Measure and Improve GPU Performance
Measure GPU Performance
Measure Code Performance on a GPU
An important measure of the performance of your code is how long it takes to run. The best way to time code running on a GPU is to use the gputimeit
function which runs a function multiple times to average out variation and compensate for overhead. The gputimeit
function also ensures that all operations on the GPU are complete before recording the time.
For example, measure the time that the lu
function takes to compute the LU factorization of a random matrix A
of size N
byN
. To perform this measurement, create a function handle to the lu
function and pass the function handle to gputimeit
.
N = 1000;
A = rand(N,"gpuArray");
f = @() lu(A);
numOutputs = 2;
gputimeit(f,numOutputs)
You can also time your code using tic
and toc
. However, to get accurate timing information for code running on a GPU, you must wait for operations to complete before calling tic
and toc
. To do this, you can use the wait
function with a gpuDevice
object as its input. For example, measure the time taken to compute the LU factorization of matrix A
using tic
, toc
, and wait
.
D = gpuDevice; wait(D) tic [L,U] = lu(A); wait(D) toc
You can view how long each part of your code takes using the MATLAB^{®} Profiler. For more information about profiling your code, see profile
and Profile Your Code to Improve Performance. The Profiler is useful for identifying performance bottlenecks in your code but cannot accurately time GPU code as it does not account for overlapping execution, which is common when you use a GPU.
Use this table to help you decide which timing method to use.
Timing Method  Suitable Tasks  Limitations 

gputimeit  Timing individual functions 

tic and toc  Timing multiple lines of code or entire workflows 

MATLAB Profiler  Finding performance bottlenecks  The Profiler runs each line of code independently and does not account for overlapping execution, which is common when you use a GPU. You cannot use the Profiler as a way to accurately time GPU code. 
GPU Benchmarking
Benchmark tests are useful for identifying the strengths and weaknesses of a GPU and for comparing the performance of different GPUs. Measure the performance of your GPU by using these benchmark tests:
Run the Measure GPU Performance example to obtain detailed information about your GPU, including PCI bus speed, GPU memory read/write, and peak calculation performance for doubleprecision matrix calculations.
Use
gpuBench
to test memory and computationintensive tasks in single and double precision.gpuBench
can be downloaded from the AddOn Explorer or from the MATLAB Central File Exchange. For more information, see https://www.mathworks.com/matlabcentral/fileexchange/34080gpubench.
Improve GPU Performance
The purpose of GPU computing in MATLAB is to speed up your code. You can achieve better performance on the GPU by implementing best practices for writing code and configuring your GPU hardware. Various methods to improve performance are discussed below, starting with the most straightforward to implement.
Use this table to help you decide which methods to use.
Performance Improvement Method  When Should I Use This Method?  Limitations 

Use GPU Arrays – pass GPU arrays to supported functions to run your code on the GPU  Generally applicable  Your functions must support 
Profile and Improve Your MATLAB Code – profile your code to identify bottlenecks  Generally applicable  The profiler cannot be used to accurately time code running on the GPU as described in the Measure Code Performance on a GPU section. 
Vectorize Calculations – replace forloops with matrix and vector operations  When running code that operates on vectors or matrices inside a forloop  For more information, see Using Vectorization. 
Perform Calculations in Single Precision – reduce computation by using lower precision data  When smaller ranges of values and lower accuracy are acceptable  Some types of calculation, such as linear algebra problems, might require doubleprecision processing. 
Use 

For information about supported functions and additional limitations, see 
Use  When using a function that performs independent matrix operations on a large number of small matrices  Not all builtin MATLAB functions are supported. For information about supported functions and additional limitations, see 
Write MEX File Containing CUDA Code – access additional libraries of GPU functions  When you want access to NVIDIA^{®} libraries or advanced CUDA features  Requires code written using the CUDA C++ framework. 
Configure Your Hardware for GPU Performance – make the best use of your hardware  Generally applicable 

