ 1.00 (1%)
["scalar", "predicate", "(\"isequal\", \"Int64\")"] 0.64 (25%) ✅ 1.00 (1%)
["scalar", "predicate", "(\"isinteger\", \"Complex{BigFloat}\")"] 1.73 (40%) ❌ 1.00 (1%)
["scalar", "predicate", "(\"isless\", \"BigFloat\")"] 1.58 (40%) ❌ 1.00 (1%)
["scalar", "sincos", "(\"argument reduction (easy) abs(x) < 4π/4\", \"negative argument\", \"Float32\")"] 1.22 (15%) ❌ 1.00 (1%)
["scalar", "sincos", "(\"argument reduction (easy) abs(x) < 4π/4\", \"positive argument\", \"Float64\")"] 1.22 (15%) ❌ 1.00 (1%)
["scalar", "sincos", "(\"argument reduction (easy) abs(x) < 5π/4\", \"negative argument\", \"Float32\")"] 1.25 (15%) ❌ 1.00 (1%)
["scalar", "sincos", "(\"argument reduction (easy) abs(x) < 5π/4\", \"negative argument\", \"Float64\")"] 1.25 (15%) ❌ 1.00 (1%)
["scalar", "sincos", "(\"argument reduction (easy) abs(x) < 6π/4\", \"negative argument\", \"Float64\")"] 1.25 (15%) ❌ 1.00 (1%)
["scalar", "sincos", "(\"argument reduction (easy) abs(x) < 6π/4\", \"positive argument\", \"Float32\")"] 1.25 (15%) ❌ 1.00 (1%)
["scalar", "sincos", "(\"argument reduction (easy) abs(x) < 6π/4\", \"positive argument\", \"Float64\")"] 1.25 (15%) ❌ 1.00 (1%)
["scalar", "sincos", "(\"argument reduction (easy) abs(x) < 7π/4\", \"positive argument\", \"Float32\")"] 1.22 (15%) ❌ 1.00 (1%)
["scalar", "sincos", "(\"argument reduction (easy) abs(x) < 8π/4\", \"negative argument\", \"Float32\")"] 1.25 (15%) ❌ 1.00 (1%)
["scalar", "sincos", "(\"argument reduction (easy) abs(x) < 9π/4\", \"negative argument\", \"Float32\")"] 1.22 (15%) ❌ 1.00 (1%)
["scalar", "sincos", "(\"argument reduction (easy) abs(x) < 9π/4\", \"positive argument\", \"Float64\")"] 1.25 (15%) ❌ 1.00 (1%)
["scalar", "tan", "(\"large\", \"negative argument\", \"Float64\")"] 1.19 (15%) ❌ 1.00 (1%)
["scalar", "tan", "(\"large\", \"positive argument\", \"Float64\")"] 1.20 (15%) ❌ 1.00 (1%)
["scalar", "tan", "(\"medium\", \"negative argument\", \"Float64\")"] 1.21 (15%) ❌ 1.00 (1%)
["scalar", "tan", "(\"medium\", \"positive argument\", \"Float32\")"] 1.16 (15%) ❌ 1.00 (1%)
["scalar", "tan", "(\"medium\", \"positive argument\", \"Float64\")"] 1.21 (15%) ❌ 1.00 (1%)
["shootout", "meteor_contest"] 1.06 (15%) 1.02 (1%) ❌
["sparse", "index", "(\"spmat\", \"integer\", 1000)"] 1.35 (30%) ❌ 1.00 (1%)
["sparse", "matmul", "(\"A_mul_Bc!\", \"dense 2000x20, sparse 20x20 -> dense 2000x20\")"] 1.00 (30%) 0.88 (1%) ✅
["sparse", "matmul", "(\"A_mul_Bc!\", \"dense 200x20, sparse 200x20 -> dense 200x200\")"] 0.98 (30%) 0.88 (1%) ✅
