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AntoinePrvAntoinePrv
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Add simple notebook
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‎.github/workflows/deploy-jupyterlite.yml‎

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name: Jupyterlite deployment
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on:
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workflow_dispatch:
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pull_request:
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push:
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branches:
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- master
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permissions:
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contents: read
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pages: write
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id-token: write
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jobs:
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build:
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runs-on: ubuntu-24.04
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steps:
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- name: Checkout code
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uses: actions/checkout@v6
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- uses: mamba-org/setup-micromamba@v3
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with:
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cache-environment: true
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post-cleanup: 'all'
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environment-name: jupyterlite-builer
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create-args: jupyter jupyterlite-core jupyterlite-xeus
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- name: Build WASM environment
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run: |
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micromamba create -p ./wasm-env \
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--platform=emscripten-wasm32 \
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--channel https://prefix.dev/emscripten-forge-4x \
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--channel https://prefix.dev/conda-forge \
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xeus-cpp=0.10
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- name: Build Jupyterlite distribution
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run: |
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micromamba run -n jupyterlite-builer \
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jupyter lite build \
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--XeusAddon.prefix=./wasm-env \
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--contents include/ \
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--contents notebooks/ \
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--output-dir ./dist
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- name: Upload artifact
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uses: actions/upload-pages-artifact@v3
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with:
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path: ./dist
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deploy:
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needs: build
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if: github.ref == 'refs/heads/master'
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permissions:
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pages: write
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id-token: write
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environment:
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name: github-pages
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url: ${{ steps.deployment.outputs.page_url }}
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runs-on: ubuntu-24.04
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steps:
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- name: Deploy to GitHub Pages
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id: deployment
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uses: actions/deploy-pages@v4

‎notebooks/simple.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2efd7be2-5ea7-42cb-a2e2-9ddb409bc893",
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"metadata": {},
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"outputs": [],
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"source": [
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"#include <cstddef>\n",
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"#include <vector>\n",
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"#include <random>\n",
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"#include <iostream>\n",
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"\n",
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"#include <xsimd/xsimd.hpp>"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6f498bff-4366-4e1c-ab25-3d3589740e78",
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"metadata": {},
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"source": [
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"Comparing two implementation of element wise mean."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "cf6b2c85-1427-49a3-8bef-0340601319e8",
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"metadata": {},
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"outputs": [],
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"source": [
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"std::vector<double> random_vector(std::size_t n)\n",
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"{\n",
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" static std::mt19937 gen(42);\n",
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" std::uniform_real_distribution<double> dist(0.0, 1.0);\n",
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" std::vector<double> v(n);\n",
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" for (auto& x : v)\n",
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" {\n",
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" x = dist(gen);\n",
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" }\n",
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" return v;\n",
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"}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a2ab6177-60e2-4d65-b323-51a6a21ad530",
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"metadata": {},
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"outputs": [],
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"source": [
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"std::vector<double> mean_scalar(const std::vector<double>& a, const std::vector<double>& b)\n",
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"{\n",
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" std::size_t size = a.size();\n",
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" std::vector<double> res(size);\n",
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" for (std::size_t i = 0; i < size; ++i)\n",
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" {\n",
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" res[i] = (a[i] + b[i]) / 2;\n",
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" }\n",
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" return res;\n",
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"}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "082ed2cd-273a-4c3c-a206-03b1efe49482",
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"metadata": {},
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"outputs": [],
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"source": [
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"std::vector<double> mean_simd(const std::vector<double>& a, const std::vector<double>& b)\n",
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"{\n",
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" using b_type = xsimd::batch<double>;\n",
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" std::size_t inc = b_type::size;\n",
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" std::size_t size = a.size();\n",
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" std::vector<double> res(size);\n",
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"\n",
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" // size for which the vectorization is possible\n",
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" std::size_t vec_size = size - size % inc;\n",
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" for (std::size_t i = 0; i < vec_size; i += inc)\n",
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" {\n",
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" b_type avec = b_type::load_unaligned(&a[i]);\n",
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" b_type bvec = b_type::load_unaligned(&b[i]);\n",
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" b_type rvec = (avec + bvec) / 2;\n",
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" rvec.store_unaligned(&res[i]);\n",
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" }\n",
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" // Remaining part that cannot be vectorize\n",
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" for (std::size_t i = vec_size; i < size; ++i)\n",
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" {\n",
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" res[i] = (a[i] + b[i]) / 2;\n",
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" }\n",
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" return res;\n",
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"}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "5959864d",
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"metadata": {},
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"outputs": [],
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"source": [
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"// Print v[begin:end] as grayscale ASCII blocks (dark = 0, light = 1).\n",
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"void print_grayscale(const std::vector<double>& v, std::size_t begin, std::size_t end)\n",
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"{\n",
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" for (std::size_t i = begin; i < end; ++i)\n",
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" {\n",
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" // map value in [0, 1] to a 24-bit truecolor gray (256 levels)\n",
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" int gray = static_cast<int>(v[i] * 255);\n",
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" std::cout << \"\\033[38;2;\" << gray << \";\" << gray << \";\" << gray << \"m█\";\n",
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" }\n",
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" std::cout << \"\\033[0m\\n\";\n",
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"}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2068e201",
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"metadata": {},
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"outputs": [],
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"source": [
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"std::size_t n = 1000;\n",
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"std::vector<double> const a = random_vector(n);\n",
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"std::vector<double> const b = random_vector(n);"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9d5a7adc-5b55-4224-a4ca-5e438d1fc4d4",
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"metadata": {},
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"outputs": [],
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"source": [
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"auto const res_simd = mean_simd(a, b);"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d279cd74-c397-4ea8-af15-3745f043f656",
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"metadata": {},
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"outputs": [],
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"source": [
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"auto const res_scalar = mean_scalar(a, b);"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a04ae145",
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"metadata": {},
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"source": [
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"Each block is a value in `[0, 1]` shown as a grayscale shade (dark = low, light = high). The vector is drawn in chunks of 100, each chunk stacking input `a`, the SIMD mean, the scalar mean, and input `b`. The two mean rows should look identical, each sitting between its inputs."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "59ca84ad-8b97-4523-af0d-d3eca01b41d0",
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"metadata": {},
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"outputs": [],
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"source": [
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"std::size_t const width = 100;\n",
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"for (std::size_t begin = 0; begin < n; begin += width)\n",
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"{\n",
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" std::size_t end = std::min(begin + width, n);\n",
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" std::cout << \"a: \";\n",
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" print_grayscale(a, begin, end);\n",
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" std::cout << \"simd: \";\n",
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" print_grayscale(res_simd, begin, end);\n",
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" std::cout << \"scalar: \";\n",
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" print_grayscale(res_scalar, begin, end);\n",
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" std::cout << \"b: \";\n",
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" print_grayscale(b, begin, end);\n",
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" std::cout << \"\\n\";\n",
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"}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "90b86fb4-0426-43fd-b5fc-8626ef8732a5",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "C++17",
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"language": "cpp",
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"name": "xcpp17"
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},
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"language_info": {
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"codemirror_mode": "text/x-c++src",
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"file_extension": ".cpp",
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"mimetype": "text/x-c++src",
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"name": "C++",
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"nbconvert_exporter": "",
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"pygments_lexer": "",
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"version": "cxx17"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

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