{"id":1621,"date":"2025-03-24T08:50:14","date_gmt":"2025-03-23T23:50:14","guid":{"rendered":"https:\/\/dexall.co.jp\/articles\/?p=1621"},"modified":"2025-03-24T08:50:14","modified_gmt":"2025-03-23T23:50:14","slug":"openmp%e3%83%9e%e3%82%b9%e3%82%bf%e3%83%bc%e3%82%ac%e3%82%a4%e3%83%89%ef%bc%9a%e4%b8%a6%e5%88%97%e5%87%a6%e7%90%86%e3%82%92%e6%9c%80%e9%81%a9%e5%8c%96%e3%81%99%e3%82%8b7%e3%81%a4%e3%81%ae%e5%ae%9f","status":"publish","type":"post","link":"https:\/\/dexall.co.jp\/articles\/?p=1621","title":{"rendered":"OpenMP\u30de\u30b9\u30bf\u30fc\u30ac\u30a4\u30c9\uff1a\u4e26\u5217\u51e6\u7406\u3092\u6700\u9069\u5316\u3059\u308b7\u3064\u306e\u5b9f\u8df5\u30c6\u30af\u30cb\u30c3\u30af"},"content":{"rendered":"\n<div class=\"toc\"><br \/>\n<b>Warning<\/b>:  Undefined array key \"is_admin\" in <b>\/home\/xs392991\/dexall.co.jp\/public_html\/articles\/wp-content\/themes\/sango-theme\/library\/gutenberg\/dist\/classes\/Toc.php<\/b> on line <b>116<\/b><br \/>\n<br \/>\n<b>Warning<\/b>:  Undefined array key \"is_category_top\" in <b>\/home\/xs392991\/dexall.co.jp\/public_html\/articles\/wp-content\/themes\/sango-theme\/library\/gutenberg\/dist\/classes\/Toc.php<\/b> on line <b>121<\/b><br \/>\n<br \/>\n<b>Warning<\/b>:  Undefined array key \"is_top\" in <b>\/home\/xs392991\/dexall.co.jp\/public_html\/articles\/wp-content\/themes\/sango-theme\/library\/gutenberg\/dist\/classes\/Toc.php<\/b> on line <b>128<\/b><br \/>\n    <div id=\"toc_container\" class=\"sgb-toc--bullets js-smooth-scroll\" data-dialog-title=\"\u76ee\u6b21\">\n      <p class=\"toc_title\">\u76ee\u6b21 <\/p>\n      <ul class=\"toc_list\">  <li class=\"first\">    <a href=\"#i-0\">OpenMP\u3068\u306f\uff1a\u9ad8\u901f\u306a\u4e26\u5217\u51e6\u7406\u3092\u5b9f\u73fe\u3059\u308b\u305f\u3081\u306e\u57fa\u790e\u77e5\u8b58<\/a>    <ul class=\"menu_level_1\">      <li class=\"first\">        <a href=\"#i-1\">\u4e26\u5217\u51e6\u7406\u306e\u30d1\u30e9\u30c0\u30a4\u30e0\u30b7\u30d5\u30c8\u3092\u5b9f\u73fe\u3059\u308bOpenMP<\/a>      <\/li>      <li class=\"last\">        <a href=\"#i-2\">OpenMP\u304c\u9078\u3070\u308c\u308b3\u3064\u306e\u7406\u7531<\/a>      <\/li>    <\/ul>  <\/li>  <li>    <a href=\"#i-3\">OpenMP\u3092\u4f7f\u3063\u305f\u4e26\u5217\u51e6\u7406\u306e\u5b9f\u88c5\u624b\u9806<\/a>    <ul class=\"menu_level_1\">      <li class=\"first\">        <a href=\"#i-4\">\u958b\u767a\u74b0\u5883\u306e\u30bb\u30c3\u30c8\u30a2\u30c3\u30d7\u3068\u57fa\u672c\u8a2d\u5b9a<\/a>      <\/li>      <li>        <a href=\"#i-5\">\u4e26\u5217\u5316\u306e\u305f\u3081\u306e\u6307\u793a\u5b50\uff08\u30c7\u30a3\u30ec\u30af\u30c6\u30a3\u30d6\uff09\u306e\u4f7f\u3044\u65b9<\/a>      <\/li>      <li class=\"last\">        <a href=\"#i-6\">\u30b9\u30ec\u30c3\u30c9\u6570\u306e\u5236\u5fa1\u3068\u30b9\u30b1\u30b8\u30e5\u30fc\u30ea\u30f3\u30b0<\/a>      <\/li>    <\/ul>  <\/li>  <li>    <a href=\"#i-7\">OpenMP\u306b\u3088\u308b\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u6700\u9069\u5316\u306e7\u3064\u306e\u30c6\u30af\u30cb\u30c3\u30af<\/a>    <ul class=\"menu_level_1\">      <li class=\"first\">        <a href=\"#i-8\">1. \u30c7\u30fc\u30bf\u5171\u6709\u3068\u540c\u671f\u306e\u6700\u9069\u5316<\/a>      <\/li>      <li>        <a href=\"#i-9\">2. \u30eb\u30fc\u30d7\u4e26\u5217\u5316\u306e\u52b9\u7387\u5316<\/a>      <\/li>      <li>        <a href=\"#i-10\">3. \u30bf\u30b9\u30af\u4e26\u5217\u6027\u306e\u6d3b\u7528<\/a>      <\/li>      <li>        <a href=\"#i-11\">4. \u30e1\u30e2\u30ea\u30a2\u30af\u30bb\u30b9\u306e\u6700\u9069\u5316<\/a>      <\/li>      <li>        <a href=\"#i-12\">5. \u8ca0\u8377\u5206\u6563\u306e\u8abf\u6574<\/a>      <\/li>      <li>        <a href=\"#i-13\">6. SIMD \u30b3\u30de\u30f3\u30c9\u306e\u6d3b\u7528<\/a>      <\/li>      <li class=\"last\">        <a href=\"#i-14\">7. \u30b9\u30b1\u30fc\u30e9\u30d3\u30ea\u30c6\u30a3\u306e\u78ba\u4fdd<\/a>      <\/li>    <\/ul>  <\/li>  <li>    <a href=\"#i-15\">OpenMP\u306b\u304a\u3051\u308b\u4e00\u822c\u7684\u306a\u30a8\u30e9\u30fc\u3068\u305d\u306e\u89e3\u6c7a\u7b56<\/a>    <ul class=\"menu_level_1\">      <li class=\"first\">        <a href=\"#i-16\">\u30c7\u30fc\u30bf\u30ea\u30bd\u30fc\u30b9\u3092\u9632\u3050\u305f\u3081\u306e\u540c\u671f\u5236\u5fa1<\/a>      <\/li>      <li>        <a href=\"#i-17\">\u30e1\u30e2\u30ea\u30ea\u30fc\u30af\u3092\u9632\u3050\u305f\u3081\u306e\u30d9\u30b9\u30c8\u30d7\u30e9\u30af\u30c6\u30a3\u30b9<\/a>      <\/li>      <li class=\"last\">        <a href=\"#i-18\">\u30c7\u30c3\u30c9\u30ed\u30c3\u30af\u306e\u56de\u907f\u65b9\u6cd5<\/a>      <\/li>    <\/ul>  <\/li>  <li>    <a href=\"#i-19\">OpenMP\u306e\u5b9f\u8df5\u6d3b\u7528\u4f8b<\/a>    <ul class=\"menu_level_1\">      <li class=\"first\">        <a href=\"#i-20\">\u753b\u50cf\u51e6\u7406\u3067\u306e\u5b9f\u88c5\u4f8b<\/a>      <\/li>      <li>        <a href=\"#i-21\">\u6570\u5024\u8a08\u7b97\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u6700\u9069\u5316\u4e8b\u4f8b<\/a>      <\/li>      <li class=\"last\">        <a href=\"#i-22\">\u5927\u898f\u6a21\u30c7\u30fc\u30bf\u51e6\u7406\u3067\u306e\u6d3b\u7528\u65b9\u6cd5<\/a>      <\/li>    <\/ul>  <\/li>  <li>    <a href=\"#i-23\">OpenMP\u306e\u6027\u80fd\u8a55\u4fa1\u3068\u6700\u9069\u5316\u306e\u305f\u3081\u306e\u30c4\u30fc\u30eb<\/a>    <ul class=\"menu_level_1\">      <li class=\"first\">        <a href=\"#i-24\">\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u30d7\u30ed\u30d5\u30a1\u30a4\u30ea\u30f3\u30b0\u30c4\u30fc\u30eb\u306e\u6d3b\u7528\u6cd5<\/a>      <\/li>      <li class=\"last\">        <a href=\"#i-25\">\u30dc\u30c8\u30eb\u30cd\u30c3\u30af\u306e\u7279\u5b9a\u3068\u89e3\u6c7a\u624b\u9806<\/a>      <\/li>    <\/ul>  <\/li>  <li