["sparse", "matmul", "(\"A_mul_Bc!\", \"dense 200x200, sparse 200x200 -> dense 200x200\")"] 0.97 (30%) 0.88 (1%) ✅
["sparse", "matmul", "(\"A_mul_Bc!\", \"dense 200x200, sparse 20x200 -> dense 200x20\")"] 1.04 (30%) 0.88 (1%) ✅
["sparse", "matmul", "(\"A_mul_Bc!\", \"dense 20x20, sparse 2000x20 -> dense 20x2000\")"] 0.99 (30%) 0.88 (1%) ✅
["sparse", "matmul", "(\"A_mul_Bc!\", \"dense 20x200, sparse 2000x200 -> dense 20x2000\")"] 1.00 (30%) 0.88 (1%) ✅
["sparse", "matmul", "(\"A_mul_Bc!\", \"dense 20x2000, sparse 2000x2000 -> dense 20x2000\")"] 1.01 (30%) 0.88 (1%) ✅
["sparse", "matmul", "(\"A_mul_Bc!\", \"dense 20x2000, sparse 200x2000 -> dense 20x200\")"] 1.03 (30%) 0.88 (1%) ✅
["sparse", "matmul", "(\"A_mul_Bt!\", \"dense 4000x40, sparse 40x40 -> dense 4000x40\")"] 1.01 (30%) 0.88 (1%) ✅
["sparse", "matmul", "(\"A_mul_Bt!\", \"dense 400x40, sparse 400x40 -> dense 400x400\")"] 1.00 (30%) 0.88 (1%) ✅
["sparse", "matmul", "(\"A_mul_Bt!\", \"dense 400x400, sparse 400x400 -> dense 400x400\")"] 1.01 (30%) 0.88 (1%) ✅
["sparse", "matmul", "(\"A_mul_Bt!\", \"dense 400x400, sparse 40x400 -> dense 400x40\")"] 1.01 (30%) 0.88 (1%) ✅
["sparse", "matmul", "(\"A_mul_Bt!\", \"dense 40x40, sparse 4000x40 -> dense 40x4000\")"] 1.00 (30%) 0.88 (1%) ✅
["sparse", "matmul", "(\"A_mul_Bt!\", \"dense 40x400, sparse 4000x400 -> dense 40x4000\")"] 1.01 (30%) 0.88 (1%) ✅
["sparse", "matmul", "(\"A_mul_Bt!\", \"dense 40x4000, sparse 4000x4000 -> dense 40x4000\")"] 1.02 (30%) 0.88 (1%) ✅
["sparse", "matmul", "(\"A_mul_Bt!\", \"dense 40x4000, sparse 400x4000 -> dense 40x400\")"] 1.01 (30%) 0.88 (1%) ✅
["sparse", "matmul", "(\"Ac_mul_B!\", \"sparse 2000x200, dense 2000x20 -> dense 200x20\")"] 1.00 (30%) 0.25 (1%) ✅
["sparse", "matmul", "(\"Ac_mul_B!\", \"sparse 2000x2000, dense 2000x20 -> dense 2000x20\")"] 0.99 (30%) 0.25 (1%) ✅
["sparse", "matmul", "(\"Ac_mul_B!\", \"sparse 200x20, dense 200x200 -> dense 20x200\")"] 1.00 (30%) 0.25 (1%) ✅
["sparse", "matmul", "(\"Ac_mul_B!\", \"sparse 200x200, dense 200x200 -> dense 200x200\")"] 0.97 (30%) 0.25 (1%) ✅
["sparse", "matmul", "(\"Ac_mul_B!\", \"sparse 200x2000, dense 200x20 -> dense 2000x20\")"] 0.96 (30%) 0.25 (1%) ✅
["sparse", "matmul", "(\"Ac_mul_B!\", \"sparse 20x20, dense 20x2000 -> dense 20x2000\")"] 1.00 (30%) 0.25 (1%) ✅
["sparse", "matmul", "(\"Ac_mul_B!\", \"sparse 20x200, dense 20x200 -> dense 200x200\")"] 0.99 (30%) 0.25 (1%) ✅
