GitHub - kaytu-io/kaytu: The Kaytu CLI helps you save on cloud costs by finding the perfect server sizes. Kaytu analyzes historical usage and provides tailored recommendations, ensuring you only pay for the resources you need.
Ease of use: One-line command. Use without modifying workloads or making configuration changes. Base on actual Usage: Analyzes the past seven days of usage from Cloud native monitoring (CloudWatch), including advanced AWS CloudWatch metrics (where available). Customize: Optimize for region, CPU, memory, network performance, storage, licenses, and more to match your specific requirements. Secure -
Software optimization resources. C++ and assembly. Windows, Linux, BSD, Mac OS X
See also my blog Contents Optimization manuals Vector class library Object file converter and disassembler Subroutine library ForwardCom: An open standard instruction set for high performance microprocessors Test programs for measuring clock cycles in C++ and assembly code Floating point exception tracking through NAN propagation CPUID manipulation program Links Optimization manuals This series of
Small String Optimization で Rust ライブラリ ratatui を最適化した話 - はやくプログラムになりたい
最近 ratatui という crate に Small String Optimization を利用した最適化を入れたので，その話を書きます． 目次 Small String Optimization (SSO) とは（SSO を既に知っている人は読み飛ばして大丈夫です） Rust で SSO を適用した文字列型を提供する crate 比較 SSO を利用して ratatui のメモリ効率と実行効率を最適化した話 compact_str crate の実装の最適化の話 インラインストレージに24バイト全てを使える理由 隙間最適化のための工夫 説明を簡潔にするため，特に断りが無い場合 64bit アーキテクチャを前提とします． Small String Optimization (SSO) とは Rust の可変長文字列型 String は文字列バッファへのポインタ，文字列の長さ，バッフ
徐々に高度になるリングバッファの話 - Software Transactional Memo
リングバッファのイメージ図 1. リングバッファとは何か 機能的にはFirst In First Out (FIFO)とも呼ばれるキューの一種であるが、リング状にバッファを置いてそれの中でReadとWriteのインデックスがグルグルと回る構造をとる事によって容量に上限ができることと引き換えに高速な読み書き速度を得たものである。キューを単に実装するだけなら山ほど方法があって線形リストを使ってもいいしスタックを2つ使っても原理的には可能だ。その中でもリングバッファを用いた方法の利点はひとえに性能の高さでありメモリ確保などを行わないお陰でシステム系の様々な場所で使われている。 これの実装自体は情報系の大学生の演習レベルの難度であるが少し奥が深い。まずリングバッファのスタンダードなインタフェースと実装は以下のようなものである。 class RingBuffer { public: explicit
Practical libc-free threading on Linux
Suppose you’re not using a C runtime on Linux, and instead you’re programming against its system call API. It’s long-term and stable after all. Memory management and buffered I/O are easily solved, but a lot of software benefits from concurrency. It would be nice to also have thread spawning capability. This article will demonstrate a simple, practical, and robust approach to spawning and managing
Size Optimization Tricks
This blog post will cover some of the tricks I've used in the past to make c / c++ / python binaries smaller using x86 assembly. Much of it will revolve around the Cosmopolitan codebase, since I recently received feedback from the ELKS project that they love the code and want to hear more about how the tricks cosmo uses can potentially improve projects as intriguing as a i8086 Linux port. In many
jott - interactive_optimization
# `loop_tool`'s Interactive Loop Optimization *by [@bwasti](https://twitter.com/bwasti) (all thoughts in this writeup are my own)* **** I'm working on a [loop tool](https://github.com/facebookresearch/loop_tool) to interactively optimize dense linear algebra workloads. Below is a demo of a user optimizing a program to be 10x faster than [NumPy](https://numpy.org): <center> <video playsinline loop
Intro
Intro Optimizing software for performance is fun. Loads of fun. And sometimes incredibly frustrating. It’s also something to do on a long intercontinental flight to keep from watching that same movie again while being incredibly bored.. In this post I’m going to show the steps I went through to optimize a specific Linux kernel function, and how I got to a set of final results. As with all performa
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https://tratt.net/laurie/blog/2023/four_kinds_of_optimisation.html
Analyzing Data 170,000x Faster with Python
aegraphs
Faster than Rust and C++: the PERFECT hash table
Practical Optimizations
数理・計算科学特論B プログラミング言語処理系の最先端実装技術 第6講 inliningとdevirtualization
Loop Optimizations: interpreting the compiler optimization report - Johnny's Software Lab
I wrote a peephole optimizer (for QBE), part 1 - Dr. Brian Robert Callahan
GitHub - garritfra/qbe-rs: QBE IR in natural Rust data structures
FOSDEM 2022 - Introduction to qbe
Optimization story - quantum mechanics simulation speedup
Nadav Rotem on Twitter: "I spent some time optimizing memset and memcpy in x86 assembly. Here are a few interesting things about memset and memcpy. 1/"
