R: Function for measuring algorithmic performance of high-level...

benchmark64 {bit64} | R Documentation

## Function for measuring algorithmic performance
of high-level and low-level integer64 functions

### Description

`benchmark64` compares high-level integer64 functions against the integer functions from Base R
`optimizer64` compares for each high-level integer64 function the Base R integer function with several low-level integer64 functions with and without caching

### Usage

    benchmark64(nsmall = 2^16, nbig = 2^25, timefun = repeat.time
    )
    optimizer64(nsmall = 2^16, nbig = 2^25, timefun = repeat.time
    , what = c("match", "%in%", "duplicated", "unique", "unipos", "table", "rank", "quantile")
    , uniorder = c("original", "values", "any")
    , taborder = c("values", "counts")
    , plot = TRUE
    )

### Arguments

`nsmall` | size of smaller vector
`nbig` | size of larger bigger vector
`timefun` | a function for timing such as `repeat.time` or `system.time`
`what` | a vector of names of high-level functions
`uniorder` | one of the order parameters that are allowed in `unique.integer64` and `unipos.integer64`
`taborder` | one of the order parameters that are allowed in `table.integer64`
`plot` | set to FALSE to suppress plotting

### Details

`benchmark64` compares the following scenarios for the following use cases:

scenario name | explanation
32-bit | applying Base R function to 32-bit integer data
64-bit | applying bit64 function to 64-bit integer data (with no cache)
hashcache | dito when cache contains `hashmap`, see `hashcache`
sortordercache | dito when cache contains sorting and ordering, see `sortordercache`
ordercache | dito when cache contains ordering only, see `ordercache`
allcache | dito when cache contains sorting, ordering and hashing

use case name | explanation
cache | filling the cache according to scenario
match(s,b) | match small in big vector
s %in% b | small %in% big vector
match(b,s) | match big in small vector
b %in% s | big %in% small vector
match(b,b) | match big in (different) big vector
b %in% b | big %in% (different) big vector
duplicated(b) | duplicated of big vector
unique(b) | unique of big vector
table(b) | table of big vector
sort(b) | sorting of big vector
order(b) | ordering of big vector
rank(b) | ranking of big vector
quantile(b) | quantiles of big vector
summary(b) | summary of of big vector
SESSION | exemplary session involving multiple calls (including cache filling costs)

Note that the timings for the cached variants do not contain the time costs of building the cache, except for the timing of the exemplary user session, where the cache costs are included in order to evaluate amortization.

### Value

`benchmark64` returns a matrix with elapsed seconds, different high-level tasks in rows and different scenarios to solve the task in columns. The last row named 'SESSION' contains the elapsed seconds of the exemplary sesssion.
`optimizer64` returns a dimensioned list with one row for each high-level function timed and two columns named after the values of the `nsmall` and `nbig` sample sizes. Each list cell contains a matrix with timings, low-level-methods in rows and three measurements `c("prep","both","use")` in columns. If it can be measured separately, `prep` contains the timing of preparatory work such as sorting and hashing, and `use` contains the timing of using the prepared work. If the function timed does both, preparation and use, the timing is in `both`.

### Author(s)

Jens Oehlschlägel <Jens.Oehlschlaegel@truecluster.com>

### See Also

`integer64`

### Examples

    message("this small example using system.time does not give serious timings\n
    this we do this only to run regression tests")
    benchmark64(nsmall=2^7, nbig=2^13, timefun=function(expr)system.time(expr, gcFirst=FALSE))
    optimizer64(nsmall=2^7, nbig=2^13, timefun=function(expr)system.time(expr, gcFirst=FALSE)
    , plot=FALSE
    )
    ## Not run:
    message("for real measurement of sufficiently large datasets run this on your machine")
    benchmark64()
    optimizer64()

    ## End(Not run)
    message("let's look at the performance results on Core i7 Lenovo T410 with 8 GB RAM")
    data(benchmark64.data)
    print(benchmark64.data)

    matplot(log2(benchmark64.data[-1,1]/benchmark64.data[-1,])
    , pch=c("3", "6", "h", "s", "o", "a")
    , xlab="tasks [last=session]"
    , ylab="log2(relative speed) [bigger is better]"
    )
    matplot(t(log2(benchmark64.data[-1,1]/benchmark64.data[-1,]))
    , type="b", axes=FALSE
    , lwd=c(rep(1, 14), 3)
    , xlab="context"
    , ylab="log2(relative speed) [bigger is better]"
    )
    axis(1
    , labels=c("32-bit", "64-bit", "hash", "sortorder", "order", "hash+sortorder")
    , at=1:6
    )
    axis(2)
    data(optimizer64.data)
    print(optimizer64.data)
    oldpar <- par(no.readonly = TRUE)
    par(mfrow=c(2,1))
    par(cex=0.7)
    for (i in 1:nrow(optimizer64.data)){
     for (j in 1:2){
       tim <- optimizer64.data[[i,j]]
      barplot(t(tim))
      if (rownames(optimizer64.data)[i]=="match")
       title(paste("match", colnames(optimizer64.data)[j], "in", colnames(optimizer64.data)[3-j]))
      else if (rownames(optimizer64.data)[i]=="%in%")
       title(paste(colnames(optimizer64.data)[j], "%in%", colnames(optimizer64.data)[3-j]))
      else
       title(paste(rownames(optimizer64.data)[i], colnames(optimizer64.data)[j]))
     }
    }
    par(mfrow=c(1,1))

[Package bit64 version 4.0.2 Index]