["sparse", "matmul", "(\"Ac_mul_B!\", \"sparse 20x2000, dense 20x20 -> dense 2000x20\")"] 1.03 (30%) 0.25 (1%) ✅
["sparse", "matmul", "(\"Ac_mul_B\", \"sparse 500x50, dense 500x5 -> dense 50x5\")"] 0.99 (30%) 0.99 (1%) ✅
["sparse", "matmul", "(\"Ac_mul_B\", \"sparse 50x5, dense 50x50 -> dense 5x50\")"] 1.00 (30%) 0.99 (1%) ✅
["sparse", "matmul", "(\"Ac_mul_Bc!\", \"dense 2000x20, sparse 2000x2000 -> dense 20x2000\")"] 1.01 (30%) 0.88 (1%) ✅
["sparse", "matmul", "(\"Ac_mul_Bc!\", \"dense 2000x20, sparse 200x2000 -> dense 20x200\")"] 1.02 (30%) 0.88 (1%) ✅
["sparse", "matmul", "(\"Ac_mul_Bc!\", \"dense 200x20, sparse 2000x200 -> dense 20x2000\")"] 1.00 (30%) 0.88 (1%) ✅
["sparse", "matmul", "(\"Ac_mul_Bc!\", \"dense 200x200, sparse 200x200 -> dense 200x200\")"] 1.00 (30%) 0.88 (1%) ✅
["sparse", "matmul", "(\"Ac_mul_Bc!\", \"dense 200x200, sparse 20x200 -> dense 200x20\")"] 1.01 (30%) 0.88 (1%) ✅
["sparse", "matmul", "(\"Ac_mul_Bc!\", \"dense 20x20, sparse 2000x20 -> dense 20x2000\")"] 1.00 (30%) 0.88 (1%) ✅
["sparse", "matmul", "(\"Ac_mul_Bc!\", \"dense 20x200, sparse 200x20 -> dense 200x200\")"] 0.98 (30%) 0.88 (1%) ✅
["sparse", "matmul", "(\"Ac_mul_Bc!\", \"dense 20x2000, sparse 20x20 -> dense 2000x20\")"] 1.00 (30%) 0.88 (1%) ✅
["sparse", "matmul", "(\"Ac_mul_Bc!\", \"sparse 2000x200, dense 20x2000 -> dense 200x20\")"] 1.00 (30%) 0.88 (1%) ✅
["sparse", "matmul", "(\"Ac_mul_Bc!\", \"sparse 2000x2000, dense 20x2000 -> dense 2000x20\")"] 1.01 (30%) 0.88 (1%) ✅
["sparse", "matmul", "(\"Ac_mul_Bc!\", \"sparse 200x20, dense 200x200 -> dense 20x200\")"] 1.00 (30%) 0.88 (1%) ✅
["sparse", "matmul", "(\"Ac_mul_Bc!\", \"sparse 200x200, dense 200x200 -> dense 200x200\")"] 1.00 (30%) 0.88 (1%) ✅
["sparse", "matmul", "(\"Ac_mul_Bc!\", \"sparse 200x2000, dense 20x200 -> dense 2000x20\")"] 1.01 (30%) 0.88 (1%) ✅
["sparse", "matmul", "(\"Ac_mul_Bc!\", \"sparse 20x20, dense 2000x20 -> dense 20x2000\")"] 1.00 (30%) 0.88 (1%) ✅
["sparse", "matmul", "(\"Ac_mul_Bc!\", \"sparse 20x200, dense 200x20 -> dense 200x200\")"] 1.00 (30%) 0.88 (1%) ✅
["sparse", "matmul", "(\"Ac_mul_Bc!\", \"sparse 20x2000, dense 20x20 -> dense 2000x20\")"] 1.01 (30%) 0.88 (1%) ✅
["sparse", "matmul", "(\"Ac_mul_Bc\", \"sparse 500x50, dense 5x500 -> dense 50x5\")"] 1.00 (30%) 0.98 (1%) ✅
["sparse", "matmul", "(\"Ac_mul_Bc\", \"sparse 50x5, dense 50x50 -> dense 5x50\")"] 1.00 (30%) 0.98 (1%) ✅
["sparse", "matmul", "(\"At_mul_B!\", \"sparse 4000x400, dense 4000x40 -> dense 400x40\")"] 1.00 (30%) 0.25 (1%) ✅
