Top 5 Jupyter Notebook Performance Projects

amh-code

8 551 10.0 Jupyter Notebook

Complete implementations from "Algorithms for Modern Hardware"

Project mention: Ask HN: Recommendations for high quality, free CS books online | news.ycombinator.com | 2024-03-26

I recently stumbled on https://en.algorithmica.org/hpc/ which I absolutely loved. It's really well written, comprehensible and concise. It felt like a pleasure to read which I find really rare with CS textbooks and I feel like I've come out of it understanding how computers work a bit better
Does anyone have any similar CS books they'd recommend? Ideally they'd be:

gpu-benches

1 136 7.5 Jupyter Notebook

collection of benchmarks to measure basic GPU capabilities
WorkOS

workos.com sponsored

The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
tsdownsample

7 127 6.0 Jupyter Notebook

High-performance time series downsampling algorithms for visualization
Microbenchmarks

1 84 2.3 Jupyter Notebook

Microbenchmarks comparing the Julia Programming language with other languages (by JuliaLang)

Project mention: Romeo and Julia, Where Romeo Is Basic Statistics | news.ycombinator.com | 2024-03-15

> Every language I've ever seen with garbage collection has gone through decades of "now the garbage collection is better" or "just wait until the next version, garbage collection will be better".
Ok but the Go example I linked is already in production, right now, you can use it. This isn't a "it will get better in two releases" situation, Go's GC as of today has pause times that are sub-millisecond. The Java Shenandoah example I linked is still mostly in beta, but it's also something you can use right now, though admittedly it'll probably be awhile before it's in a mainline release.
> This is besides the point of performance and no longer talking about reality, it's just FUD from a "what if" future.
It's not "just FUD", there are dozens of reported security issues that have happened because of bad manual memory management problems. Off the top of my head, Heartbleed was a famous case.
This isn't me badmouthing anyone; manual memory management is hard to get right, even for very smart people.
> Right, but you get it by avoiding allocation and avoiding the garbage collector the same way avoiding allocation in C++ is important, but in julia it won't be woven in to the performance, it will cause big pauses.
Fair enough, I did look at the code for the official benchmarks (https://github.com/JuliaLang/Microbenchmarks/blob/master/per...) and outside of the integer parsing code it does indeed seem to avoid dynamic allocations so I will concede that the benchmarks might be a bit more skewed compared to real-world code.
I still get a hunch that if you compared it allocation-heavy Julia to malloc+free-heavy C++ the differences wouldn't really be that far off, but that's just a hunch and I don't have data to back that up; might be a fun test to write though, so maybe I'll try that this weekend.
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Sort of tangential, but I also do think that there's value in having decent concurrency constructs built into the language. With C++, if you stick to built-ins you are basically stuck with mutexes and despite what people like to pretend, getting correct code with mutexes is really really hard to get right, and very easy to screw up in a non-obvious way. If you allow yourself to use libraries, then you have stuff like ZeroMQ and OpenMP and stuff, so it's really not that dire realistically. However, I think there's value in having nice, easy to use concurrency constructs in the language other than mutexes, and I do wonder if as a result of that it encourages people to utilize multiple threads more frequently, because they don't have to worry about weird deadlock situations as much.
Again, I believe Rust actually does address this because of the single-owner-enforced-at-compile-time stuff, but I haven't used it enough to really draw a conclusion on it.

mlscorecheck

1 11 9.3 Jupyter Notebook

Testing the consistency of binary classification performance scores reported in papers

Project mention: [N] (In)validating published ML performance scores is possible | /r/MachineLearning | 2023-11-18

InfluxDB

www.influxdata.com sponsored

Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

NOTE: The open source projects on this list are ordered by number of github stars. The number of mentions indicates repo mentiontions in the last 12 Months or since we started tracking (Dec 2020).