Timeline for What causes excessive CPU use by taskgated, notifyd, and launchd processes?
Current License: CC BY-SA 4.0
13 events
| when toggle format | what | by | license | comment | |
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| Aug 16, 2020 at 9:55 | history | edited | Graham Miln | CC BY-SA 4.0 |
Mention ad-hoc code signing to address issue in comments of @jvarela helpful answer.
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| Aug 13, 2020 at 10:34 | history | edited | Graham Miln | CC BY-SA 4.0 |
Missing word and clarification.
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| Aug 13, 2020 at 8:15 | history | edited | Graham Miln | CC BY-SA 4.0 |
Mentioned performance mode and HPC.
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| Aug 13, 2020 at 3:36 | comment | added | Stuart Robbins | The -m cProfile tool is nice. I've spent the better part of the last 9 hours using it to optimize this code chunk, in a test region getting the time down from 14.1 seconds to 3.8 seconds. The HUGE time sink was that my code uses two different conda environments (they don't play well together), and using "conda run -n [other] [code]" was causing a ≈0.5–1-second lag to just start it up (x3500 times). So, I have used other tools to work around those required scripts. However, while this full test area now runs in 15 minutes instead of 25, I'm STILL getting a 20–25% System usage cut, same PIDs. | |
| Aug 12, 2020 at 17:33 | comment | added | Stuart Robbins | Looks like you edited again, so to address your question at the top: My goal is both. Obviously, running the code faster is the practical goal, but if there are coding issues running afoul of the way macOS operates that causes this, then I should know about those so as I proceed, I don't re-introduce the issue. Running it virtually in a different OS is not ideal, at all, and seems to be a cheat factor for an underlying issue. | |
| Aug 12, 2020 at 12:09 | history | edited | Graham Miln | CC BY-SA 4.0 |
Fix grammar.
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| Aug 12, 2020 at 9:04 | history | edited | Graham Miln | CC BY-SA 4.0 |
Added performance profile link.
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| Aug 12, 2020 at 8:31 | comment | added | Stuart Robbins | You've made many additions to your post, so I'll just quickly note: I'm running everything on NVMe, so disk I/O is very fast. In the last 10 minutes, I've changed all the mv/rm/cp calls with os.system to built-in Python calls, but that did not change the overhead. The other programs that it calls are not something I have any ability to modify. This chunk of code has already been updated to minimize disk I/O, I can't change it further (I can change other aspects, but those aren't bogged down in this overhead). I'll let you know if Docker works, BUT in principle, it doesn't answer my question. | |
| Aug 12, 2020 at 8:23 | history | edited | Graham Miln | CC BY-SA 4.0 |
More detail.
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| Aug 12, 2020 at 8:16 | history | edited | Graham Miln | CC BY-SA 4.0 |
More detail.
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| Aug 12, 2020 at 8:04 | history | edited | Graham Miln | CC BY-SA 4.0 |
added 396 characters in body
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| Aug 12, 2020 at 8:04 | comment | added | Stuart Robbins | I'll try this, I will see if it actually works right without the performance hit. The Linux test was with a slightly older version of my code. | |
| Aug 12, 2020 at 8:02 | history | answered | Graham Miln | CC BY-SA 4.0 |