Unleash the power of performance by comparing your code. In other words, only use your champion. Stop guessing, start winning. Are you juggling multiple code snippets for the same task, unsure which one truly reigns supreme? Performance testing is your secret weapon.
検索の応答性能を維持するための Benchmarking Automation | メルカリエンジニアリング
※この記事は、"Blog Series of Introduction of Developer Productivity Engineering at Mercari" の一環で書かれています。 はじめに こんにちは、メルカリMicroservices SREチームの藤本(@jimo1001)です。 私は Embedded SRE としてメルカリJPの検索に関連するマイクロサービスを提供している サーチインフラチームに入り、サービスの信頼性向上やインフラ周りの自動化に従事しています。今回は、メルカリの商品検索の応答性能を維持するための Benchmarking Automation の取り組みについて紹介したいと思います。 検索基盤のアーキテクチャ まず、検索基盤のアーキテクチャについて簡単に説明します。主要なコンポーネントに絞ってシンプルに表現したものが以下の図になります。 各コンポー
Benchmarking CRuby, MJIT, YJIT, JRuby and TruffleRuby In this blog post we benchmark many Ruby versions and the latest Ruby Just-in-Time compilers (JITs) on the newest Ruby benchmark suite, yjit-bench. As a teaser, the geometric mean speedups compared to CRuby 3.1 on these 14 benchmarks are: MJIT 1.26x, YJIT 1.39x, JRuby 1.86x and TruffleRuby 6.23x. Read on to find more about the benchmarks and ga
Some backgroundIf you’re a developer looking to build a reliable, memory safe, high performance application today, Rust & Go are surely your options. If you’re looking to get even more performance out of your internal applications, you might also want to look at using gRPC instead of a normal REST api. All these are solutions to reducing your computational overhead. I try to compare various librar
GitHub - utkusen/reqstress: a benchmarking&stressing tool that can send raw HTTP requests
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Benchmarking low-level I/O: C, C++, Rust, Golang, Java, Python
This post is a continuation of Measuring network service performance. When my computer doesn’t have an Internet connection, I find that there is not much I can do with it. Indeed, we mostly use our laptops and smartphones to access information stored or generated somewhere else. It’s even hard to imagine the utility of non-user facing apps without network communication. While the proportion of I/O
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Benchmarking AWS, Azure, and GCP in the 2021 Cloud Report. Who came out on top?
The 2021 Cloud Report stands on benchmarks. Now in its third year, our report is more precise than ever, capturing an evaluation of Amazon Web Services (AWS), Microsoft Azure (Azure), and Google Cloud Platform (GCP) that tells realistic and universal performance stories on behalf of mission-critical OLTP applications. Our intention is to help our customers and any builder of OLTP applications unde
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Learn Docker With My Newest Course Dive into Docker takes you from "What is Docker?" to confidently applying Docker to your own projects. It's packed with best practices and examples. Start Learning Docker → Updated on July 10th, 2018 in #docker Benchmarking Debian vs Alpine as a Base Docker ImageMost official Docker images offer both Debian and Alpine based images but there's some surprising perf
GitHub - six-ddc/plow: A high-performance HTTP benchmarking tool that includes a real-time web UI and terminal display
Plow is an HTTP(S) benchmarking tool, written in Golang. It uses excellent fasthttp instead of Go's default net/http due to its lightning fast performance. Plow runs at a specified connections(option -c) concurrently and real-time records a summary statistics, histogram of execution time and calculates percentiles to display on Web UI and terminal. It can run for a set duration( option -d), for a
CI for performance: Reliable benchmarking in noisy environments
CI for performance: Reliable benchmarking in noisy environments by Itamar Turner-Trauring Last updated 23 Oct 2022, originally created 02 Dec 2020 You have some code—whether it’s Python, Rust, Java, or some other language—whose speed you want to measure over time. Ideally you want it to get faster, but at the very least you don’t want to get any slower. So you write a benchmark, and now you need t
Announcing Divan! Divan is a Rust framework for quick comfy benchmarking, featuring: Simple easy-to-use interface Allocation profiling Generic parameters Thread contention insights Divan currently has 706 GitHub stars and is used by 269 projects. Get started easily with examples that span from introductory to advanced scenarios. The entire example benchmark suite compiles and runs in 40 seconds on
Advances in multicore processors and accelerators have opened the flood gates to greater exploration and application of machine learning techniques to a variety of applications. These advances, along with breakdowns of several trends including Moore's Law, have prompted an explosion of processors and accelerators that promise even greater computational and machine learning capabilities. These proc
