mmap-go
plb2
mmap-go | plb2 | |
---|---|---|
3 | 7 | |
906 | 245 | |
- | - | |
0.0 | 9.4 | |
almost 2 years ago | about 2 months ago | |
Go | C | |
BSD 3-clause "New" or "Revised" License | Creative Commons Zero v1.0 Universal |
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.
mmap-go
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The One Billion Row Challenge in Go: from 1m45s to 4s in nine solutions
Well, I guess it's more that the standard library doesn't have a cross-platform way to access them, not that memory-mapped files themselves can't be done on (say) Windows. It looks like there's a fairly popular 3rd party package that supports at least Linux, macOS, and Windows: https://github.com/edsrzf/mmap-go
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llama.go - Meta's LLaMA GPT inference in pure Golang
Can you use https://github.com/edsrzf/mmap-go to speed up loading
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Discovering and exploring mmap using Go
We're going to explore more mmap functionalities from the point of view of the API provided by mmap-go. There are probably more features that the native syscall provides that this library does not implement.
plb2
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Byte-Sized Swift: Building Tiny Games for the Playdate
https://github.com/attractivechaos/plb2 - limited but broad comparison across a large number of languages. Swift and Nim both compare favourably to C.
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The One Billion Row Challenge in Go: from 1m45s to 4s in nine solutions
https://github.com/attractivechaos/plb2/blob/master/README.m...
Synthetic benchmarks aside, I think as far as average (spring boots of the world) code goes, Go beats Java almost every time, often in less lines than the usual pom.xml
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Python 3.13 Gets a JIT
I wouldn't be so enthusiastic. Look at other languages that have JIT now: Ruby and PHP. After years of efforts, they are still an order of magnitude slower than V8 and even PyPy [1]. It seems to me that you need to design a JIT implementation from ground up to get good performance – V8, Dart, LuaJIT and PyPy are like this; if you start with a pure interpreter, it may be difficult to speed it up later.
[1] https://github.com/attractivechaos/plb2
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Benchmarking 20 programming languages on N-queens and matrix multiplication
A curious thing about Swift: after https://github.com/attractivechaos/plb2/pull/23, the matrix multiplication example is comparable to C and Rust. However, I don’t see a way to idiomatically optimise the sudoku example, whose main overhead is allocating several arrays each time solve() is called. Apparently, in Swift there is no such thing as static array allocation. That’s very unfortunate.
What are some alternatives?
llama.go - llama.go is like llama.cpp in pure Golang!
c-examples - Example C code
laser - The HPC toolbox: fused matrix multiplication, convolution, data-parallel strided tensor primitives, OpenMP facilities, SIMD, JIT Assembler, CPU detection, state-of-the-art vectorized BLAS for floats and integers
weave - A state-of-the-art multithreading runtime: message-passing based, fast, scalable, ultra-low overhead
tarantool - Get your data in RAM. Get compute close to data. Enjoy the performance.
blis - BLAS-like Library Instantiation Software Framework
related_post_gen - Data Processing benchmark featuring Rust, Go, Swift, Zig, Julia etc.
1brc - 1BRC in .NET among fastest on Linux
BenchmarkDotNet - Powerful .NET library for benchmarking
Nuitka-Action - Action to build with Nuitka on GitHub in your workflows
Iron python - Implementation of the Python programming language for .NET Framework; built on top of the Dynamic Language Runtime (DLR).
Pyjion - Pyjion - A JIT for Python based upon CoreCLR