Streaming and threading are a complex issue in computing in general. This chapter provides the keys to help you understand how it is working so you can make the right choices later.
First, you have to be aware that streaming and threading are two different things even if they are linked to a certain extent. In OTB:
To sum up: streaming is good if you have big images, threading is good if you have several processing units.
However, these two properties are not unrelated. Both rely on the filter ability to process parts of the image and combine the result, that what the ThreadedGenerateData() method can do.
For OTB, streaming is pipeline related while threading is filter related. If you build a pipeline where one filter is not streamable, the whole pipeline is not streamable: at one point, you would hold the entire image in memory. Whereas you will benefit from a threaded filter even if the rest of the pipeline is made of non-threadable filters (the processing time will be shorter for this particular filter).
Even if you use a non streamed writer, each filter which has a ThreadedGenerateData() will split the image into two and send each part to one thread and you will notice two calls to the function.
If you have some particular requirement and want to use only one thread, you can call the SetNumberOfThreads() method on each of your filter.
When you are writing your own filter, you have to follow some rules to make your filter streamable and threadable. Some details are provided in sections 28.3 and 28.4.
The division of the image occurs generally at the writer level. Different strategies are available and can be specified explicitly. In OTB, these are referred as splitter. Several available splitters are:
You can add your own strategies based on these examples.
To change the splitting strategy of the writer, you can use the following model: