bench

MATLAB benchmark

Syntax

t = bench

t = bench(N)

Description

t = bench measures the execution time of six different benchmarking tasks on your computer and compares the results to several benchmark computers. The function:

• Returns a 1-by-6 vector with the measured execution times

• Displays execution times for the benchmark computers

• Creates a bar graph that ranks the computers based on their speed

Note

A benchmark is intended to compare the performance of one particular MATLAB^® release on different computers. It does not offer direct comparisons between different MATLAB releases because tasks and problem sizes change from release to release.

t = bench(N) runs each of the six tasks N times and returns an N-by-6 array with the execution times. If N is zero, the function does not run any of the tasks on your computer, but instead displays the execution times for other computers and compares their speeds.

Fluctuations of 5–10% in the measured times of repeated runs on a single computer are normal.

Examples

Benchmark Performance of Your Computer

Measure the execution time of the six benchmarking tasks on your computer and compare the results to other benchmark computers.

t = bench

t = 1×6

    0.8555    0.7921    0.3945    1.0705    1.1453    3.9184

Input Arguments

N — Number of times to run tasks

nonnegative integer

Number of times to run the six tasks, specified as a nonnegative integer.

More About

Benchmarking Tasks

The six benchmarking tasks are listed in this table.

Task	Description	Performance Factors
LU	Perform lu of a full matrix	Floating-point, regular memory access
FFT	Perform fft of a full vector	Floating-point, irregular memory access
ODE	Solve van der Pol equation with ode45	Data structures and MATLAB function files
Sparse	Solve a symmetric sparse linear system	Mixed integer and floating-point
2-D	Plot Lissajous curves	2-D line drawing graphics
3-D	Display colormapped peaks with clipping and transforms	3-D animated OpenGL graphics

The LU and FFT tasks involve large matrices and long vectors.

The 2-D and 3-D tasks measure graphics performance, including support for hardware-accelerated graphics. The rendererinfo function provides information about the graphics renderer implementation that MATLAB uses. For example, this command gets the information for the current axes and stores it in a structure called info.

info = rendererinfo(gca)

Version History

Introduced in R2008a

R2020a: Change in Relative Task Execution Speeds

Starting in R2020a, problem sizes have increased for the numerical computation tasks (LU, FFT, ODE, and Sparse) so that the ranking of computers using bench test results are not dominated by the 2-D and 3-D graphics tasks. In previous releases, the 2-D and 3-D tasks take significantly longer to complete compared to the numerical computation tasks and therefore contribute disproportionately to the ranking of computers.

This table shows different task execution times in R2020a using a Windows^® 10, Intel^® Xeon^® W-2133 @ 3.60 GHz test system. The measured values are expressed in seconds.

Task	New Problem Sizes	Old Problem Sizes
LU	0.388	0.068
FFT	0.312	0.112
ODE	0.480	0.015
Sparse	0.488	0.103
2-D	0.301	0.307
3-D	0.296	0.303