["sparse", "matmul", "(\"At_mul_B!\", \"sparse 4000x4000, dense 4000x40 -> dense 4000x40\")"] 1.00 (30%) 0.25 (1%) ✅
["sparse", "matmul", "(\"At_mul_B!\", \"sparse 400x40, dense 400x400 -> dense 40x400\")"] 1.00 (30%) 0.25 (1%) ✅
["sparse", "matmul", "(\"At_mul_B!\", \"sparse 400x400, dense 400x400 -> dense 400x400\")"] 1.03 (30%) 0.25 (1%) ✅
["sparse", "matmul", "(\"At_mul_B!\", \"sparse 400x4000, dense 400x40 -> dense 4000x40\")"] 0.79 (30%) 0.25 (1%) ✅
["sparse", "matmul", "(\"At_mul_B!\", \"sparse 40x40, dense 40x4000 -> dense 40x4000\")"] 1.00 (30%) 0.25 (1%) ✅
["sparse", "matmul", "(\"At_mul_B!\", \"sparse 40x400, dense 40x400 -> dense 400x400\")"] 0.99 (30%) 0.25 (1%) ✅
["sparse", "matmul", "(\"At_mul_B!\", \"sparse 40x4000, dense 40x40 -> dense 4000x40\")"] 1.02 (30%) 0.25 (1%) ✅
["sparse", "matmul", "(\"At_mul_B\", \"sparse 500x50, dense 500x5 -> dense 50x5\")"] 1.00 (30%) 0.99 (1%) ✅
["sparse", "matmul", "(\"At_mul_B\", \"sparse 50x5, dense 50x50 -> dense 5x50\")"] 1.00 (30%) 0.99 (1%) ✅
["sparse", "matmul", "(\"At_mul_Bt!\", \"dense 4000x40, sparse 4000x4000 -> dense 40x4000\")"] 0.96 (30%) 0.88 (1%) ✅
["sparse", "matmul", "(\"At_mul_Bt!\", \"dense 4000x40, sparse 400x4000 -> dense 40x400\")"] 1.01 (30%) 0.88 (1%) ✅
["sparse", "matmul", "(\"At_mul_Bt!\", \"dense 400x40, sparse 4000x400 -> dense 40x4000\")"] 1.01 (30%) 0.88 (1%) ✅
["sparse", "matmul", "(\"At_mul_Bt!\", \"dense 400x400, sparse 400x400 -> dense 400x400\")"] 1.01 (30%) 0.88 (1%) ✅
["sparse", "matmul", "(\"At_mul_Bt!\", \"dense 400x400, sparse 40x400 -> dense 400x40\")"] 1.01 (30%) 0.88 (1%) ✅
["sparse", "matmul", "(\"At_mul_Bt!\", \"dense 40x40, sparse 4000x40 -> dense 40x4000\")"] 1.00 (30%) 0.88 (1%) ✅
["sparse", "matmul", "(\"At_mul_Bt!\", \"dense 40x400, sparse 400x40 -> dense 400x400\")"] 1.00 (30%) 0.88 (1%) ✅
["sparse", "matmul", "(\"At_mul_Bt!\", \"dense 40x4000, sparse 40x40 -> dense 4000x40\")"] 1.01 (30%) 0.88 (1%) ✅
["sparse", "matmul", "(\"At_mul_Bt!\", \"sparse 4000x400, dense 40x4000 -> dense 400x40\")"] 1.00 (30%) 0.88 (1%) ✅
["sparse", "matmul", "(\"At_mul_Bt!\", \"sparse 4000x4000, dense 40x4000 -> dense 4000x40\")"] 1.01 (30%) 0.88 (1%) ✅
["sparse", "matmul", "(\"At_mul_Bt!\", \"sparse 400x40, dense 400x400 -> dense 40x400\")"] 1.00 (30%) 0.88 (1%) ✅
["sparse", "matmul", "(\"At_mul_Bt!\", \"sparse 400x400, dense 400x400 -> dense 400x400\")"] 1.00 (30%) 0.88 (1%) ✅
