Pablo, Here's my quick list of ideas to generate gigantic images... The easiest way to fill the image quickly is to load huge packages that can also generate tons of data. The firsts that come to mind are Moose, Roassal, Seaside, BioSmalltalk, PolyMath, Marea, Magma (not sure if it is still supported though) and Dr. Geo. Another possibility is to use tools that can import tons of data into the image : there are tons of stress-test-gicantic XML schemas out there that we could load/read with one of our XML readers out there. Another good candidate to torture the GC would be the FHCP challenge: they had to solved gigantic graphs and, luckily for us, the winners in 2016 were from Inria so someone there must have the data to load those graphs (and perhaps the algorithms!). DeepTraverser could probably do the job here. Other obvious tests involve pushing the language/VM to its limits : how does Pharo react if we flood the image with 4 million symbols??? Or create a hierarchy of 30000 classes? Or have a package with 2000 tags? Is there any limit to the size of a methods (say, we create a method with 5000 literals)? Or create a gazillion classes, each with the maximum number of instance variables possible? What if we create a class that has a reference to every class in the image and see how the dependency checker copes with that? Also, there's a lot of objects that are treated "specially" in the image.?? How does the VM/GC reacts when there's a gazillion of them? Semaphores, blocks, symbols, points, processes, weak objects, etc. We could also generate a humongous graph/collection/whatever that could be loaded fast with Fuel instead of having to create those objects from scratch every time. P.S. I have found quite a few discussions on the Squeak & Pharo mailing lists regarding problems with large images and I've collected the references to those threads. I'd be more than happy to share those with you in private if you're interested! I've also found lots of references to GC benchmarks/torture-tests used by other languages with GC and I think lots of them could also apply to our memory model... On 2020-01-29 13:30, tesonep@gmail.com wrote:
The main goal of the project is to start a recollection of existing solutions and to add new ones to generate this synthetic images. We want also to generate images that reproduce the nature of images. For example, it is not enough to generate random method selectors if we are testing the indexing of them. A good index varies its performance depending of the nature of the text. We need to generate random methods following some rules a developer whould use, for example using more a given word or using real words.
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