README

install.packages("data.table.threads")

if(!require(remotes)) install.packages("remotes"); remotes::install_github("Anirban166/data.table.threads")

if(!require(devtools)) install.packages("devtools"); devtools::install_github("Anirban166/data.table.threads")

Usage

findOptimalThreadCount(rowCount, columnCount, ...) is the go-to function that runs a set of predefined benchmarks for various data.table functions that are parallelizable, across varying thread counts (iteratively from one to the highest number available as per the user’s system). It involves computation to find the optimal/ideal speedup and thread count for each function. It returns a data.table object of a custom class (print and plot methods have been provided), which contains the optimal thread count for each function. It also provides plot data (consisting of speedup trends and key points) as attributes.

> benchmarks <- data.table.threads::findOptimalThreadCount(1e7, 10, verbose = TRUE)
Running benchmarks with 1 thread, 10000000 rows, and 10 columns.
...
Running benchmarks with 10 threads, 10000000 rows, and 10 columns.

It returns a data.table object for which print and plot methods have been provided.

> benchmarks
data.table function  Thread count Fastest median runtime (ms)
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
forder               8            99.320183
GForce_sum           5            15.709294
subsetting           6            57.685606
frollmean            9            23.233573
fcoalesce            9            7.332542
between              8            21.947874
fifelse              5            19.284555
nafill               6            7.316102
CJ                   2            3.658697

The output here is a table which shows the fastest runtime (median value in milliseconds) for each applicable data.table function along with the corresponding thread count that achieved it.

Plotting this object would generate a plot that shows the ideal and measured speedup trends for each routine:

> plot(benchmarkData)

If the user wants to factor in a specified speedup efficiency, they can use the function addRecommendedEfficiency to add a speedup line (with a slope configured by input argument efficiencyFactor; default value is 0.5, or 50% efficiency) along with a point representing the recommended thread count which stems from the highest intersection between this line (of specified thread-use efficiency) and measured speedup data for each function:

benchmarks_r <- addRecommendedEfficiency(benchmarks, recommendedEfficiency = 0.4)
plot(benchmarks_r)

In both cases (with our without the addition of recommended efficiency), the generated plot delineates the speedup across multiple threads (from 1 to the number of threads available in the user’s system; 10 in my case here) for each function.

setThreadCount(benchmarks, functionName, efficiencyFactor) can then be used to set the thread count based on the observed results for a user-specified function and efficiency value (of the range [0, 1]) for the speedup:

> setThreadCount(benchmarks_r, functionName = "forder", efficiencyFactor = 0.6, verbose = TRUE)
The number of threads that data.table will use has been set to 3, based on an efficiency factor of 0.6 for data.table::forder() based on the performed benchmarks.
> getDTthreads()
[1] 3

Installation

Usage