I can't reproduce it. I managed to install pyopencl and run the script. It takes more than 2 hours, and uses only 7GB RAM. Maybe, some faster backend for OpenCL is required?
I used Microsoft Azure Compute, Standard_A4m_v2 (4 cores, 32 GB memory) instance. More easy way to reproduce is needed... > My best idea about what's going on at the moment is that memory > fragmentation is worse in Python 3.6 for some reason. The virtual memory > size indicates that a large address space is acquired, but the resident > memory size is smaller indicating that not all of that address space is > actually used. In fact, the code might be especially bad to fragmentation > because it takes a lot of small NumPy arrays and concatenates them into > larger arrays. But I'm still surprised that this is only a problem with > Python 3.6 (if this hypothesis is correct). > > Jan Generally speaking, VMM vs RSS doesn't mean fragmentation. If RSS : total allocated memory ratio is bigger than 1.5, it may be fragmentation. And large VMM won't cause swap. Only RSS is meaningful. -- https://mail.python.org/mailman/listinfo/python-list
