Type:	Package
Title:	Crowd Sourced System Benchmarks
Version:	1.0.8
Maintainer:	Colin Gillespie <csgillespie@gmail.com>
Description:	Benchmark your CPU and compare against other CPUs. Also provides functions for obtaining system specifications, such as RAM, CPU type, and R version.
License:	GPL-2 | GPL-3
URL:	https://github.com/csgillespie/benchmarkme
BugReports:	https://github.com/csgillespie/benchmarkme/issues
Depends:	R (≥ 3.5.0)
Imports:	benchmarkmeData (≥ 1.0.4), compiler, doParallel, dplyr, foreach, graphics, httr, Matrix, methods, parallel, stringr, tibble, utils
Suggests:	covr, DT, ggplot2, knitr, RcppZiggurat, rmarkdown, testthat
VignetteBuilder:	knitr
Encoding:	UTF-8
LazyData:	TRUE
RoxygenNote:	7.1.2
NeedsCompilation:	no
Packaged:	2022-06-12 14:52:28 UTC; ncsg3
Author:	Colin Gillespie [aut, cre]
Repository:	CRAN
Date/Publication:	2022-06-12 15:50:16 UTC

The benchmarkme package

Description

Benchmark your CPU and compare against other CPUs. Also provides functions for obtaining system specifications, such as RAM, CPU type, and R version.

Author(s)

csgillespie@gmail.com

See Also

https://github.com/csgillespie/benchmarkme

Examples

## Benchmark your system and compare
## Not run: 
res = benchmark_std()
upload_results(res)
plot(res)

## End(Not run)

IO benchmarks

Description

Benchmarking reading and writing a csv file (containing random numbers). The tests are essentially write.csv(x) and read.csv(...) where x is a data frame. Of sizeMB.

Usage

benchmark_io(
  runs = 3,
  size = c(5, 50),
  tmpdir = tempdir(),
  verbose = TRUE,
  cores = 0L
)

bm_read(runs = 3, size = c(5, 50), tmpdir = tempdir(), verbose = TRUE)

bm_write(runs = 3, size = c(5, 50), tmpdir = tempdir(), verbose = TRUE)

Arguments

Run standard benchmarks

Description

This function runs a set of standard benchmarks, which should be suitable for most machines. It runs a collection of matrix benchmark functions

• benchmark_prog

• benchmark_matrix_cal

• benchmark_matrix_fun

To view the list of benchmarks, see get_available_benchmarks.

Usage

benchmark_std(runs = 3, verbose = TRUE, cores = 0L)

Arguments

Details

Setting cores equal to 1 is useful for assessing the impact of the parallel computing overhead.

Examples

## Benchmark your system
## Not run: 
res = benchmark_std(3)

## Plot results
plot(res)

## End(Not run)

Matrix calculation benchmarks

Description

A collection of matrix benchmark functions aimed at assessing the calculation speed.

• Creation, transp., deformation of a 2500x2500 matrix.

• 2500x2500 normal distributed random matrix ^1000.

• Sorting of 7,000,000 random values.

• 2500x2500 cross-product matrix (b = a' * a)

• Linear regr. over a 3000x3000 matrix.

These benchmarks have been developed by many authors. See http://r.research.att.com/benchmarks/R-benchmark-25.R for a complete history. The function benchmark_matrix_cal() runs the five bm functions.

Usage

bm_matrix_cal_manip(runs = 3, verbose = TRUE)

bm_matrix_cal_power(runs = 3, verbose = TRUE)

bm_matrix_cal_sort(runs = 3, verbose = TRUE)

bm_matrix_cal_cross_product(runs = 3, verbose = TRUE)

bm_matrix_cal_lm(runs = 3, verbose = TRUE)

benchmark_matrix_cal(runs = 3, verbose = TRUE, cores = 0L)

Arguments

References

http://r.research.att.com/benchmarks/R-benchmark-25.R

Matrix function benchmarks

Description

A collection of matrix benchmark functions

• FFT over 2,500,000 random values.

• Eigenvalues of a 640x640 random matrix.

• Determinant of a 2500x2500 random matrix.

• Cholesky decomposition of a 3000x3000 matrix.

• Inverse of a 1600x1600 random matrix.

These benchmarks have been developed by many authors. See http://r.research.att.com/benchmarks/R-benchmark-25.R for a complete history. The function benchmark_matrix_fun() runs the five bm functions.

Usage

bm_matrix_fun_fft(runs = 3, verbose = TRUE)

bm_matrix_fun_eigen(runs = 3, verbose = TRUE)

bm_matrix_fun_determinant(runs = 3, verbose = TRUE)

bm_matrix_fun_cholesky(runs = 3, verbose = TRUE)

bm_matrix_fun_inverse(runs = 3, verbose = TRUE)

benchmark_matrix_fun(runs = 3, verbose = TRUE, cores = 0L)

Arguments

References

http://r.research.att.com/benchmarks/R-benchmark-25.R

Benchmark in parallel

Description

This function runs benchmarks in parallel to test multithreading

Usage

bm_parallel(bm, runs, verbose, cores, ...)

