1brc
Tribuo
1brc | Tribuo | |
---|---|---|
28 | 15 | |
5,246 | 1,227 | |
- | 0.7% | |
9.8 | 4.8 | |
24 days ago | 11 days ago | |
Java | Java | |
Apache License 2.0 | Apache 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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1brc
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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
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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.
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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)"
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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.
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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
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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
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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
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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
Tribuo
- FLaNK Weekly 08 Jan 2024
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Is deeplearning4j a good choice?
It seems to have been picked up by Eclipse and there is also Oracle Labs' Tribuo and Deep Java Library. All seem active, but I don't know much about any of them. I agree it's probably best to follow the community and use a more popular tool like PyTorch.
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Stochastic gradient descent written in SQL
We built model & data provenance into our open source ML library, though it's admittedly not the W3C PROV standard. There were a few gaps in it until we built an automated reproducibility system on top of it, but now it's pretty solid for all the algorithms we implement. Unfortunately some of the things we wrap (notably TensorFlow) aren't reproducible enough due to some unfixed bugs. There's an overview of the provenance system in this reprise of the JavaOne talk I gave here https://www.youtube.com/watch?v=GXOMjq2OS_c. The library is on GitHub - https://github.com/oracle/tribuo.
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Just want to vent a bit
Although it may be a bit more work, you can do both machine learning and AI in Java. If you are doing deep learning, you can use DeepJavaLibrary (I do work on this one at Amazon). If you are looking for other ML algorithms, I have seen Smile, Tribuo, or some around Spark.
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Anybody here using Java for machine learning?
We've been developing Tribuo on Github for two years now, MS are very actively developing ONNX Runtime (and the Java layer is fairly thin and wrapped over the same C API they use for node.js and C#), and things like XGBoost and LibSVM have been around for many years and the Java bits are developed in tree with the rest of the code so updated along with it. Amazon have a team of people working on DJL, though you'd have to ask them what their plans are.
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Java engineer wants to be a researcher
FWIW, Oracle actually did release a Java ML library - https://github.com/oracle/tribuo.
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txtai 3.4 released - Build AI-powered semantic search applications in Java
Tribuo (tribuo.org, github.com/oracle/tribuo). ONNX export support is there for 2 models at the moment in main, there's a PR for factorization machines which supports ONNX export, and we plan to add another couple of models and maybe ensembles before the upcoming release. Plus I need to write a tutorial on how it all works, but you can check the tests in the meantime.
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Hottest topics for research for JAVA software engineers
You can do ML & data science in Java (full disclosure: I help run TensorFlow-Java, I maintain ONNX Runtime's Java interface, and I'm the lead developer on Oracle Labs' Java ML library Tribuo, so I'm pretty biased). It tends not to be as favoured in research, though I've published academic ML papers which used Java implementations. People do deploy ML models quite a bit in Java in industry.
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John Snow Labs Spark-NLP 3.1.0: Over 2600+ new models and pipelines in 200+ languages, new DistilBERT, RoBERTa, and XLM-RoBERTa transformers, support for external Transformers, and lots more!
It might be worth having a look at the ONNX Runtime Java API in addition to TF-Java, it'll let you deploy the rest of the HuggingFace pytorch models that don't have TF equivalents. I built the Java API a few years ago, and it's now a supported part of the ONNX Runtime project. We use it in Tribuo to provide one of our text feature embedding classes (BERTFeatureExtractor).
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If it gets better w age, will java become compatible for machine learning and data science?
The IJava notebook kernel works pretty well for data science on top of Java. We use it in Tribuo to write all our tutorials, and if you've got the jar file in the right folder everything is runnable. For example, this is our intro classification tutorial - https://github.com/oracle/tribuo/blob/main/tutorials/irises-tribuo-v4.ipynb.
What are some alternatives?
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.
Deep Java Library (DJL) - An Engine-Agnostic Deep Learning Framework in Java
yolov7-object-tracking - YOLOv7 Object Tracking Using PyTorch, OpenCV and Sort Tracking
Deeplearning4j - Suite of tools for deploying and training deep learning models using the JVM. Highlights include model import for keras, tensorflow, and onnx/pytorch, a modular and tiny c++ library for running math code and a java based math library on top of the core c++ library. Also includes samediff: a pytorch/tensorflow like library for running deep learning using automatic differentiation.
csvlens - Command line csv viewer
oj! Algorithms - oj! Algorithms
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.
spark-nlp - State of the Art Natural Language Processing
pocketbase - Open Source realtime backend in 1 file
txtai - 💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
grobid - A machine learning software for extracting information from scholarly documents