System benchmarking

R benchmarking made easy. The package contains a number of benchmarks, heavily based on the benchmarks at http://r.research.att.com/benchmarks/R-benchmark-25.R, for assessing the speed of your system.

Overview

A straightforward way of speeding up your analysis is to buy a better computer. Modern desktops are relatively cheap, especially compared to user time. However, it isn't clear if upgrading your computing is worth the cost. The benchmarkme package provides a set of benchmarks to help quantify your system. More importantly, it allows you to compare your timings with other systems.

You can view past benchmarks via the Shiny interface.

Overview

The package is on CRAN and can be installed in the usual way

install.packages("benchmarkme")

There are two groups of benchmarks:

• benchmark_std(): this benchmarks numerical operations such as loops and matrix operations. The benchmark comprises of three separate benchmarks: prog, matrix_fun, and matrix_cal.
• benchmark_io(): this benchmarks reading and writing a 5, 50, and 200 MB csv file.

The benchmark_std() function

This benchmarks numerical operations such as loops and matrix operations. This benchmark comprises of three separate benchmarks: prog, matrix_fun, and matrix_cal. If you have less than 3GB of RAM (run get_ram() to find out how much is available on your system), then you should kill any memory hungry applications, e.g. firefox, and set runs = 1 as an argument.

To benchmark your system, use

library("benchmarkme")
## Increase runs if you have a higher spec machine
res = benchmark_std(runs = 3)

and upload your results

## You can control exactly what is uploaded. See details below.
upload_results(res)

You can compare your results to other users via

plot(res)

You can also compare your results using the Shiny interface. Simply create a results bundle

create_bundle(res, filename = "results.rds")

and upload to the webpage.

The benchmark_io() function

This function benchmarks reading and writing a 5MB, 50MB and 200MB (if you have less than 4GB of RAM, reduce the number of runs to 1). Run the benchmark using

res_io = benchmark_std(runs = 3)
upload_results(res_io)
plot(res_io)

By default the files are written to a temporary directory generated

tempdir()

which depends on the value of

Sys.getenv("TMPDIR")

You can alter this to via the tmpdir argument. This is useful for comparing hard drive access to a network drive.

res_io = benchmark_io(tmpdir = "some_other_directory")

Machine specs

The package has a few useful functions for extracting system specs:

• RAM: get_ram()
• CPUs: get_cpu()
• BLAS library: get_linear_algebra()
• Is byte compiling enabled: get_byte_compiler()
• General platform info: get_platform_info()
• R version: get_r_version()

The above functions have been tested on a number of systems. If they don't work on your system, please raise GitHub issue.

Uploaded data sets

A summary of the uploaded data sets is available in the benchmarkmeData package

data(past_results, package = "benchmarkmeData")

A column of this data set, contains the unique identifier returned by the upload_results function. A complete version of the uploaded data sets will be made available (soon) in a companion package.

What's uploaded

Two objects are uploaded:

1. Your benchmarks from benchmark_std or benchmark_io;
2. A summary of your system information (get_sys_details()).

The get_sys_details() returns:

• Sys.info();
• get_platform_info();
• get_r_version();
• get_ram();
• get_cpu();
• get_byte_compiler();
• get_linear_algebra();
• installed.packages();
• Sys.getlocale();
• The benchmarkme version number;
• Unique ID - used to extract results;
• The current date.

The function Sys.info() does include the user and nodenames. In the public release of the data, this information will be removed. If you don't wish to upload certain information, just set the corresponding argument, i.e.

upload_results(res, args = list(sys_info=FALSE))