Use GPU Arrays
If all the functions that your code uses are supported on the GPU, the only necessary modification is to transfer the input data to the GPU by calling gpuArray
. For a list of MATLAB functions that support gpuArray
input, see Run MATLAB Functions on a GPU.
A gpuArray
object stores data in GPU memory. Because most numeric functions in MATLAB and in many other toolboxes support gpuArray
objects, you can usually run your code on a GPU by making minimal changes. These functions take gpuArray
inputs, perform calculations on the GPU, and return gpuArray
outputs. In general, these functions support the same arguments and data types as standard MATLAB functions that run on the CPU.
Tip
To reduce overhead, limit the number of times you transfer data between the host memory and the GPU. Create arrays directly on the GPU where possible. For more information see, Create GPU Arrays Directly. Similarly, only transfer data from the GPU back to the host memory using gather
if the data needs to be displayed, saved, or used in code that does not support gpuArray
objects.
Profile and Improve Your MATLAB Code
When converting MATLAB code to run on a GPU, it is best to start with MATLAB code that already performs well. Many of the guidelines for writing code that runs well on a CPU will also improve the performance of code that runs on a GPU. You can profile your CPU code using the MATLAB Profiler. The lines of code that take the most time on the CPU will likely be ones that you should improve or consider moving onto the GPU using gpuArray
objects. For more information about profiling your code, see Profile Your Code to Improve Performance.
Because the MATLAB Profiler runs each line of code independently, it does not account for overlapping execution, which is common when you use a GPU. To time whole algorithms use tic
and toc
or gputimeit
as described in the Measure Code Performance on a GPU section.
Vectorize Calculations
Vector, matrix, and higherdimensional operations typically perform much better than scalar operations on a GPU because GPUs achieve high performance by calculating many results in parallel. You can achieve better performance by rewriting loops to make use of higherdimensional operations. The process of revising loopbased, scalaroriented code to use MATLAB matrix and vector operations is called vectorization. For information on vectorization, see Using Vectorization and Improve Performance Using a GPU and Vectorized Calculations. This plot from the Improve Performance Using a GPU and Vectorized Calculations example shows the increase in performance achieved by vectorizing a function executing on the CPU and on the GPU.
Perform Calculations in Single Precision
You can improve the performance of code running on your GPU by calculating in single precision instead of double precision. CPU computations do not provide this improvement when switching from double to single precision because most GPU cards are designed for graphic display, which demands a high singleprecision performance. For more information on converting data to single precision and performing arithmetic operations on singleprecision data, see FloatingPoint Numbers.
Typical examples of workflows suitable for singleprecision computation on the GPU include image processing and machine learning. However, other types of calculation, such as linear algebra problems, typically require doubleprecision processing. The Deep Learning Toolbox™ performs many operations in single precision by default. For more information, see Deep Learning Precision (Deep Learning Toolbox).
The exact performance improvement depends on the GPU card and total number of cores. Highend compute cards typically show a smaller improvement. For a comprehensive performance overview of NVIDIA GPU cards, including single and doubleprecision processing power, see https://en.wikipedia.org/wiki/List_of_Nvidia_graphics_processing_units.
Improve Performance of ElementWise Functions
If you have an elementwise function, you can often improve its performance by calling it with arrayfun
. The arrayfun
function on the GPU turns an elementwise MATLAB function into a custom CUDA kernel, which reduces the overhead of performing the operation. You can often use arrayfun
with a subset of your code even if arrayfun
does not support your entire code. The performance of a wide variety of elementwise functions can be improved using arrayfun
, including functions performing many elementwise operations within looping or branching code, and nested functions where the nested function accesses variables declared in its parent function.
The Improve Performance of ElementWise MATLAB Functions on the GPU Using arrayfun example shows a basic application of arrayfun
. The Using GPU arrayfun for MonteCarlo Simulations example shows arrayfun
used to improve the performance of a function executing elementwise operations within a loop. The Stencil Operations on a GPU example shows arrayfun
used to call a nested function that accesses variables declared in a parent function.
Improve Performance of Operations on Small Matrices
If you have a function that performs independent matrix operations on a large number of small matrices, you can improve its performance by calling it with pagefun
. You can use pagefun
to perform matrix operations in parallel on the GPU instead of looping over the matrices. The Improve Performance of Small Matrix Problems on the GPU Using pagefun example shows how to improve performance using pagefun
when operating on many small matrices.
Write MEX File Containing CUDA Code
While MATLAB provides an extensive library of GPUenabled functions, you can access libraries of additional functions that do not have analogs in MATLAB. Examples include NVIDIA libraries such as the NVIDIA Performance Primitives (NPP) and cuRAND libraries. You can compile MEX files that you write in the CUDA C++ framework using the mexcuda
function. You can execute the compiled MEX files in MATLAB and call functions from NVIDIA libraries. For an example that shows how to write and run MEX functions that take gpuArray
input and return gpuArray
output, see Run MEX Functions Containing CUDA Code.
Configure Your Hardware for GPU Performance
Because many computations require large quantities of memory and most systems use the GPU constantly for graphics, using the same GPU for computations and graphics is usually impractical.
On Windows^{®} systems, a GPU device has two operating models: Windows Display Driver Model (WDDM) or Tesla Compute Cluster (TCC). To attain the best performance for your code, set the devices that you use for computing to use the TCC model. To see which model your GPU device is using, inspect the DriverModel
property returned by the gpuDevice
function. For more information about switching models and which GPU devices support the TCC model, consult the NVIDIA documentation.
To reduce the likelihood of running out of memory on the GPU, do not use one GPU on multiple instances of MATLAB. To see which GPU devices are available and selected, use the gpuDeviceTable
function.
See Also
gpuDevice
 gputimeit
 tic
 toc
 gpuArray
 arrayfun
 pagefun
 mexcuda