Benchmarking Ruby 2.5 to 3.1
This is another Ruby comparison benchmark, in the tradition of 2016, 2017 and 2020. This christmas Ruby 3.1.0 was released, featuring the brand-new YJIT just in time compiler. Pre-liminary benchmarks showed noticeable performance benefits for HexaPDF, so let’s see what the final version brings. The Benchmark Setup I will be using the same applications/libraries as last time (look there if you need
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GitHub - evanwashere/mitata: cross-runtime benchmarking lib and cli
import { run, bench, group, baseline } from 'mitata'; // deno // import { ... } from 'npm:mitata'; // d8/jsc // import { ... } from '<path to mitata>/src/cli.mjs'; bench('noop', () => {}); bench('noop2', () => {}); group('group', () => { baseline('baseline', () => {}); bench('Date.now()', () => Date.now()); bench('performance.now()', () => performance.now()); }); group({ name: 'group2', summary: f
Benford’s Law⌗ After watching a Netflix documentary about Benford’s Law (“connected” Season 1 Episode 4 “Digits”), I got really interested in it because it can help detect fraud in e.g. balance sheets. So I searched my favorite programming languages and detected that there is no npm Package for checking Benford’s Law. Sooo … Long story short: I created nearly identical libraries for doing a Benfor
GitHub - tinylibs/tinybench: 🔎 A simple, tiny and lightweight benchmarking library!
import { Bench } from 'tinybench'; const bench = new Bench({ time: 100 }); bench .add('faster task', () => { console.log('I am faster') }) .add('slower task', async () => { await new Promise(r => setTimeout(r, 1)) // we wait 1ms :) console.log('I am slower') }) .todo('unimplemented bench') await bench.warmup(); // make results more reliable, ref: https://github.com/tinylibs/tinybench/pull/50 await
What I Learnt from Benchmarking Http4k, Ktor (Kotlin) and Actix v2, v3 (Rust) Microservices
Back in spring 2020 at GoOut, we were looking to replace our Spring-Tomcat duo by a more lightweight framework to power our future Kotlin microservices. We did some detailed (at times philosophical) theoretical comparisons that I much enjoyed, but these cannot substitute a hands-on experience. We decided to implement proof-of-concept microservices using the most viable frameworks, stressing them i
Benchmarking Ruby 2.6 to 3.2
It’s this time of the year again where Ruby is released and everyone asks: Is it faster? You will find out below! And if you are interested, you can compare the results to the previous installations of this post for the years 2016, 2017, 2020 and 2021. This christmas Ruby 3.2.0 was released, featuring improvements across the board. The YJIT just in time compiler has been ported to Rust and can now
Benchmarking String Literal ("") vs Template Literal (``) - using Performance.now()
Benchmarking String Literal ("") vs Template Literal (``) - using Performance.now()
GitHub - FranxYao/chain-of-thought-hub: Benchmarking large language models' complex reasoning ability with chain-of-thought prompting
"A fantasy graph illustrating a chain of stars in a dark night with blue sky, digital art, super resolution". Midjourney V5 By Yao Fu, Litu Ou, Mingyu Chen, Yuhao Wan, Hao Peng, Tushar Khot, Wenhu Chen From University of Edinburgh, University of Washington, Allen Institute for AI, University of Waterloo [paper] [blog] [twitter] Recently, there are a lot of progress in LLMs. Many claim that a small
Chatbot Arena: Benchmarking LLMs in the Wild with Elo Ratingsby: Lianmin Zheng*, Ying Sheng*, Wei-Lin Chiang, Hao Zhang, Joseph E. Gonzalez, Ion Stoica, May 03, 2023 We present Chatbot Arena, a benchmark platform for large language models (LLMs) that features anonymous, randomized battles in a crowdsourced manner. In this blog post, we are releasing our initial results and a leaderboard based on t
Benchmarking principal component analysis for large-scale single-cell RNA-sequencing - Genome Biology
Research Open access Published: 20 January 2020 Benchmarking principal component analysis for large-scale single-cell RNA-sequencing Koki Tsuyuzaki ORCID: orcid.org/0000-0003-3797-21481,2, Hiroyuki Sato ORCID: orcid.org/0000-0003-1399-81403, Kenta Sato ORCID: orcid.org/0000-0002-6569-55741,4 & …Itoshi Nikaido ORCID: orcid.org/0000-0002-7261-25701,5 Show authors Genome Biology volume 21, Article nu
Continuous BenchmarkingA safer path to deliveryMake sure your deliveries are production-ready by continuously measuring your code execution speed. Prevent UX RegressionsEvery millisecond counts to ensure an optimal experience for your users. Check your latencies and make sure your users will not be affected by your new deliveries. Avoid Production IssuesSome lines of code are critical hidden threa
