Griddata Memory Usage for small arrays
显示 更早的评论
I am using vq = griddata(x,y,z,v,xq,yq,zq) to interpolate scattered data onto a rectangular grid (note: sample values are complex). Typical array sizes for x,y, etc. are on the order of 500 x 800. Nearest neighbor and v4 work, but take close to 10 minutes to run. While waiting, I notice that my memory usage exceeds 26 GB (32GB installed) and CPU / disk usage is pegged. The line of code is a part of an app designer app, and I have tried moving it to a separate function just in case being inside a Method was making it sluggish, but it did not affect the speed.
What can I do to troubleshoot this issue?
1 个评论
Walter Roberson
2022-6-1
Have you experimented with scatteredInterpolant? What is the size of the query space?
采纳的回答
Prateekshya
2023-9-7
编辑：Prateekshya
2023-9-7
Hi Rod,
As per my understanding you are trying to optimize the speed for your code which uses "griddata" function. When dealing with large data sets and experiencing slow performance and high memory usage during the "griddata" interpolation, assuming that you have already checked the data size and complexity as well as considered reducing the data size, there are several steps you can take to troubleshoot and optimize the process:
- The "griddata" function supports different interpolation methods, such as "linear", "cubic", "natural" and "nearest". Try different methods to see if any provide better performance for your specific data set. For example:
vq = griddata(x,y,z,v,xq,yq,zq,"nearest");
- If you have access to the Parallel Computing Toolbox and you are using for loop, you can parallelize the "griddata" interpolation using "parfor" or "spmd" constructs. Please refer to the following documentation for knowing more about these constructs:
For "parfor": https://in.mathworks.com/help/coder/ref/parfor.html
- Use MATLAB's profiling tools, such as the Profiler or the "profile" command, to identify the parts of your code that are taking the most time and consuming the most memory. For more information please go through: https://in.mathworks.com/help/matlab/matlab_prog/profiling-for-improving-performance.html
- If your output grid does not require high precision, you can reduce the resolution of the output grid to speed up the interpolation. This can be done by reducing the number of grid points in the "xq", "yq", and "zq" arrays.
Hope this helps!
3 个评论
Stephen23
2023-9-7
"...you can parallelize the "griddata" interpolation using "parfor"..."
How?
Bruno Luong
2023-9-7
Divide the query points in chunks and use parfor on chunk?
It's not ideal since the shared preprocessing part of the algorithm must be redo for every chunk, contrary to scatteredInterpolant.
Rod Lopez
2023-9-7
更多回答（0 个）
类别
在 帮助中心 和 File Exchange 中查找有关 Get Started with Curve Fitting Toolbox 的更多信息
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!
Translated by 
