itamarst

12 ms·

91.
by itamarst 4y ago
Have you tried PyPy?
92.
When should you upgrade to Python 3.11?(pythonspeed.com)
6 points by itamarst 4y ago|5 comments
93.
by itamarst 4y ago
They're shipping PGO builds of the Rust compiler, so for faster compilation you don't have to do anything ("Windows builds now use profile-guided optimization, providing 10-20% improvements to compiler performance", per
94.
Find slow data processing tasks (before your customers do)(pythonspeed.com)
2 points by itamarst 4y ago|0 comments
95.
More or less horrible things you can do to measure Python performance(pythonspeed.com)
3 points by itamarst 4y ago|0 comments
96.
Invasive procedures: Python affordances for performance measurement(pythonspeed.com)
1 points by itamarst 4y ago|0 comments
97.
Counts: A command-line tool for ad-hoc profiling(github.com)
2 points by itamarst 4y ago|0 comments
98.
Finding performance problems: profiling or logging?(pythonspeed.com)
15 points by itamarst 4y ago|1 comments
99.
by itamarst 4y ago
For pile-of-strings data, there are still things you can do. E.g. in Pandas, if there are a small number of different values, switch to categoricals ( https://pythonspeed.com/articles/pandas-load-less-data/ item 3)
100.
by itamarst 4y ago
It takes like 5 minutes, and once you are in the habit it's something you do automatically as you write the code and so it doesn't actually cost you extra time. Efficient representation should be something you build into your data
101.
by itamarst 4y ago
There's just a huge amount of waste in many cases which is very easy to fix. For example, if we have a list of fractions (0.0-1.0): * Python list of N Python floats: 32×N bytes (approximate, the Python float is 24 bytes + 8-byte pointe
102.
To find performance bottlenecks, observe production(pythonspeed.com)
1 points by itamarst 4y ago|0 comments
103.
Aya: your tRusty eBPF companion(deepfence.io)
58 points by itamarst 4y ago|8 comments
104.
The limits of Python vectorization as a performance technique(pythonspeed.com)
3 points by itamarst 4y ago|0 comments
105.
by itamarst 4y ago
Does anyone actually use cache-oblivious data structure in practice? Not, like, "yes I know there's a cache I will write a datastructure for that", that's common, but specifically cache-oblivious data structures? People
106.
Finding performance bottlenecks in Celery tasks(pythonspeed.com)
1 points by itamarst 4y ago|0 comments
107.
Show HN: How to get access to coredumps from GitHub Actions runs(github.com)
3 points by itamarst 4y ago|0 comments
108.
Show HN: Sciagraph, performance+memory profiler for production Python batch jobs(sciagraph.com)
3 points by itamarst 4y ago|0 comments
109.
Why new Macs break your Docker build, and how to fix it(pythonspeed.com)
5 points by itamarst 4y ago|0 comments
110.
Pandas vectorization: faster code, slower code, bloated memory(pythonspeed.com)
1 points by itamarst 4y ago|0 comments
111.
Making pip installs a little less slow(pythonspeed.com)
13 points by itamarst 4y ago|0 comments
112.
CPUs, cloud VMs, and noisy neighbors: the limits of parallelism(pythonspeed.com)
2 points by itamarst 4y ago|1 comments
113.
Faster, more memory-efficient Python JSON parsing with msgspec(pythonspeed.com)
5 points by itamarst 4y ago|0 comments
114.
When Python can’t thread: a deep-dive into the GIL’s impact(pythonspeed.com)
4 points by itamarst 4y ago|0 comments
115.
by itamarst 4y ago
I'm on vacation, will do Monday.
116.
by itamarst 4y ago
Fil can dump memory profiling reports on failed allocations; as other commenter said, Linux by default happily just gives you memory even if doesn't have any, so failed allocation implies giant allocation. https://pythonspee
117.
by itamarst 4y ago
BTW I am starting a Slack for devs working on profilers, would be great to have you all join, I'd love to hear more about the ELF patching technique (Fil uses LD_PRELOAD and macOS equivalent).
118.
by itamarst 4y ago
Cool! Fil also tracks every allocation too, although it doesn't dump that at the moment, just the resulting report.
119.
by itamarst 4y ago
I'm excited to see more profiling tools for Python! This sounds like it does peak memory, which is critical for batch jobs, since that's the bottleneck. Memory is fundamentally different than performance in that it's a limite
120.
Speeding up software with faster hardware: tradeoffs and alternatives(pythonspeed.com)
1 points by itamarst 5y ago|0 comments

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