["sparse", "matmul", "(\"At_mul_Bt!\", \"sparse 400x4000, dense 40x400 -> dense 4000x40\")"] 1.01 (30%) 0.88 (1%) ✅
["sparse", "matmul", "(\"At_mul_Bt!\", \"sparse 40x40, dense 4000x40 -> dense 40x4000\")"] 1.00 (30%) 0.88 (1%) ✅
["sparse", "matmul", "(\"At_mul_Bt!\", \"sparse 40x400, dense 400x40 -> dense 400x400\")"] 1.01 (30%) 0.88 (1%) ✅
["sparse", "matmul", "(\"At_mul_Bt!\", \"sparse 40x4000, dense 40x40 -> dense 4000x40\")"] 1.01 (30%) 0.88 (1%) ✅
["sparse", "matmul", "(\"At_mul_Bt\", \"sparse 500x50, dense 5x500 -> dense 50x5\")"] 1.00 (30%) 0.98 (1%) ✅
["sparse", "matmul", "(\"At_mul_Bt\", \"sparse 50x5, dense 50x50 -> dense 5x50\")"] 1.00 (30%) 0.98 (1%) ✅
["tuple", "reduction", "(\"minimum\", (4, 4))"] 0.81 (15%) ✅ 1.00 (1%)
["union", "array", "(\"perf_countnothing\", \"Array{Union{Nothing, Int8},1}\")"] 1.17 (15%) ❌ 1.00 (1%)

Benchmark Group List

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

  • ["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", "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"]
  • ["find", "findnext"]
  • ["find", "findprev"]
  • ["io", "read"]
  • ["io", "serialization"]
  • ["linalg", "arithmetic"]
  • ["linalg", "blas"]
  • ["linalg", "factorization"]
  • ["micro"]
  • ["misc", "afoldl"]
  • ["misc", "bitshift"]
  • ["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", "cos"]
  • ["scalar", "cosh"]
  • ["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", "transpose"]
  • ["string"]
  • ["string", "readuntil"]
  • ["string", "search"]
  • ["string", "searchindex"]
  • ["tuple", "index"]
  • ["tuple", "linear algebra"]
  • ["tuple", "reduction"]
  • ["union", "array"]

Version Info

Primary Build

Julia Version 0.7.0-DEV.3158
Commit 5fafb36 (2017-12-21 22:04 UTC)
Platform Info:
  OS: Linux (x86_64-linux-gnu)
      Ubuntu 14.04.4 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  109760688 s          0 s   19324214 s  4813684872 s        100 s
       #2  3501 MHz  463642743 s          0 s   12284139 s  4478438406 s         23 s
       #3  3501 MHz   91826000 s          0 s   10491071 s  4852537199 s         84 s
       #4  3501 MHz   87870001 s          0 s   10679633 s  4856299002 s         21 s
       
  Memory: 31.383651733398438 GB (3636.33203125 MB free)
  Uptime: 4.9572192e7 sec
  Load Avg:  1.0029296875  1.0146484375  1.04541015625
  WORD_SIZE: 64
  BLAS: libopenblas (USE64BITINT DYNAMIC_ARCH NO_AFFINITY Haswell)
  LAPACK: libopenblas64_
  LIBM: libopenlibm
  LLVM: libLLVM-3.9.1 (ORCJIT, haswell)