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 OehlschlĂ¤gel <Jens.Oehlschlaegel@truecluster.com>

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")
, 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)
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 4.0.5 Index]