mauro3

NEW

m = vs[3]; =====================================

Integer indexing:                           elapsed time: 0.004826669 seconds (96088 bytes allocated)
 
#### indexing with ranges
Whole column-wise indexing:                 elapsed time: 0.001638723 seconds (6527288 bytes allocated)
Column-wise indexing with a range of rows:  elapsed time: 0.003093329 seconds (123832 bytes allocated)
Whole row-wise indexing:                    elapsed time: 6.059387827 seconds (32650872 bytes allocated)

mauro3

# Binary and linear search are about equally fast for a haystack of
# size ~38 (Array{I<:Integer}).  For Array{Float64} it's about size ~15.
# For arrays of pointers binsearch probably is always faster.
#
# For Array(Int}
# For n=1 to 9, linear search takes about 50% of binary search
# 
# For n=10 to 20, linear search takes about 75% of binary search
#
# Now, this chap finds that in C linear search is faster up to array size ~ 64

mauro3

julia,sparse_getindex_small1,0.451012,0.483519,0.461447,0.012916
julia,sparse_getindex_small2,0.686494,0.718988,0.700459,0.016626
julia,sparse_getindex_medium1,0.783230,0.841894,0.803835,0.022526
julia,sparse_getindex_medium2,1.246590,1.276114,1.262807,0.011405
julia,sparse_getindex_small3,1.742440,9.295122,4.759420,3.807503
julia,sparse_getindex_small4,1.607372,8.230405,2.954001,2.949752
julia,sparse_getindex_small5,1.829975,1.870861,1.846175,0.018316
julia,sparse_getindex_small6,0.632674,0.673803,0.642136,0.017769
julia,sparse_getindex_small7,0.594250,0.604567,0.597951,0.004128
julia,sparse_getindex_small8,0.596583,0.607086,0.598952,0.004554

mauro3

using Base.Order
using Base.SparseMatrix.binarysearch

const N = 10^2
const NN = N*10^2
const rowvalA = sort(randperm(NN)[1:N]) 
const iss = randperm(NN) # disordered needle
const iso = [1:NN]       # ordered needles

function h1(rowvalA, iss)

mauro3

sparse_getindex_small1,Small sparse matrix with 10 entries/column, indexing with integers,0.448931,0.468407,0.454377,0.007985
sparse_getindex_small2,Small sparse matrix with 100 entries/column, indexing with integers,0.651765,0.696139,0.668417,0.016734
sparse_getindex_medium1,Medium sparse matrix with 10 entries/column, indexing with integers,0.795296,0.856736,0.811085,0.025680
sparse_getindex_medium2,Medium sparse matrix with 100 entries/column, indexing with integers,1.253497,1.260646,1.256850,0.003009
sparse_getindex_small3,Sparse matrix with 10 entries/column, indexing rows with a range,1.731063,9.425001,4.829933,3.849493
sparse_getindex_small4,Sparse matrix with 10 entries/column, indexing rows with a ordered array,1.619907,8.389759,2.987851,3.019779
sparse_getindex_small5,Sparse matrix with 10 entries/column, indexing rows with a disordered array,1.837361,1.860880,1.850504,0.009153
sparse_getindex_small6,Sparse matrix with 10 entries/column, indexing rows with a range,0.616219,0.638256,0.621277,0.009504

mauro3

# This performance regression seems to be fixed now.  Maybe one of the many commits of @vtjnash?
#
# sandbox/performance_regressions>> julia sg.jl # 664cab522c0
# elapsed time: 0.268316893 seconds (14911976 bytes allocated)
# elapsed time: 0.267490744 seconds (14837704 bytes allocated)
# elapsed time: 0.275272316 seconds (14793688 bytes allocated, 3.10% gc time)
# sandbox/performance_regressions>> juliamf sg.jl # around 66cfa99913
# elapsed time: 0.413370651 seconds (14868712 bytes allocated)
# elapsed time: 0.41186328 seconds (14828776 bytes allocated)
# elapsed time: 0.427139988 seconds (14916648 bytes allocated, 1.98% gc time)

mauro3

n = 200
reps = 10^4
orderedinds = [1:n]
disorderedinds = orderedinds[randperm(n)]

function frows()
    acc = 0
    inds = disorderedinds
    for i in rand(1:n, reps)
        acc += sortperm(inds)[1]

mauro3

module LoremIpsum
doc"""
LoremIpsum: dolor sit amet, consectetur adipisicing elit, sed do eiusmod
tempor incididunt ut labore et dolore magna aliqua.
 
Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi
ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit
in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur
sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt
mollit anim id est laborum.

mauro3

# How to document functions vs methods?

function plot(x::Vector, y::Vector)
    doc"""
    Line-plot of x vs y.
    """
    ...
end

function plot(a::Matrix)

mauro3

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