Memory issues when running simple program

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Britt Cagnina

Sep 2, 2021, 1:45:31 PMSep 2
to sage-support
I'm running SageMath 9.2 on windows (and also tried 9.0 on Ubuntu). My specs are: Intel i5-10600K CPU @ 4.10GHz, 32.0 GB RAM, and a RTX 3060 graphics card.

When I run the following program, it seems to starts leaking memory until all 32 GB is consumed, then crashes. I may be doing something silly in the below example (though it is roughly copy/paste from

Or perhaps I simply don't have enough memory--which would be odd considering I can ray trace much more complicated objects on Blender with no trouble.

f = lambda t: (t, t^2, t^3)
t = Tachyon(camera_center=(5,0,4))
t.texture('t0', ambient=0.1, diffuse=0.9, specular=0.1,  opacity=1.0, color=(1.0,0,0))
t.light((-20,-20,40), 0.2, (1,1,1))
t.parametric_plot(f, -10, 10,'t', min_depth=20, max_depth=40)'parametric.png', verbose=0, extra_opts='')

Thanks for the time,

Nils Bruin

Sep 2, 2021, 7:45:20 PMSep 2
to sage-support
I'd suspect the problem is not the ray tracing but the description of the object. Probably, `parametric_plot` produces a description of your shape with tubular segments and with a min_depth=20 it probably produces *very* many of them. So you probably don't get to ray tracing: you're stuck in computing the description of the scene. With smaller numbers for min_depth and max_depth it produces a picture very quickly and with no visible artifacts.

The interface with Tachyon probably ends up constructing the input to Tachyon (which is a text-file description) as a big string in memory, so a scene with very many geometric shapes would indeed pose a significant memory strain (Tachyon would be similarly strained to work with such a description).

Justin C. Walker

Sep 3, 2021, 12:11:24 AMSep 3
to SAGE Support
FWIW, I ran this on a 2019 Mac Book Pro (8-core 2.4 GHz Core i9) with 64GB of memory and ~4TB of disk.

The code thrashed the disk with more paging than I have seen in a long time with this much disk. As Nils guesses, it’s the parametric_plot() call that brings things to a halt. I finally killed it after about 4 hours.

Also, memory usage for this python process seemed to stabilize for the last 2-3 hours.


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