yolov7-object-tracking
1brc
| yolov7-object-tracking | 1brc | |
|---|---|---|
| 2 | 28 | |
| 535 | 5,246 | |
| - | - | |
| 2.8 | 9.8 | |
| 19 days ago | 24 days ago | |
| Python | Java | |
| GNU General Public License v3.0 only | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
yolov7-object-tracking
1brc
-
The One Billion Row Challenge in CUDA: from 17 minutes to 17 seconds
This would be the code to beat. Ideally with only 8 cores but any number of cores is also very interesting.
https://github.com/gunnarmorling/1brc/discussions/710
-
One Billion Row Challenge in Golang - From 95s to 1.96s
Given that 1-billion-line-file is approximately 13GB, instead of providing a fixed database, the official repository offers a script to generate synthetic data with random readings. Just follow the instructions to create your own database.
-
1BRC Merykitty's Magic SWAR: 8 Lines of Code Explained in 3k Words
Local disk I/O is no longer the bottleneck on modern systems: https://benhoyt.com/writings/io-is-no-longer-the-bottleneck/
In addition, the official 1BRC explicitly evaluated results on a RAM disk to avoid I/O speed entirely: https://github.com/gunnarmorling/1brc?tab=readme-ov-file#eva... "Programs are run from a RAM disk (i.o. the IO overhead for loading the file from disk is not relevant)"
-
Processing One Billion Rows in PHP!
You may have heard of the "The One Billion Row Challenge" (1brc) and in case you don't, go checkout Gunnar Morlings's 1brc repo.
-
The One Billion Row Challenge in Go: from 1m45s to 4s in nine solutions
Here’s a thread on results with duckdb, I don’t mean to discourage you taking a shot at all though: https://github.com/gunnarmorling/1brc/discussions/39
-
Ask HN: How can I learn about performance optimization?
If you are in “javaland” look at billion row challenge, you will learn a lot - https://github.com/gunnarmorling/1brc
- Lessons Learned from Doing the One Billion Row Challenge
- 1B Row Challenge Shows Java Can Process 1B Rows File in 2 Seconds
-
From slow to SIMD: A Go optimization story
Even manual vectorization is pain...writing ASM, really?
Rust has unstable portable SIMD and a few third-party crates, C++ has that as well, C# has stable portable SIMD and a very small BLAS-like library on top of it (hell it even exercises PackedSIMD when ran in a browser) and Java is getting stable Panama vectors some time in the future (though the question of codegen quality stands open given planned changes to unsafe API).
Go among these is uniquely disadvantaged. And if that's not enough, you may want to visit 1Brc's challenge discussions and see that Go struggles get anywhere close to 2s mark with both C# and C++ are blazing past it:
https://hotforknowledge.com/2024/01/13/1brc-in-dotnet-among-...
https://github.com/gunnarmorling/1brc/discussions/67
-
JEP Draft: Deprecate Memory-Access Methods in Sun.misc.Unsafe for Removal
In terms of performance: I realize that this is a somewhat "toy" issue, and it's a sample size of 1, but for the currently ongoing "One Billion Row Challenge"[1] (an ongoing Java performance competition related to parsing and aggregating a 13 GB file), all of the current top-performers are using Unsafe. More specifically, the use of Unsafe appears to have been the change for a few entries that allowed getting below the 3-second barrier in the test.
1. https://github.com/gunnarmorling/1brc
What are some alternatives?
concrete-ml - Concrete ML: Privacy Preserving ML framework built on top of Concrete, with bindings to traditional ML frameworks.
1brc - C99 implementation of the 1 Billion Rows Challenge. 1️⃣🐝🏎️ Runs in ~1.6 seconds on my not-so-fast laptop CPU w/ 16GB RAM.
paxml - Pax is a Jax-based machine learning framework for training large scale models. Pax allows for advanced and fully configurable experimentation and parallelization, and has demonstrated industry leading model flop utilization rates.
csvlens - Command line csv viewer
Youtube2Webpage - I learn much better from text than from videos
nodejs - 1️⃣🐝🏎️ The One Billion Row Challenge with Node.js -- A fun exploration of how quickly 1B rows from a text file can be aggregated with different languages.
ultralytics - NEW - YOLOv8 🚀 in PyTorch > ONNX > OpenVINO > CoreML > TFLite
pocketbase - Open Source realtime backend in 1 file
hands-on-train-and-deploy-ml - Train and Deploy an ML REST API to predict crypto prices, in 10 steps
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
co-tracker - CoTracker is a model for tracking any point (pixel) on a video.
java - Java bindings for TensorFlow