amaranth
cudf
amaranth | cudf | |
---|---|---|
7 | 23 | |
1,436 | 7,291 | |
1.3% | 1.8% | |
9.7 | 9.9 | |
10 days ago | 2 days ago | |
Python | C++ | |
BSD 2-clause "Simplified" License | 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.
amaranth
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Why are there only 3 languages for FPGA development?
He probably meant Amaranth.
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VRoom A high end RISC-V implementation
As an aside, the latest and active development of nMigen has been rebranded a few months ago to Amaranth and can be found here: https://github.com/amaranth-lang/amaranth . In case people googled nMigen and came to the repository that hasn't been updated in two years.
- NMigen – A Python toolbox for building complex digital hardware (FPGAs)
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Facts every web dev should know before they burn out and turn to painting
Hmm. A followup question: are there any cheats/hacks that would make it possible (if painful) to for example explore the world of USB3, PCIe, or Linux on low-end-ish ARM (eg https://www.thirtythreeforty.net/posts/2019/12/my-business-c..., based on the 533MHz https://linux-sunxi.org/F1C100s), without needing to buy equipment in the mid-4-figure/low-5-figure range, if I were able to substitute a statistically larger-than-average amount of free time (and discipline)?
For example, I learned about https://github.com/GlasgowEmbedded/glasgow recently, a bit of a niche kitchen sink that uses https://github.com/nmigen/nmigen/ to lower a domain-specific subset of Python 3 (https://nmigen.info/nmigen/latest/lang.html) into Verilog which then runs on the Glasgow board's iCE40HX8K. The project basically makes it easier to use cheap FPGAs for rapid iteration. (The README makes a point that the synthesis is sufficiently fast that caching isn't needed.)
In certain extremely specific situations where circumstances align perfectly (caveat emptor), devices like this can sometimes present a temporary escape to the inevitable process of acquiring one's first second-hand high-end oscilloscope (fingers-crossed the expensive bits still have a few years left in them). To some extent they may also commoditize the exploration of very high-speed interfaces, which are rapidly becoming a commonplace principal of computers (eg, having 10Gbps everywhere when USB3.1 hits market saturation will be interesting) faster than test and analysis kit can keep up (eg to do proper hardware security analysis work). The Glasgow is perhaps not quite an answer to that entire statement, but maybe represents beginning steps in that sort of direction.
So, to reiterate - it's probably an unhelpfully broad question, and I'm still learning about the field so haven't quite got the preciseness I want yet, but I'm curious what gadgetry, techniques, etc would perhaps allow someone to "hack it" and dive into this stuff on a shoestring budget? :)
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Awesome Lattice FPGA Boards
Worth knowing that are two "nmigen"s nowadays - the one originated in M-Labs and one under a project also called nmigen:
https://github.com/nmigen/nmigen
It's a fork, made for reasons, but more actively developed. whitequark (long time author/contributor) works on this fork, and no longer the M-Labs version.
- Chisel/Firrtl Hardware Compiler Framework
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Unifying the CUDA Python Ecosystem
Sounds like nmigen might be a good open source successor to the project that you describe: https://github.com/nmigen/nmigen
cudf
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A Polars exploration into Kedro
The interesting thing about Polars is that it does not try to be a drop-in replacement to pandas, like Dask, cuDF, or Modin, and instead has its own expressive API. Despite being a young project, it quickly got popular thanks to its easy installation process and its “lightning fast” performance.
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Why we dropped Docker for Python environments
Perhaps the largest for package size is the NVIDIA developed rapids toolkit https://rapids.ai/ . Even still adding things like pandas and some geospatial tools, you rapidly end up with an image well over a gigabyte, despite following cutting edge best practice with docker and python.
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Introducing TeaScript C++ Library
Yes sure, that is how OpenMP does; but on the other side: you seem to already do some basic type inference, and building an AST, no? Then you know as well the size and type of your vectors, and can execute actions in parallel if there is enough data to be worth parallelizing. Is there anyone who don't want their code to execute faster if it is possible? Those that do work in big data domain do use threads and vectorized instructions without user having to type in any directive; just import different library. Example, numpy or numpy with cuda backend, or similar GPU accelerated libraries like cudf.
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[D] Can we use Ray for distributed training on vertex ai ? Can someone provide me examples for the same ? Also which dataframe libraries you guys used for training machine learning models on huge datasets (100 gb+) (because pandas can't handle huge data).
Not the answer about Ray: you could use rapids.ai. I'm using it for for dataframe manipulation on GPU
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Story of my life
To put Data Analytics on GPU Steroids, Try RAPIDS cudf https://rapids.ai/
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Artificial Intelligence in Python
You can scope out https://rapids.ai/. Nvidia's AI toolkits. They have some handy notebooks to poke at to get you started.
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[D] [R] Large-scale clustering
try https://rapids.ai/
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[P] Looking for state of the art clustering algorithms
As a companion to the other comments, I'd like to mention that the RAPIDS library cuML provides GPU-accelerated versions of quite a few of the algorithms mentioned in this thread (HDBSCAN, UMAP, SVM, PCA, {Exact, Approximate} Nearest Neighbors, DBSCAN, KMeans, etc.).
- Integrating multiple point clouds?
- Buka | Sains Data GPU RAPIDS
What are some alternatives?
SpinalHDL - Scala based HDL
Numba - NumPy aware dynamic Python compiler using LLVM
cocotb - cocotb, a coroutine based cosimulation library for writing VHDL and Verilog testbenches in Python
chia-plotter
chisel - Chisel: A Modern Hardware Design Language
wif500 - Try to find the WIF key and get a donation 200 btc
chiselverify - A dynamic verification library for Chisel.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
myhdl - The MyHDL development repository
rmm - RAPIDS Memory Manager
pygears - HW Design: A Functional Approach
CUDA.jl - CUDA programming in Julia.