Arguments

Examples

## Not run: 
bm_parallel("bm_matrix_cal_manip", runs = 3, verbose = TRUE, cores = 2)
bm = c("bm_matrix_cal_manip","bm_matrix_cal_power", "bm_matrix_cal_sort",
       "bm_matrix_cal_cross_product", "bm_matrix_cal_lm")
results = lapply(bm, bm_parallel,
                runs = 5, verbose = TRUE, cores = 2L)

## End(Not run)

Programming benchmarks

Description

A collection of matrix programming benchmark functions

• 3,500,000 Fibonacci numbers calculation (vector calc).

• Creation of a 3500x3500 Hilbert matrix (matrix calc).

• Grand common divisors of 1,000,000 pairs (recursion).

• Creation of a 1600x1600 Toeplitz matrix (loops).

• Escoufier's method on a 60x60 matrix (mixed).

These benchmarks have been developed by many authors. See http://r.research.att.com/benchmarks/R-benchmark-25.R for a complete history. The function benchmark_prog() runs the five bm functions.

Usage

bm_prog_fib(runs = 3, verbose = TRUE)

bm_prog_hilbert(runs = 3, verbose = TRUE)

bm_prog_gcd(runs = 3, verbose = TRUE)

bm_prog_toeplitz(runs = 3, verbose = TRUE)

bm_prog_escoufier(runs = 3, verbose = TRUE)

benchmark_prog(runs = 3, verbose = TRUE, cores = 0L)

Arguments

Upload benchmark results

Description

This function uploads the benchmarking results. These results will then be incorparated in future versions of the package.

Usage

create_bundle(results, filename = NULL, args = NULL, id_prefix = "")

upload_results(
  results,
  url = "http://www.mas.ncl.ac.uk/~ncsg3/form.php",
  args = NULL,
  id_prefix = ""
)

Arguments

Examples

## Run benchmarks
## Not run: 
res = benchmark_std()
upload_results(res)

## End(Not run)

Available benchmarks

Description

The function returns the available benchmarks

Usage

get_available_benchmarks()

Examples

get_available_benchmarks()

Byte compiler status

Description

Attempts to detect if byte compiling or JIT has been used on the package.

Usage

get_byte_compiler()

Details

For R 3.5.0 all packages are byte compiled. Before 3.5.0 it was messy. Sometimes the user would turn it on via JIT, or ByteCompiling the package. On top of that R 3.4.X(?) was byte compiled, but R 3.4.Y(?) was, not fully optimised!!! What this means is don't trust historical results!

Value

An integer indicating if byte compiling has been turn on. See ?compiler for details.

Examples

## Detect if you use byte optimization
get_byte_compiler()

CPU Description

Description

Attempt to extract the CPU model on the current host. This is OS specific:

• Linux: /proc/cpuinfo

• Apple: sysctl -n

• Solaris: Not implemented.

• Windows: wmic cpu

A value of NA is return if it isn't possible to obtain the CPU.

Usage

get_cpu()

Examples

## Return the machine CPU
get_cpu()

Get BLAS and LAPACK libraries Extract the the blas/lapack from sessionInfo()

Description

Get BLAS and LAPACK libraries Extract the the blas/lapack from sessionInfo()

Usage

get_linear_algebra()

Platform information

Description

This function just returns the outpu of .Platform

Usage

get_platform_info()

R version

Description

Returns unclass(R.version)

Usage

get_r_version()

Get the amount of RAM

Description

Attempt to extract the amount of RAM on the current machine. This is OS specific:

• Linux: proc/meminfo

• Apple: system_profiler -detailLevel mini

• Windows: First tries grep MemTotal /proc/meminfo then falls back to wmic MemoryChip get Capacity

• Solaris: prtconf

A value of NA is return if it isn't possible to determine the amount of RAM.

Usage

get_ram()

References

The print.bytes function was taken from the pryr package.

Examples

## Return (and pretty print) the amount of RAM
get_ram()
## Display using iec units
print(get_ram(), unit_system = "iec")

General system information

Description

The get_sys_info returns general system level information as a list. The function parameters control the information to upload. If a parameter is set to FALSE, an NA is uploaded instead. Each element of the list is contains the output from:

• Sys.info();

• get_platform_info();

• get_r_version();

• get_ram();

• get_cpu();

• get_byte_compiler();

• get_linear_algebra();

• Sys.getlocale()

• installed.packages();

• .Machine

• The package version number;

• Unique ID - used to extract results;

• The current date.

Usage

get_sys_details(
  sys_info = TRUE,
  platform_info = TRUE,
  r_version = TRUE,
  ram = TRUE,
  cpu = TRUE,
  byte_compiler = TRUE,
  linear_algebra = TRUE,
  locale = TRUE,
  installed_packages = TRUE,
  machine = TRUE
)

Arguments

Value

A list

Examples

## Returns all details about your machine
get_sys_details(cpu = FALSE, installed_packages = FALSE, ram = FALSE)

Compare results to past tests

Description

Plotting

Usage

## S3 method for class 'ben_results'
plot(
  x,
  test_group = unique(x$test_group),
  blas_optimize = is_blas_optimize(x),
  log = "y",
  ...
)

Arguments

Examples

data(sample_results)
plot(sample_results, blas_optimize = NULL)

Benchmark rankings

Description

Comparison with past results.

Usage

rank_results(
  results,
  blas_optimize = is_blas_optimize(results),
  verbose = TRUE
)

Arguments

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

benchmarkmeData

is_blas_optimize, plot_past

Sample benchmarking results

Description

Sample benchmark results. Used in the vignette.

Format

A data frame