Run video card benchmark code#

Performing arithmetic operations on single-precision data, see Floating-Point Numbers. For more information on converting data to single precision and This improvement when switching from double to single precision because most GPUĬards are designed for graphic display, which demands a high single-precision Single precision instead of double precision.

Run video card benchmark code#

You can improve the performance of code running on your GPU by calculating in Shows the increase in performance achieved by vectorizing a function executing Plot from the Improve Performance Using a GPU and Vectorized Calculations example Vectorization, see Using Vectorization and Improve Performance Using a GPU and Vectorized Calculations. Revising loop-based, scalar-oriented code to use MATLAB matrix and vector Rewriting loops to make use of higher-dimensional operations.

Vector, matrix, and higher-dimensional operations typically perform muchīetter than scalar operations on a GPU because GPUs achieve high performance byĬalculating many results in parallel. Gputimeit as described in the Measure Code Performance on a GPU Your code, see Profile Your Code to Improve Performance.īecause the MATLAB Profiler runs each line of code independently, it does not accountįor overlapping execution, which is common when you use a GPU. Will likely be ones that you should improve or consider moving onto the GPU The lines of code that take the most time on the CPU You can profile your CPU code using the MATLAB Profiler. Runs well on a CPU will also improve the performance of code that runs on a GPU. Many of the guidelines for writing code that When converting MATLAB code to run on a GPU, it is best to start with MATLABĬode that already performs well. Saved, or used in code that does not support gpuArray The GPU back to the host memory using gather if the data needs to be displayed, For more information see, Create GPU Arrays Directly. To reduce overhead, limit the number of times you transfer data between Might require double-precision processing.Įlement-wise functions using a custom CUDA ® kernel Some types of calculation, such as linear algebra problems, When smaller ranges of values and lower accuracy are

Perform Calculations in Single Precision – reduce computation by using lower precision When running code that operates on vectors or matrices inside aįor more information, see Using Vectorization. Vectorize Calculations – replace for-loops with matrix and vector operations On the GPU as described in the Measure Code Performance on a GPU section. The profiler cannot be used to accurately time code running Profile and Improve Your MATLAB Code – profile your code to identify bottlenecks Input, see Run MATLAB Functions on a GPU.

For a list of MATLAB functions that support gpuArray Use GPU Arrays – pass GPU arrays to supported functions to run your code on