kdb
array
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kdb | array | |
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3 | 4 | |
41 | 188 | |
- | - | |
5.6 | 6.9 | |
2 months ago | 4 months ago | |
q | C++ | |
Apache License 2.0 | Apache License 2.0 |
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kdb
- Q Coding Guidelines by Finos
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Ngn/k (free K implementation)
> let's say I have a finance team that have never heard of it - why might they be interested?
In my experience it's very good at quickly developing real-time analytics applications with only a small set of developers. A couple of q developers can develop, maintain and operate the server side of 5 or 6 separate applications without breaking a sweat. Changes come in at a high speed too.
It's a highly interactive language. A bit like a lisp, you start up a q process, open a port and then you iterate and update your application live without needing to restart. Typically on our projects we've had a well iterated program running in QA for a day or 2 before opening a PR (which becomes more of a formality for getting the solution to the problem into prod at that stage).
The q language itself is quite wordy. Check the reference page: https://code.kx.com/q/ref/ Many programs written in q consist mainly of the key words with the special operators interspersed. Also see some example libraries: https://github.com/finos/kdb
It's been a fairly stable language to work with, having few breaking changes between successive versions. q code written 8/9/10 years ago on older versions will most likely still run the same today. We have source code on one project at work which hasn't had a code change in 6 years now (despite moving through different versions 2.8->3.0->3.3->3.5->4.0) and it runs daily without a hiccup.
Mostly it's a joy working with it because I feel like I get to tell the computer what I want it to do, without also having to tell it how to do it.
array
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Benchmarking 20 programming languages on N-queens and matrix multiplication
I should have mentioned somewhere, I disabled threading for OpenBLAS, so it is comparing one thread to one thread. Parallelism would be easy to add, but I tend to want the thread parallelism outside code like this anyways.
As for the inner loop not being well optimized... the disassembly looks like the same basic thing as OpenBLAS. There's disassembly in the comments of that file to show what code it generates, I'd love to know what you think is lacking! The only difference between the one I linked and this is prefetching and outer loop ordering: https://github.com/dsharlet/array/blob/master/examples/linea...
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A basic introduction to NumPy's einsum
If you are looking for something like this in C++, here's my attempt at implementing it: https://github.com/dsharlet/array#einstein-reductions
It doesn't do any automatic optimization of the loops like some of the projects linked in this thread, but, it provides all the tools needed for humans to express the code in a way that a good compiler can turn it into really good code.
What are some alternatives?
ngn-k-tutorial - An ngn/k tutorial.
optimizing-the-memory-layout-of-std-tuple - Optimizing the memory layout of std::tuple
Kbd - Alternative unified APL keyboard layouts (AltGr, Backtick, Compositions)
NumPy - The fundamental package for scientific computing with Python.
array - Simple array language written in kotlin
cadabra2 - A field-theory motivated approach to computer algebra.
kerf1 - Kerf (Kerf1) is a columnar tick database and time-series language for Linux/OSX/BSD/iOS/Android. It is written in C and natively speaks JSON and SQL. Kerf can be used for trading platforms, feedhandlers, low-latency networking, high-volume analysis of realtime and historical data, logfile processing, and more.
alphafold2 - To eventually become an unofficial Pytorch implementation / replication of Alphafold2, as details of the architecture get released
PDP_11_Simulator - PDP11 Simulator written in APL
Einsum.jl - Einstein summation notation in Julia
ok - An open-source interpreter for the K5 programming language.
c-examples - Example C code