Each public benchmark has its own instructions on how to use. For example, to use the Image Classification on ImageNet benchmark on your model in a framework-independent way, create a sotabench.py file like this: Example sotabench.py structure from sotabencheval.image_classification import ImageNetEvaluator evaluator = ImageNetEvaluator( # automatically compare to this paper model_name='ResNeXt-10
Benchmarking The Linux Mitigated Performance For Retbleed: It's Painful - Phoronix
Show Your Support: Did you know that the hundreds of articles written on Phoronix each month are mostly authored by one individual? Phoronix.com doesn't have a whole news room with unlimited resources and relies upon people reading our content without blocking ads and alternatively by people subscribing to Phoronix Premium for our ad-free service with other extra features. Benchmarking The Linux M
AWS Lambda allows us to execute code in the cloud without needing to provision anything. In the past few years, it has become increasignly well-known thanks to the rise of serverless applications. As an addition to all the available runtimes in AWS Lambda, AWS announced Custom Runtimes at Re:Invent 2018. They released open-source custom runtimes for C++ and Rust, while some some AWS partners also
Ruby/Rails Benchmarking and Profiling with TDD
大阪Ruby会議02での発表資料です https://regional.rubykaigi.org/osaka02/Read less
Benchmarking read/write speed on Amazon Aurora, classic RDS and local disk
I was curious to compare the read/write speed of the different storage options for a PostgreSQL database on AWS, so I made this benchmark in which I tested Amazon Aurora PostgreSQL, the classic PostgreSQL on Amazon RDS, and a manually-installed PostgreSQL on a EC2 machine with a local SSD disk. The different optionsClassic RDS: If you want to store a PostgreSQL database on AWS, the most obvious an
Ruby 3.2.0 was released today! This is a perfect moment to check it out and see how YJIT behaves. I tested it out against Hanami test suite and then ran a bunch of benchmarks that we have in dry-rb projects and the results are absolutely fascinating! Hanami test suiteThis is a really representative test suite because it uses Hanami applications fully configured and tests a lot of the framework top
Benchmarking cloud data warehouse BigQuery to scale fast | Google Cloud Blog
Why Verizon Media picked BigQuery for scale, performance and cost As the owner of Analytics, Monetization and Growth Platforms at Yahoo, one of the core brands of Verizon Media, I'm entrusted to make sure that any solution we select is fully tested across real-world scenarios. Today, we just completed a massive migration of Hadoop and enterprise data warehouse (EDW) workloads to Google Cloud’s Big
Benchmarking Quantized Mobile Speech Recognition Models with PyTorch Lightning and Grid
PyTorch Lightning enables you to rapidly train models while not worrying about boilerplate. While this makes training easier, in practice models are not trained for the sake of training models but rather for deploying to production applications. In this fourth and final part of the tutorial, we summarize our findings from the first three parts (Training a baseline model, Background on Quantization
GitHub - yyx990803/vite-vs-next-turbo-hmr: Benchmarking Vite vs. Next + turbopack HMR performance
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Our Transformers library implements several state-of-the-art transformer architectures used for NLP tasks like text classification, information extraction, question answering, and text generation. It is used by researchers and companies alike, offering PyTorch and TensorFlow front-ends. Since the release of our TensorFlow implementation, we have been working on productionizing the models and makin
Benchmarking, Profiling, and Optimizing JavaScript libraries
Introduction I wish to bring you with me on a journey to learn about optimizing a library for localization, I would like to share my learnings with you on benchmarking, profiling, and optimizing. At Swissquote Bank, we use client-composed micro-frontends, and our platforms are usually available in at least 8 languages. To support this requirement, we had to build a translation/localization library
Apache Kafka® is one of the most popular event streaming systems. There are many ways to compare messaging systems in this space, but one thing everyone cares about is performance. Kafka is known to be fast, but how fast is it today, and how does it stack up against other systems? Here's a complete performance test across Kafka, RabbitMQ, and Pulsar on the latest cloud hardware. How We Compared Ra
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