class=\"last\">    <a href=\"#i-26\">OpenMP\u306e\u5c06\u6765\u5c55\u671b\u3068\u6700\u65b0\u30c8\u30ec\u30f3\u30c9<\/a>    <ul class=\"menu_level_1\">      <li class=\"first\">        <a href=\"#i-27\">OpenMP 5.0\u4ee5\u964d\u306e\u65b0\u6a5f\u80fd\u3068\u6d3b\u7528\u6cd5<\/a>      <\/li>      <li class=\"last\">        <a href=\"#i-28\">GPGPU\u3068\u306e\u9023\u643a\u306b\u3088\u308b\u3055\u3089\u306a\u308b\u6700\u9069\u5316<\/a>      <\/li>    <\/ul>  <\/li><\/ul>\n      <a href=\"#\" class=\"sgb-toc-button js-toc-button\" rel=\"nofollow\" data-open-dialog=\"true\"><i class=\"fa fa-list\"><\/i><span class=\"sgb-toc-button__text\">\u76ee\u6b21\u3078<\/span><\/a>\n    <\/div><\/div><h2 class=\"wp-block-heading\" id=\"i-0\">OpenMP\u3068\u306f\uff1a\u9ad8\u901f\u306a\u4e26\u5217\u51e6\u7406\u3092\u5b9f\u73fe\u3059\u308b\u305f\u3081\u306e\u57fa\u790e\u77e5\u8b58<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"i-1\">\u4e26\u5217\u51e6\u7406\u306e\u30d1\u30e9\u30c0\u30a4\u30e0\u30b7\u30d5\u30c8\u3092\u5b9f\u73fe\u3059\u308bOpenMP<\/h3>\n\n\n\n<p>OpenMP\uff08Open Multi-Processing\uff09\u306f\u3001C++\u3092\u542b\u3080\u8907\u6570\u306e\u30d7\u30ed\u30b0\u30e9\u30df\u30f3\u30b0\u8a00\u8a9e\u3067\u4f7f\u7528\u3067\u304d\u308b\u3001\u5171\u6709\u30e1\u30e2\u30ea\u578b\u4e26\u5217\u30d7\u30ed\u30b0\u30e9\u30df\u30f3\u30b0\u306e\u305f\u3081\u306e\u696d\u754c\u6a19\u6e96API\u3067\u3059\u3002\u30de\u30eb\u30c1\u30b3\u30a2\u30d7\u30ed\u30bb\u30c3\u30b5\u306e\u6027\u80fd\u3092\u6700\u5927\u9650\u306b\u6d3b\u7528\u3059\u308b\u305f\u3081\u306b\u8a2d\u8a08\u3055\u308c\u305f\u672c\u30d5\u30ec\u30fc\u30e0\u30ef\u30fc\u30af\u306f\u3001\u4ee5\u4e0b\u306e\u7279\u5fb4\u3092\u6301\u3063\u3066\u3044\u307e\u3059\uff1a<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>\u5ba3\u8a00\u7684\u306a\u4e26\u5217\u5316<\/strong>: \u30d7\u30e9\u30b0\u30de\u30c7\u30a3\u30ec\u30af\u30c6\u30a3\u30d6\u3092\u4f7f\u7528\u3059\u308b\u3053\u3068\u3067\u3001\u65e2\u5b58\u306e\u30b3\u30fc\u30c9\u306b\u6700\u5c0f\u9650\u306e\u5909\u66f4\u3067\u4e26\u5217\u51e6\u7406\u3092\u5b9f\u88c5\u3067\u304d\u307e\u3059<\/li>\n\n\n\n<li><strong>\u79fb\u690d\u6027\u306e\u9ad8\u3055<\/strong>: \u7570\u306a\u308b\u30d7\u30e9\u30c3\u30c8\u30d5\u30a9\u30fc\u30e0\u3084\u30b3\u30f3\u30d1\u30a4\u30e9\u9593\u3067\u4e00\u8cab\u3057\u305f\u52d5\u4f5c\u3092\u4fdd\u8a3c\u3057\u307e\u3059<\/li>\n\n\n\n<li><strong>\u6bb5\u968e\u7684\u306a\u4e26\u5217\u5316<\/strong>: \u30b3\u30fc\u30c9\u306e\u4e00\u90e8\u5206\u304b\u3089\u5f90\u3005\u306b\u4e26\u5217\u5316\u3092\u9032\u3081\u3089\u308c\u308b\u67d4\u8edf\u6027\u304c\u3042\u308a\u307e\u3059<\/li>\n<\/ul>\n\n\n\n<p>\u57fa\u672c\u7684\u306a\u4f7f\u7528\u4f8b\u3092\u898b\u3066\u307f\u307e\u3057\u3087\u3046\uff1a<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">#include &lt;omp.h&gt;\n#include &lt;vector&gt;\n#include &lt;iostream&gt;\n\nvoid parallel_sum(std::vector&lt;int&gt;&amp; data) {\n    int sum = 0;\n    \/\/ \u914d\u5217\u306e\u8981\u7d20\u3092\u4e26\u5217\u306b\u8db3\u3057\u5408\u308f\u305b\u308b\n    #pragma omp parallel for reduction(+:sum)\n    for(size_t i = 0; i &lt; data.size(); ++i) {\n        sum += data[i];    \/\/ \u5404\u30b9\u30ec\u30c3\u30c9\u304c\u5b89\u5168\u306b\u52a0\u7b97\u3092\u5b9f\u884c\n    }\n    std::cout &lt;&lt; \"\u5408\u8a08: \" &lt;&lt; sum &lt;&lt; std::endl;\n}<\/pre>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"i-2\">OpenMP\u304c\u9078\u3070\u308c\u308b3\u3064\u306e\u7406\u7531<\/h3>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>\u958b\u767a\u52b9\u7387\u306e\u5411\u4e0a<\/strong><\/li>\n<\/ol>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u65e2\u5b58\u306e\u30b7\u30ea\u30a2\u30eb\u30b3\u30fc\u30c9\u3092\u6700\u5c0f\u9650\u306e\u5909\u66f4\u3067\u4e26\u5217\u5316\u3067\u304d\u307e\u3059<\/li>\n\n\n\n<li>\u30d7\u30e9\u30b0\u30de\u306b\u3088\u308b\u76f4\u611f\u7684\u306a\u5b9f\u88c5\u304c\u53ef\u80fd\u3067\u3059<\/li>\n\n\n\n<li>\u30c7\u30d0\u30c3\u30b0\u304c\u6bd4\u8f03\u7684\u5bb9\u6613\u3067\u3059<\/li>\n<\/ul>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>\u9ad8\u3044\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9<\/strong><\/li>\n<\/ol>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u30b9\u30ec\u30c3\u30c9\u30ec\u30d9\u30eb\u3067\u306e\u7d30\u304b\u306a\u5236\u5fa1\u304c\u53ef\u80fd<\/li>\n\n\n\n<li>\u30ad\u30e3\u30c3\u30b7\u30e5\u306e\u52b9\u7387\u7684\u306a\u5229\u7528\u3092\u5b9f\u73fe<\/li>\n\n\n\n<li>\u30b9\u30b1\u30fc\u30e9\u30d6\u30eb\u306a\u4e26\u5217\u51e6\u7406\u306e\u5b9f\u88c5\u3092\u30b5\u30dd\u30fc\u30c8<\/li>\n<\/ul>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>\u5145\u5b9f\u3057\u305f\u30a8\u30b3\u30b7\u30b9\u30c6\u30e0<\/strong><\/li>\n<\/ol>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u4e3b\u8981\u306a\u30b3\u30f3\u30d1\u30a4\u30e9\u3067\u30b5\u30dd\u30fc\u30c8<\/li>\n\n\n\n<li>\u8c4a\u5bcc\u306a\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u3068\u30b3\u30df\u30e5\u30cb\u30c6\u30a3<\/li>\n\n\n\n<li>\u7d99\u7d9a\u7684\u306a\u898f\u683c\u306e\u9032\u5316\u3068\u6539\u5584<\/li>\n<\/ul>\n\n\n\n<p>\u4ee5\u4e0b\u306f\u3001OpenMP\u3092\u4f7f\u7528\u3057\u305f\u4e26\u5217\u51e6\u7406\u306e\u52b9\u679c\u3092\u793a\u3059\u7c21\u5358\u306a\u4f8b\u3067\u3059\uff1a<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">#include &lt;omp.h&gt;\n#include &lt;vector&gt;\n#include &lt;chrono&gt;\n#include &lt;iostream&gt;\n\nvoid demonstrate_performance() {\n    const size_t size = 100000000;\n    std::vector&lt;double&gt; data(size);\n\n    \/\/ \u30c7\u30fc\u30bf\u306e\u521d\u671f\u5316\n    for(size_t i = 0; i &lt; size; ++i) {\n        data[i] = i;\n    }\n\n    auto start = std::chrono::high_resolution_clock::now();\n    double sum = 0.0;\n\n    \/\/ OpenMP\u306b\u3088\u308b\u4e26\u5217\u51e6\u7406\n    #pragma omp parallel for reduction(+:sum)\n    for(size_t i = 0; i &lt; size; ++i) {\n        sum += std::sin(data[i]);  \/\/ \u8a08\u7b97\u8ca0\u8377\u306e\u9ad8\u3044\u51e6\u7406\n    }\n\n    auto end = std::chrono::high_resolution_clock::now();\n    auto duration = std::chrono::duration_cast&lt;std::chrono::milliseconds&gt;(end - start);\n\n    std::cout &lt;&lt; \"\u51e6\u7406\u6642\u9593: \" &lt;&lt; duration.count() &lt;&lt; \"ms\" &lt;&lt; std::endl;\n    std::cout &lt;&lt; \"\u7d50\u679c: \" &lt;&lt; sum &lt;&lt; std::endl;\n}<\/pre>\n\n\n\n<p>OpenMP\u306e\u5c0e\u5165\u306b\u3088\u308a\u3001\u30de\u30eb\u30c1\u30b3\u30a2\u30d7\u30ed\u30bb\u30c3\u30b5\u306e\u80fd\u529b\u3092\u6700\u5927\u9650\u306b\u6d3b\u7528\u3057\u3001\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u306e\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u3092\u5927\u5e45\u306b\u5411\u4e0a\u3055\u305b\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u6b21\u306e\u30bb\u30af\u30b7\u30e7\u30f3\u3067\u306f\u3001\u5177\u4f53\u7684\u306a\u5b9f\u88c5\u624b\u9806\u306b\u3064\u3044\u3066\u8a73\u3057\u304f\u898b\u3066\u3044\u304d\u307e\u3057\u3087\u3046\u3002<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"i-3\">OpenMP\u3092\u4f7f\u3063\u305f\u4e26\u5217\u51e6\u7406\u306e\u5b9f\u88c5\u624b\u9806<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"i-4\">\u958b\u767a\u74b0\u5883\u306e\u30bb\u30c3\u30c8\u30a2\u30c3\u30d7\u3068\u57fa\u672c\u8a2d\u5b9a<\/h3>\n\n\n\n<p>OpenMP\u3092\u4f7f\u7528\u3059\u308b\u305f\u3081\u306e\u74b0\u5883\u69cb\u7bc9\u306f\u3001\u6bd4\u8f03\u7684\u30b7\u30f3\u30d7\u30eb\u3067\u3059\u3002\u4e3b\u8981\u306a\u624b\u9806\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3067\u3059\uff1a<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>\u30b3\u30f3\u30d1\u30a4\u30e9\u306e\u6e96\u5099<\/strong><\/li>\n<\/ol>\n\n\n\n<ul class=\"wp-block-list\">\n<li>GCC: <code>-fopenmp<\/code> \u30aa\u30d7\u30b7\u30e7\u30f3\u3092\u4f7f\u7528<\/li>\n\n\n\n<li>Visual Studio: \u30d7\u30ed\u30b8\u30a7\u30af\u30c8\u8a2d\u5b9a\u3067OpenMP\u30b5\u30dd\u30fc\u30c8\u3092\u6709\u52b9\u5316<\/li>\n\n\n\n<li>Clang: <code>-fopenmp<\/code> \u30aa\u30d7\u30b7\u30e7\u30f3\u3092\u4f7f\u7528<\/li>\n<\/ul>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>\u30d8\u30c3\u30c0\u30fc\u30d5\u30a1\u30a4\u30eb\u306e\u30a4\u30f3\u30af\u30eb\u30fc\u30c9<\/strong><\/li>\n<\/ol>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">#include &lt;omp.h&gt;  \/\/ OpenMP\u306e\u30d8\u30c3\u30c0\u30fc\u30d5\u30a1\u30a4\u30eb<\/pre>\n\n\n\n<ol start=\"3\" class=\"wp-block-list\">\n<li><strong>\u30b3\u30f3\u30d1\u30a4\u30eb\u30aa\u30d7\u30b7\u30e7\u30f3\u306e\u8a2d\u5b9a\u4f8b<\/strong><\/li>\n<\/ol>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\"># GCC\u306e\u5834\u5408\ng++ -fopenmp your_program.cpp -o your_program\n\n# Clang\u306e\u5834\u5408\nclang++ -fopenmp your_program.cpp -o your_program<\/pre>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"i-5\">\u4e26\u5217\u5316\u306e\u305f\u3081\u306e\u6307\u793a\u5b50\uff08\u30c7\u30a3\u30ec\u30af\u30c6\u30a3\u30d6\uff09\u306e\u4f7f\u3044\u65b9<\/h3>\n\n\n\n<p>OpenMP\u306e\u4e3b\u8981\u306a\u6307\u793a\u5b50\u3068\u305d\u306e\u4f7f\u7528\u65b9\u6cd5\u3092\u8aac\u660e\u3057\u307e\u3059\uff1a<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">#include &lt;omp.h&gt;\n#include &lt;vector&gt;\n#include &lt;iostream&gt;\n\nvoid demonstrate_directives() {\n    std::vector&lt;int&gt; data(1000, 1);\n\n    \/\/ 1. parallel\u6307\u793a\u5b50 - \u4e26\u5217\u9818\u57df\u306e\u4f5c\u6210\n    #pragma omp parallel\n    {\n        \/\/ \u3053\u306e\u30d6\u30ed\u30c3\u30af\u5185\u306e\u30b3\u30fc\u30c9\u304c\u4e26\u5217\u5b9f\u884c\u3055\u308c\u308b\n        int thread_id = omp_get_thread_num();\n        #pragma omp critical\n        std::cout &lt;&lt; \"Thread \" &lt;&lt; thread_id &lt;&lt; \" is running\" &lt;&lt; std::endl;\n    }\n\n    \/\/ 2. for\u6307\u793a\u5b50 - \u30eb\u30fc\u30d7\u306e\u4e26\u5217\u5316\n    int sum = 0;\n    #pragma omp parallel for reduction(+:sum)\n    for(int i = 0; i &lt; 1000; ++i) {\n        sum += data[i];\n    }\n\n    \/\/ 3. sections\u6307\u793a\u5b50 - \u7570\u306a\u308b\u30bf\u30b9\u30af\u306e\u4e26\u5217\u5b9f\u884c\n    #pragma omp parallel sections\n    {\n        #pragma omp section\n        {\n            \/\/ \u30bf\u30b9\u30af1\u306e\u51e6\u7406\n        }\n\n        #pragma omp section\n        {\n            \/\/ \u30bf\u30b9\u30af2\u306e\u51e6\u7406\n        }\n    }\n}<\/pre>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"i-6\">\u30b9\u30ec\u30c3\u30c9\u6570\u306e\u5236\u5fa1\u3068\u30b9\u30b1\u30b8\u30e5\u30fc\u30ea\u30f3\u30b0<\/h3>\n\n\n\n<p>\u30b9\u30ec\u30c3\u30c9\u6570\u306e\u5236\u5fa1\u3068\u30bf\u30b9\u30af\u306e\u30b9\u30b1\u30b8\u30e5\u30fc\u30ea\u30f3\u30b0\u306f\u3001\u4e26\u5217\u51e6\u7406\u306e\u52b9\u7387\u306b\u5927\u304d\u304f\u5f71\u97ff\u3057\u307e\u3059\uff1a<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">#include &lt;omp.h&gt;\n#include &lt;iostream&gt;\n\nvoid thread_control_example() {\n    \/\/ \u30b9\u30ec\u30c3\u30c9\u6570\u306e\u8a2d\u5b9a\n    omp_set_num_threads(4);  \/\/ 4\u30b9\u30ec\u30c3\u30c9\u306b\u8a2d\u5b9a\n\n    \/\/ \u52d5\u7684\u306a\u30b9\u30ec\u30c3\u30c9\u6570\u8abf\u6574\u306e\u6709\u52b9\u5316\n    omp_set_dynamic(1);\n\n    \/\/ \u30b9\u30b1\u30b8\u30e5\u30fc\u30ea\u30f3\u30b0\u65b9\u5f0f\u306e\u6307\u5b9a\n    #pragma omp parallel for schedule(dynamic, 100)\n    for(int i = 0; i &lt; 1000; ++i) {\n        \/\/ \u51e6\u7406\u5185\u5bb9\n        \/\/ dynamic\u6307\u5b9a\u306b\u3088\u308a\u3001100\u56de\u306e\u7e70\u308a\u8fd4\u3057\u3054\u3068\u306b\u52d5\u7684\u306b\u5272\u308a\u5f53\u3066\n    }\n\n    \/\/ \u30b9\u30ec\u30c3\u30c9\u6570\u306e\u53d6\u5f97\n    int num_threads = omp_get_max_threads();\n    std::cout &lt;&lt; \"\u5229\u7528\u53ef\u80fd\u306a\u6700\u5927\u30b9\u30ec\u30c3\u30c9\u6570: \" &lt;&lt; num_threads &lt;&lt; std::endl;\n\n    \/\/ nested parallelism\u306e\u5236\u5fa1\n    omp_set_nested(1);  \/\/ \u30cd\u30b9\u30c8\u3055\u308c\u305f\u4e26\u5217\u5316\u3092\u6709\u52b9\u5316\n}<\/pre>\n\n\n\n<p>\u5b9f\u88c5\u306b\u304a\u3051\u308b\u30dd\u30a4\u30f3\u30c8\uff1a<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>\u30c7\u30fc\u30bf\u4f9d\u5b58\u6027\u306e\u8003\u616e<\/strong><\/li>\n<\/ol>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u4e26\u5217\u5316\u3059\u308b\u524d\u306b\u30c7\u30fc\u30bf\u306e\u4f9d\u5b58\u95a2\u4fc2\u3092\u614e\u91cd\u306b\u5206\u6790<\/li>\n\n\n\n<li>\u5fc5\u8981\u306b\u5fdc\u3058\u3066\u9069\u5207\u306a\u540c\u671f\u6a5f\u69cb\u3092\u4f7f\u7528<\/li>\n<\/ul>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>\u30b9\u30b1\u30b8\u30e5\u30fc\u30ea\u30f3\u30b0\u65b9\u5f0f\u306e\u9078\u629e<\/strong><\/li>\n<\/ol>\n\n\n\n<ul class=\"wp-block-list\">\n<li><code>static<\/code>: \u5747\u7b49\u306a\u8ca0\u8377\u5206\u6563\u304c\u671f\u5f85\u3067\u304d\u308b\u5834\u5408<\/li>\n\n\n\n<li><code>dynamic<\/code>: \u8ca0\u8377\u304c\u4e0d\u5747\u4e00\u306a\u5834\u5408<\/li>\n\n\n\n<li><code>guided<\/code>: \u52d5\u7684\u306a\u8ca0\u8377\u5206\u6563\u304c\u5fc5\u8981\u306a\u5834\u5408<\/li>\n<\/ul>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>\u30b9\u30ec\u30c3\u30c9\u6570\u306e\u6700\u9069\u5316<\/strong><\/li>\n<\/ol>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u30b7\u30b9\u30c6\u30e0\u306e\u30b3\u30a2\u6570\u3092\u8003\u616e<\/li>\n\n\n\n<li>\u30aa\u30fc\u30d0\u30fc\u30d8\u30c3\u30c9\u3068\u30b9\u30b1\u30fc\u30e9\u30d3\u30ea\u30c6\u30a3\u306e\u30d0\u30e9\u30f3\u30b9<\/li>\n\n\n\n<li>\u52d5\u7684\u306a\u30b9\u30ec\u30c3\u30c9\u6570\u8abf\u6574\u306e\u6d3b\u7528<\/li>\n<\/ul>\n\n\n\n<p>\u3053\u308c\u3089\u306e\u57fa\u672c\u7684\u306a\u5b9f\u88c5\u624b\u9806\u3092\u7406\u89e3\u3059\u308b\u3053\u3068\u3067\u3001\u52b9\u7387\u7684\u306a\u4e26\u5217\u51e6\u7406\u306e\u5b9f\u88c5\u304c\u53ef\u80fd\u306b\u306a\u308a\u307e\u3059\u3002\u6b21\u306e\u30bb\u30af\u30b7\u30e7\u30f3\u3067\u306f\u3001\u3055\u3089\u306b\u8e0f\u307f\u8fbc\u3093\u3067\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u6700\u9069\u5316\u306e\u30c6\u30af\u30cb\u30c3\u30af\u306b\u3064\u3044\u3066\u8aac\u660e\u3057\u307e\u3059\u3002<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"i-7\">OpenMP\u306b\u3088\u308b\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u6700\u9069\u5316\u306e7\u3064\u306e\u30c6\u30af\u30cb\u30c3\u30af<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"i-8\">1. \u30c7\u30fc\u30bf\u5171\u6709\u3068\u540c\u671f\u306e\u6700\u9069\u5316<\/h3>\n\n\n\n<p>\u30c7\u30fc\u30bf\u5171\u6709\u3068\u540c\u671f\u51e6\u7406\u306e\u6700\u9069\u5316\u306f\u3001\u4e26\u5217\u51e6\u7406\u306e\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u3092\u5927\u304d\u304f\u5de6\u53f3\u3057\u307e\u3059\uff1a<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">#include &lt;omp.h&gt;\n#include &lt;vector&gt;\n\nvoid optimize_data_sharing() {\n    std::vector&lt;double&gt; data(10000);\n    double result = 0.0;\n\n    \/\/ \u6700\u9069\u5316\u524d\n    #pragma omp parallel for\n    for(int i = 0; i &lt; 10000; ++i) {\n        #pragma omp critical  \/\/ \u30dc\u30c8\u30eb\u30cd\u30c3\u30af\u3068\u306a\u308b\u53ef\u80fd\u6027\u304c\u9ad8\u3044\n        {\n            result += data[i];\n        }\n    }\n\n    \/\/ \u6700\u9069\u5316\u5f8c\n    #pragma omp parallel for reduction(+:result)\n    for(int i = 0; i &lt; 10000; ++i) {\n        result += data[i];  \/\/ reduction\u306b\u3088\u308b\u52b9\u7387\u7684\u306a\u96c6\u8a08\n    }\n}<\/pre>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"i-9\">2. \u30eb\u30fc\u30d7\u4e26\u5217\u5316\u306e\u52b9\u7387\u5316<\/h3>\n\n\n\n<p>\u30eb\u30fc\u30d7\u4e26\u5217\u5316\u306e\u52b9\u7387\u3092\u9ad8\u3081\u308b\u305f\u3081\u306e\u30c6\u30af\u30cb\u30c3\u30af\uff1a<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">void optimize_loop_parallelization() {\n    const int size = 10000;\n    std::vector&lt;double&gt; result(size);\n\n    \/\/ \u30c1\u30e3\u30f3\u30af\u30b5\u30a4\u30ba\u306e\u6700\u9069\u5316\n    #pragma omp parallel for schedule(dynamic, 256)\n    for(int i = 0; i &lt; size; ++i) {\n        \/\/ \u8a08\u7b97\u8ca0\u8377\u304c\u4e0d\u5747\u4e00\u306a\u51e6\u7406\n        result[i] = heavy_computation(i);\n    }\n\n    \/\/ \u30eb\u30fc\u30d7\u878d\u5408\u306b\u3088\u308b\u6700\u9069\u5316\n    #pragma omp parallel for\n    for(int i = 0; i &lt; size; ++i) {\n        \/\/ \u8907\u6570\u306e\u51e6\u7406\u30921\u3064\u306e\u30eb\u30fc\u30d7\u306b\u307e\u3068\u3081\u308b\n        result[i] = step1(i);\n        result[i] = step2(result[i]);\n        result[i] = step3(result[i]);\n    }\n}<\/pre>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"i-10\">3. \u30bf\u30b9\u30af\u4e26\u5217\u6027\u306e\u6d3b\u7528<\/h3>\n\n\n\n<p>\u8907\u96d1\u306a\u4f9d\u5b58\u95a2\u4fc2\u3092\u6301\u3064\u51e6\u7406\u306e\u30bf\u30b9\u30af\u4e26\u5217\u5316\uff1a<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">void optimize_task_parallelism() {\n    #pragma omp parallel\n    {\n        #pragma omp single  \/\/ 1\u3064\u306e\u30b9\u30ec\u30c3\u30c9\u304c\u30bf\u30b9\u30af\u3092\u751f\u6210\n        {\n            #pragma omp task\n            {\n                \/\/ \u975e\u540c\u671f\u30bf\u30b9\u30af1\n                heavy_task1();\n            }\n\n            #pragma omp task\n            {\n                \/\/ \u975e\u540c\u671f\u30bf\u30b9\u30af2\n                heavy_task2();\n            }\n\n            #pragma omp taskwait  \/\/ \u30bf\u30b9\u30af\u306e\u5b8c\u4e86\u3092\u5f85\u6a5f\n        }\n    }\n}<\/pre>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"i-11\">4. \u30e1\u30e2\u30ea\u30a2\u30af\u30bb\u30b9\u306e\u6700\u9069\u5316<\/h3>\n\n\n\n<p>\u30ad\u30e3\u30c3\u30b7\u30e5\u52b9\u7387\u3092\u8003\u616e\u3057\u305f\u30e1\u30e2\u30ea\u30a2\u30af\u30bb\u30b9\u30d1\u30bf\u30fc\u30f3\uff1a<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">void optimize_memory_access() {\n    const int N = 1000;\n    std::vector&lt;std::vector&lt;double&gt;&gt; matrix(N, std::vector&lt;double&gt;(N));\n\n    \/\/ \u30ad\u30e3\u30c3\u30b7\u30e5\u52b9\u7387\u306e\u826f\u3044\u30a2\u30af\u30bb\u30b9\u30d1\u30bf\u30fc\u30f3\n    #pragma omp parallel for collapse(2)\n    for(int i = 0; i &lt; N; ++i) {\n        for(int j = 0; j &lt; N; ++j) {\n            matrix[i][j] = compute_value(i, j);\n        }\n    }\n\n    \/\/ first touch policy\u306e\u6d3b\u7528\n    #pragma omp parallel for\n    for(int i = 0; i &lt; N; ++i) {\n        \/\/ \u30c7\u30fc\u30bf\u3092\u4f7f\u7528\u3059\u308b\u30b9\u30ec\u30c3\u30c9\u3067\u521d\u671f\u5316\n        matrix[i].resize(N);\n    }\n}<\/pre>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"i-12\">5. \u8ca0\u8377\u5206\u6563\u306e\u8abf\u6574<\/h3>\n\n\n\n<p>\u51e6\u7406\u8ca0\u8377\u306e\u5747\u7b49\u5316\u3068\u30b9\u30b1\u30b8\u30e5\u30fc\u30ea\u30f3\u30b0\u306e\u6700\u9069\u5316\uff1a<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">void optimize_load_balancing() {\n    std::vector&lt;Task&gt; tasks(1000);\n\n    \/\/ \u52d5\u7684\u8ca0\u8377\u5206\u6563\n    #pragma omp parallel for schedule(guided)\n    for(size_t i = 0; i &lt; tasks.size(); ++i) {\n        \/\/ \u51e6\u7406\u6642\u9593\u304c\u53ef\u5909\u306e\u30bf\u30b9\u30af\n        process_task(tasks[i]);\n    }\n\n    \/\/ \u30bf\u30b9\u30af\u30d7\u30fc\u30eb\u65b9\u5f0f\n    #pragma omp parallel\n    {\n        #pragma omp for schedule(dynamic, 1)\n        for(size_t i = 0; i &lt; tasks.size(); ++i) {\n            \/\/ \u7d30\u304b\u3044\u7c92\u5ea6\u3067\u306e\u30bf\u30b9\u30af\u5272\u308a\u5f53\u3066\n            process_task(tasks[i]);\n        }\n    }\n}<\/pre>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"i-13\">6. SIMD \u30b3\u30de\u30f3\u30c9\u306e\u6d3b\u7528<\/h3>\n\n\n\n<p>SIMD\u547d\u4ee4\u306b\u3088\u308b\u30d9\u30af\u30c8\u30eb\u5316\u306e\u6700\u9069\u5316\uff1a<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">void optimize_simd_usage() {\n    std::vector&lt;float&gt; data(1000);\n\n    \/\/ SIMD\u3068OpenMP\u306e\u7d44\u307f\u5408\u308f\u305b\n    #pragma omp parallel for simd\n    for(int i = 0; i &lt; 1000; ++i) {\n        data[i] = std::sqrt(data[i]); \/\/ \u30d9\u30af\u30c8\u30eb\u5316\u53ef\u80fd\u306a\u6f14\u7b97\n    }\n\n    \/\/ \u30a2\u30e9\u30a4\u30e1\u30f3\u30c8\u306e\u6700\u9069\u5316\n    alignas(32) float aligned_data[1000];\n    #pragma omp parallel for simd aligned(aligned_data: 32)\n    for(int i = 0; i &lt; 1000; ++i) {\n        aligned_data[i] *= 2.0f;\n    }\n}<\/pre>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"i-14\">7. \u30b9\u30b1\u30fc\u30e9\u30d3\u30ea\u30c6\u30a3\u306e\u78ba\u4fdd<\/h3>\n\n\n\n<p>\u30b9\u30b1\u30fc\u30e9\u30d3\u30ea\u30c6\u30a3\u3092\u7dad\u6301\u3059\u308b\u305f\u3081\u306e\u6700\u9069\u5316\uff1a<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">void optimize_scalability() {\n    const int N = 10000;\n    std::vector&lt;double&gt; data(N);\n\n    \/\/ false sharing\u306e\u56de\u907f\n    struct alignas(64) PaddedSum {\n        double value;\n        char padding[56]; \/\/ \u30ad\u30e3\u30c3\u30b7\u30e5\u30e9\u30a4\u30f3\u5883\u754c\u307e\u3067\u30d1\u30c7\u30a3\u30f3\u30b0\n    };\n    std::vector&lt;PaddedSum&gt; partial_sums(omp_get_max_threads());\n\n    #pragma omp parallel\n    {\n        int tid = omp_get_thread_num();\n        #pragma omp for nowait\n        for(int i = 0; i &lt; N; ++i) {\n            partial_sums[tid].value += data[i];\n        }\n    }\n\n    \/\/ \u7d50\u679c\u306e\u96c6\u8a08\n    double total = 0.0;\n    for(const auto&amp; sum : partial_sums) {\n        total += sum.value;\n    }\n}<\/pre>\n\n\n\n<p>\u3053\u308c\u3089\u306e\u6700\u9069\u5316\u30c6\u30af\u30cb\u30c3\u30af\u3092\u9069\u5207\u306b\u7d44\u307f\u5408\u308f\u305b\u308b\u3053\u3068\u3067\u3001OpenMP\u3092\u4f7f\u7528\u3057\u305f\u4e26\u5217\u51e6\u7406\u306e\u6027\u80fd\u3092\u6700\u5927\u9650\u306b\u5f15\u304d\u51fa\u3059\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u305f\u3060\u3057\u3001\u5404\u30c6\u30af\u30cb\u30c3\u30af\u306e\u9069\u7528\u306f\u3001\u5bfe\u8c61\u3068\u306a\u308b\u554f\u984c\u306e\u7279\u6027\u3084\u30cf\u30fc\u30c9\u30a6\u30a7\u30a2\u74b0\u5883\u306b\u5fdc\u3058\u3066\u614e\u91cd\u306b\u691c\u8a0e\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"i-15\">OpenMP\u306b\u304a\u3051\u308b\u4e00\u822c\u7684\u306a\u30a8\u30e9\u30fc\u3068\u305d\u306e\u89e3\u6c7a\u7b56<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"i-16\">\u30c7\u30fc\u30bf\u30ea\u30bd\u30fc\u30b9\u3092\u9632\u3050\u305f\u3081\u306e\u540c\u671f\u5236\u5fa1<\/h3>\n\n\n\n<p>\u4e26\u5217\u51e6\u7406\u306b\u304a\u3051\u308b\u6700\u3082\u4e00\u822c\u7684\u306a\u554f\u984c\u306e\u4e00\u3064\u306f\u30c7\u30fc\u30bf\u7af6\u5408\u3067\u3059\u3002\u9069\u5207\u306a\u540c\u671f\u5236\u5fa1\u304c\u306a\u3044\u3068\u3001\u4e88\u671f\u305b\u306c\u7d50\u679c\u3092\u62db\u304f\u53ef\u80fd\u6027\u304c\u3042\u308a\u307e\u3059\uff1a<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">#include &lt;omp.h&gt;\n#include &lt;vector&gt;\n#include &lt;iostream&gt;\n\n\/\/ \u554f\u984c\u306e\u3042\u308b\u30b3\u30fc\u30c9\nvoid problematic_synchronization() {\n    std::vector&lt;int&gt; shared_data(1000, 0);\n\n    \/\/ \u30c7\u30fc\u30bf\u7af6\u5408\u304c\u767a\u751f\u3059\u308b\u4f8b\n    #pragma omp parallel for\n    for(int i = 0; i &lt; 1000; ++i) {\n        shared_data[i % 10] += 1;  \/\/ \u8907\u6570\u30b9\u30ec\u30c3\u30c9\u304c\u540c\u3058\u8981\u7d20\u306b\u540c\u6642\u30a2\u30af\u30bb\u30b9\n    }\n}\n\n\/\/ \u6539\u5584\u3055\u308c\u305f\u30b3\u30fc\u30c9\nvoid proper_synchronization() {\n    std::vector&lt;int&gt; shared_data(1000, 0);\n\n    \/\/ atomic\u64cd\u4f5c\u306b\u3088\u308b\u89e3\u6c7a\n    #pragma omp parallel for\n    for(int i = 0; i &lt; 1000; ++i) {\n        #pragma omp atomic\n        shared_data[i % 10] += 1;\n    }\n\n    \/\/ \u307e\u305f\u306f\u3001critical\u30bb\u30af\u30b7\u30e7\u30f3\u306b\u3088\u308b\u89e3\u6c7a\n    #pragma omp parallel for\n    for(int i = 0; i &lt; 1000; ++i) {\n        #pragma omp critical\n        {\n            shared_data[i % 10] += 1;\n        }\n    }\n}<\/pre>\n\n\n\n<p>\u540c\u671f\u5236\u5fa1\u306e\u30d9\u30b9\u30c8\u30d7\u30e9\u30af\u30c6\u30a3\u30b9\uff1a<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>\u3067\u304d\u308b\u3060\u3051\u7d30\u304b\u3044\u7c92\u5ea6\u3067\u540c\u671f\u3092\u53d6\u308b<\/li>\n\n\n\n<li>\u5fc5\u8981\u6700\u5c0f\u9650\u306e\u7bc4\u56f2\u3067critical\u30bb\u30af\u30b7\u30e7\u30f3\u3092\u4f7f\u7528<\/li>\n\n\n\n<li>\u53ef\u80fd\u306a\u5834\u5408\u306fatomic\u64cd\u4f5c\u3092\u512a\u5148<\/li>\n\n\n\n<li>reduction\u53e5\u306e\u6d3b\u7528\u3092\u691c\u8a0e<\/li>\n<\/ol>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"i-17\">\u30e1\u30e2\u30ea\u30ea\u30fc\u30af\u3092\u9632\u3050\u305f\u3081\u306e\u30d9\u30b9\u30c8\u30d7\u30e9\u30af\u30c6\u30a3\u30b9<\/h3>\n\n\n\n<p>OpenMP\u3092\u4f7f\u7528\u3059\u308b\u969b\u306e\u30e1\u30e2\u30ea\u7ba1\u7406\u306f\u7279\u306b\u6ce8\u610f\u304c\u5fc5\u8981\u3067\u3059\uff1a<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">#include &lt;omp.h&gt;\n#include &lt;memory&gt;\n\n\/\/ \u30e1\u30e2\u30ea\u30ea\u30fc\u30af\u306e\u5371\u967a\u304c\u3042\u308b\u5b9f\u88c5\nvoid risky_memory_management() {\n    #pragma omp parallel\n    {\n        int* data = new int[1000];  \/\/ \u5404\u30b9\u30ec\u30c3\u30c9\u3067\u30e1\u30e2\u30ea\u78ba\u4fdd\n        \/\/ \u4f8b\u5916\u304c\u767a\u751f\u3057\u305f\u5834\u5408\u3001delete\u304c\u5b9f\u884c\u3055\u308c\u306a\u3044\u53ef\u80fd\u6027\n        process_data(data);\n        delete[] data;\n    }\n}\n\n\/\/ \u5b89\u5168\u306a\u5b9f\u88c5\nvoid safe_memory_management() {\n    #pragma omp parallel\n    {\n        \/\/ \u30b9\u30de\u30fc\u30c8\u30dd\u30a4\u30f3\u30bf\u306e\u4f7f\u7528\n        std::unique_ptr&lt;int[]&gt; data(new int[1000]);\n        try {\n            process_data(data.get());\n        } catch (...) {\n            \/\/ \u4f8b\u5916\u51e6\u7406\n        }\n        \/\/ \u30b9\u30b3\u30fc\u30d7\u3092\u629c\u3051\u308b\u3068\u81ea\u52d5\u7684\u306b\u89e3\u653e\n    }\n}<\/pre>\n\n\n\n<p>\u30e1\u30e2\u30ea\u7ba1\u7406\u306e\u30dd\u30a4\u30f3\u30c8\uff1a<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>\u30b9\u30de\u30fc\u30c8\u30dd\u30a4\u30f3\u30bf\u306e\u6d3b\u7528<\/li>\n\n\n\n<li>RAII\u30d1\u30bf\u30fc\u30f3\u306e\u9069\u7528<\/li>\n\n\n\n<li>\u30b9\u30ec\u30c3\u30c9\u30ed\u30fc\u30ab\u30eb\u30b9\u30c8\u30ec\u30fc\u30b8\u306e\u9069\u5207\u306a\u4f7f\u7528<\/li>\n\n\n\n<li>\u30e1\u30e2\u30ea\u78ba\u4fdd\u30fb\u89e3\u653e\u306e\u30d0\u30e9\u30f3\u30b9\u78ba\u8a8d<\/li>\n<\/ol>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"i-18\">\u30c7\u30c3\u30c9\u30ed\u30c3\u30af\u306e\u56de\u907f\u65b9\u6cd5<\/h3>\n\n\n\n<p>\u30c7\u30c3\u30c9\u30ed\u30c3\u30af\u306f\u4e26\u5217\u30d7\u30ed\u30b0\u30e9\u30df\u30f3\u30b0\u306b\u304a\u3051\u308b\u91cd\u5927\u306a\u554f\u984c\u306e\u4e00\u3064\u3067\u3059\uff1a<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">#include &lt;omp.h&gt;\n#include &lt;mutex&gt;\n\n\/\/ \u30c7\u30c3\u30c9\u30ed\u30c3\u30af\u306e\u5371\u967a\u304c\u3042\u308b\u5b9f\u88c5\nvoid deadlock_prone_code() {\n    std::mutex mutex1, mutex2;\n\n    #pragma omp parallel sections\n    {\n        #pragma omp section\n        {\n            std::lock_guard&lt;std::mutex&gt; lock1(mutex1);\n            \/\/ \u51e6\u7406\n            std::lock_guard&lt;std::mutex&gt; lock2(mutex2);\n        }\n\n        #pragma omp section\n        {\n            std::lock_guard&lt;std::mutex&gt; lock2(mutex2);\n            \/\/ \u51e6\u7406\n            std::lock_guard&lt;std::mutex&gt; lock1(mutex1);\n        }\n    }\n}\n\n\/\/ \u30c7\u30c3\u30c9\u30ed\u30c3\u30af\u56de\u907f\u306e\u5b9f\u88c5\nvoid deadlock_free_code() {\n    std::mutex mutex1, mutex2;\n\n    #pragma omp parallel sections\n    {\n        #pragma omp section\n        {\n            \/\/ std::scoped_lock\u3092\u4f7f\u7528\u3057\u3066\u5b89\u5168\u306b\u30ed\u30c3\u30af\n            std::scoped_lock locks(mutex1, mutex2);\n            \/\/ \u51e6\u7406\n        }\n\n        #pragma omp section\n        {\n            std::scoped_lock locks(mutex1, mutex2);\n            \/\/ \u51e6\u7406\n        }\n    }\n}<\/pre>\n\n\n\n<p>\u30c7\u30c3\u30c9\u30ed\u30c3\u30af\u56de\u907f\u306e\u305f\u3081\u306e\u6307\u91dd\uff1a<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>\u30ed\u30c3\u30af\u306e\u7372\u5f97\u9806\u5e8f\u3092\u4e00\u8cab\u3055\u305b\u308b<\/li>\n\n\n\n<li>\u8907\u6570\u306e\u30ed\u30c3\u30af\u304c\u5fc5\u8981\u306a\u5834\u5408\u306fstd::scoped_lock\u3092\u4f7f\u7528<\/li>\n\n\n\n<li>\u30ed\u30c3\u30af\u306e\u4fdd\u6301\u6642\u9593\u3092\u6700\u5c0f\u9650\u306b\u6291\u3048\u308b<\/li>\n\n\n\n<li>\u30cd\u30b9\u30c8\u3057\u305f\u30af\u30ea\u30c6\u30a3\u30ab\u30eb\u30bb\u30af\u30b7\u30e7\u30f3\u3092\u907f\u3051\u308b<\/li>\n<\/ol>\n\n\n\n<p>\u30a8\u30e9\u30fc\u9632\u6b62\u306e\u305f\u3081\u306e\u4e00\u822c\u7684\u306a\u30d9\u30b9\u30c8\u30d7\u30e9\u30af\u30c6\u30a3\u30b9\uff1a<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>\u30c7\u30fc\u30bf\u4f9d\u5b58\u95a2\u4fc2\u306e\u614e\u91cd\u306a\u5206\u6790<\/li>\n\n\n\n<li>\u30b9\u30ec\u30c3\u30c9\u30bb\u30fc\u30d5\u306a\u30c7\u30fc\u30bf\u69cb\u9020\u306e\u4f7f\u7528<\/li>\n\n\n\n<li>\u9069\u5207\u306a\u30b3\u30f3\u30d1\u30a4\u30e9\u8b66\u544a\u306e\u6709\u52b9\u5316<\/li>\n\n\n\n<li>\u4e26\u5217\u51e6\u7406\u306e\u5358\u4f53\u30c6\u30b9\u30c8\u306e\u5b9f\u65bd<\/li>\n<\/ol>\n\n\n\n<p>\u3053\u308c\u3089\u306e\u30a8\u30e9\u30fc\u30d1\u30bf\u30fc\u30f3\u3068\u89e3\u6c7a\u7b56\u3092\u7406\u89e3\u3059\u308b\u3053\u3068\u3067\u3001\u3088\u308a\u5b89\u5b9a\u3057\u305f\u4e26\u5217\u51e6\u7406\u306e\u5b9f\u88c5\u304c\u53ef\u80fd\u306b\u306a\u308a\u307e\u3059\u3002\u6b21\u306e\u30bb\u30af\u30b7\u30e7\u30f3\u3067\u306f\u3001\u5b9f\u969b\u306e\u6d3b\u7528\u4f8b\u3092\u898b\u3066\u3044\u304d\u307e\u3057\u3087\u3046\u3002<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"i-19\">OpenMP\u306e\u5b9f\u8df5\u6d3b\u7528\u4f8b<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"i-20\">\u753b\u50cf\u51e6\u7406\u3067\u306e\u5b9f\u88c5\u4f8b<\/h3>\n\n\n\n<p>\u753b\u50cf\u51e6\u7406\u306f\u4e26\u5217\u5316\u306e\u6069\u6075\u3092\u53d7\u3051\u3084\u3059\u3044\u4ee3\u8868\u7684\u306a\u5fdc\u7528\u4f8b\u3067\u3059\uff1a<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">#include &lt;omp.h&gt;\n#include &lt;vector&gt;\n#include &lt;cmath&gt;\n\nclass Image {\npublic:\n    Image(int width, int height) : width_(width), height_(height), \n        data_(width * height * 3) {}\n\n    \/\/ \u30ac\u30a6\u30b7\u30a2\u30f3\u30d6\u30e9\u30fc\u51e6\u7406\u306e\u4e26\u5217\u5b9f\u88c5\n    void applyGaussianBlur(float sigma) {\n        std::vector&lt;float&gt; kernel = createGaussianKernel(sigma);\n        std::vector&lt;unsigned char&gt; temp(data_);\n\n        \/\/ \u6c34\u5e73\u65b9\u5411\u306e\u30d6\u30e9\u30fc\u51e6\u7406\n        #pragma omp parallel for collapse(2)\n        for(int y = 0; y &lt; height_; ++y) {\n            for(int x = 0; x &lt; width_; ++x) {\n                applyKernelH(x, y, kernel, temp);\n            }\n        }\n\n        \/\/ \u5782\u76f4\u65b9\u5411\u306e\u30d6\u30e9\u30fc\u51e6\u7406\n        #pragma omp parallel for collapse(2)\n        for(int y = 0; y &lt; height_; ++y) {\n            for(int x = 0; x &lt; width_; ++x) {\n                applyKernelV(x, y, kernel, temp);\n            }\n        }\n    }\n\nprivate:\n    int width_, height_;\n    std::vector&lt;unsigned char&gt; data_;\n\n    void applyKernelH(int x, int y, const std::vector&lt;float&gt;&amp; kernel, \n                      std::vector&lt;unsigned char&gt;&amp; temp);\n    void applyKernelV(int x, int y, const std::vector&lt;float&gt;&amp; kernel,\n                      std::vector&lt;unsigned char&gt;&amp; temp);\n};<\/pre>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"i-21\">\u6570\u5024\u8a08\u7b97\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u6700\u9069\u5316\u4e8b\u4f8b<\/h3>\n\n\n\n<p>\u884c\u5217\u6f14\u7b97\u3084\u6570\u5024\u7a4d\u5206\u306a\u3069\u306e\u6570\u5024\u8a08\u7b97\u3067\u306e\u6d3b\u7528\u4f8b\uff1a<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">#include &lt;omp.h&gt;\n#include &lt;vector&gt;\n#include &lt;cmath&gt;\n\nclass NumericalComputation {\npublic:\n    \/\/ \u30e2\u30f3\u30c6\u30ab\u30eb\u30ed\u6cd5\u306b\u3088\u308b\u03c0\u306e\u8a08\u7b97\n    static double calculatePi(int numPoints) {\n        int insideCircle = 0;\n\n        #pragma omp parallel reduction(+:insideCircle)\n        {\n            unsigned int seed = omp_get_thread_num();\n\n            #pragma omp for\n            for(int i = 0; i &lt; numPoints; ++i) {\n                double x = rand_r(&amp;seed) \/ (double)RAND_MAX;\n                double y = rand_r(&amp;seed) \/ (double)RAND_MAX;\n\n                if(x*x + y*y &lt;= 1.0) {\n                    insideCircle++;\n                }\n            }\n        }\n\n        return 4.0 * insideCircle \/ numPoints;\n    }\n\n    \/\/ \u884c\u5217\u4e57\u7b97\u306e\u4e26\u5217\u5b9f\u88c5\n    static void matrixMultiply(const std::vector&lt;std::vector&lt;double&gt;&gt;&amp; A,\n                             const std::vector&lt;std::vector&lt;double&gt;&gt;&amp; B,\n                             std::vector&lt;std::vector&lt;double&gt;&gt;&amp; C) {\n        int N = A.size();\n\n        #pragma omp parallel for collapse(2)\n        for(int i = 0; i &lt; N; ++i) {\n            for(int j = 0; j &lt; N; ++j) {\n                double sum = 0.0;\n                for(int k = 0; k &lt; N; ++k) {\n                    sum += A[i][k] * B[k][j];\n                }\n                C[i][j] = sum;\n            }\n        }\n    }\n};<\/pre>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"i-22\">\u5927\u898f\u6a21\u30c7\u30fc\u30bf\u51e6\u7406\u3067\u306e\u6d3b\u7528\u65b9\u6cd5<\/h3>\n\n\n\n<p>\u5927\u91cf\u306e\u30c7\u30fc\u30bf\u3092\u52b9\u7387\u7684\u306b\u51e6\u7406\u3059\u308b\u4f8b\uff1a<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">#include &lt;omp.h&gt;\n#include &lt;vector&gt;\n#include &lt;algorithm&gt;\n#include &lt;numeric&gt;\n\nclass DataProcessor {\npublic:\n    \/\/ \u5927\u898f\u6a21\u30c7\u30fc\u30bf\u306e\u4e26\u5217\u30bd\u30fc\u30c8\n    static void parallelSort(std::vector&lt;int&gt;&amp; data) {\n        const int MIN_SIZE = 1000;\n\n        if(data.size() &lt;= MIN_SIZE) {\n            std::sort(data.begin(), data.end());\n            return;\n        }\n\n        \/\/ \u30c7\u30fc\u30bf\u3092\u5206\u5272\u3057\u3066\u4e26\u5217\u30bd\u30fc\u30c8\n        #pragma omp parallel\n        {\n            #pragma omp single\n            {\n                int num_threads = omp_get_num_threads();\n                std::vector&lt;std::vector&lt;int&gt;&gt; sorted_chunks(num_threads);\n\n                \/\/ \u30c1\u30e3\u30f3\u30af\u306b\u5206\u5272\n                int chunk_size = data.size() \/ num_threads;\n                for(int i = 0; i &lt; num_threads; ++i) {\n                    int start = i * chunk_size;\n                    int end = (i == num_threads-1) ? data.size() : (i+1) * chunk_size;\n\n                    #pragma omp task\n                    {\n                        sorted_chunks[i].assign(data.begin() + start, \n                                              data.begin() + end);\n                        std::sort(sorted_chunks[i].begin(), \n                                sorted_chunks[i].end());\n                    }\n                }\n\n                #pragma omp taskwait\n\n                \/\/ \u30de\u30fc\u30b8\u51e6\u7406\n                data = sorted_chunks[0];\n                for(int i = 1; i &lt; num_threads; ++i) {\n                    std::vector&lt;int&gt; merged;\n                    std::merge(data.begin(), data.end(),\n                             sorted_chunks[i].begin(), sorted_chunks[i].end(),\n                             std::back_inserter(merged));\n                    data = std::move(merged);\n                }\n            }\n        }\n    }\n\n    \/\/ \u4e26\u5217\u30c7\u30fc\u30bf\u96c6\u8a08\u51e6\u7406\n    static std::vector&lt;double&gt; parallelAggregation(\n        const std::vector&lt;double&gt;&amp; data,\n        int window_size\n    ) {\n        std::vector&lt;double&gt; result(data.size() - window_size + 1);\n\n        #pragma omp parallel for schedule(dynamic)\n        for(int i = 0; i &lt;= data.size() - window_size; ++i) {\n            double sum = 0.0;\n            for(int j = 0; j &lt; window_size; ++j) {\n                sum += data[i + j];\n            }\n            result[i] = sum \/ window_size;\n        }\n\n        return result;\n    }\n};<\/pre>\n\n\n\n<p>\u3053\u308c\u3089\u306e\u5b9f\u88c5\u4f8b\u3067\u306f\u3001\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u6027\u80fd\u6539\u5584\u304c\u671f\u5f85\u3067\u304d\u307e\u3059\uff1a<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>\u753b\u50cf\u51e6\u7406\u306e\u4f8b<\/li>\n<\/ol>\n\n\n\n<ul class=\"wp-block-list\">\n<li>4\u30b3\u30a2CPU\u3067\u7d043.5\u500d\u306e\u51e6\u7406\u901f\u5ea6\u5411\u4e0a<\/li>\n\n\n\n<li>\u30e1\u30e2\u30ea\u30a2\u30af\u30bb\u30b9\u306e\u5c40\u6240\u6027\u3092\u8003\u616e\u3057\u305f\u5b9f\u88c5<\/li>\n<\/ul>\n\n\n\n<ol class=\"wp-block-list\">\n<li>\u6570\u5024\u8a08\u7b97\u306e\u4f8b<\/li>\n<\/ol>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u30e2\u30f3\u30c6\u30ab\u30eb\u30ed\u6cd5\uff1a\u30b3\u30a2\u6570\u306b\u307b\u307c\u6bd4\u4f8b\u3057\u305f\u901f\u5ea6\u5411\u4e0a<\/li>\n\n\n\n<li>\u884c\u5217\u4e57\u7b97\uff1a8\u30b3\u30a2\u3067\u7d046-7\u500d\u306e\u6027\u80fd\u5411\u4e0a<\/li>\n<\/ul>\n\n\n\n<ol class=\"wp-block-list\">\n<li>\u30c7\u30fc\u30bf\u51e6\u7406\u306e\u4f8b<\/li>\n<\/ol>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u30bd\u30fc\u30c8\u51e6\u7406\uff1a\u30c7\u30fc\u30bf\u30b5\u30a4\u30ba\u306b\u5fdc\u3058\u30662-4\u500d\u306e\u901f\u5ea6\u5411\u4e0a<\/li>\n\n\n\n<li>\u96c6\u8a08\u51e6\u7406\uff1a\u30a6\u30a3\u30f3\u30c9\u30a6\u30b5\u30a4\u30ba\u306b\u5fdc\u3058\u30663-5\u500d\u306e\u6027\u80fd\u6539\u5584<\/li>\n<\/ul>\n\n\n\n<p>\u5b9f\u88c5\u6642\u306e\u6ce8\u610f\u70b9\uff1a<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>\u30c7\u30fc\u30bf\u30b5\u30a4\u30ba\u306b\u5fdc\u3058\u305f\u4e26\u5217\u5316\u306e\u5224\u65ad<\/li>\n\n\n\n<li>\u30ad\u30e3\u30c3\u30b7\u30e5\u52b9\u7387\u3092\u8003\u616e\u3057\u305f\u30c7\u30fc\u30bf\u30a2\u30af\u30bb\u30b9<\/li>\n\n\n\n<li>\u9069\u5207\u306a\u30b9\u30b1\u30b8\u30e5\u30fc\u30ea\u30f3\u30b0\u65b9\u5f0f\u306e\u9078\u629e<\/li>\n\n\n\n<li>\u30aa\u30fc\u30d0\u30fc\u30d8\u30c3\u30c9\u3092\u8003\u616e\u3057\u305f\u30bf\u30b9\u30af\u7c92\u5ea6\u306e\u8abf\u6574<\/li>\n<\/ol>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"i-23\">OpenMP\u306e\u6027\u80fd\u8a55\u4fa1\u3068\u6700\u9069\u5316\u306e\u305f\u3081\u306e\u30c4\u30fc\u30eb<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"i-24\">\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u30d7\u30ed\u30d5\u30a1\u30a4\u30ea\u30f3\u30b0\u30c4\u30fc\u30eb\u306e\u6d3b\u7528\u6cd5<\/h3>\n\n\n\n<p>OpenMP\u30d7\u30ed\u30b0\u30e9\u30e0\u306e\u6027\u80fd\u3092\u6b63\u78ba\u306b\u8a55\u4fa1\u3057\u3001\u6700\u9069\u5316\u3059\u308b\u305f\u3081\u306b\u3001\u4ee5\u4e0b\u306e\u30c4\u30fc\u30eb\u3068\u624b\u6cd5\u304c\u6709\u52b9\u3067\u3059\uff1a<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Intel VTune Profiler<\/strong><\/li>\n<\/ol>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\"># \u30b3\u30f3\u30d1\u30a4\u30eb\u6642\u306e\u30d5\u30e9\u30b0\u8a2d\u5b9a\ng++ -g -fopenmp -o my_program my_program.cpp\n\n# VTune\u3067\u306e\u89e3\u6790\u5b9f\u884c\nvtune -collect hotspots .\/my_program<\/pre>\n\n\n\n<p>\u4e3b\u306a\u5206\u6790\u6a5f\u80fd\uff1a<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u30db\u30c3\u30c8\u30b9\u30dd\u30c3\u30c8\u306e\u7279\u5b9a<\/li>\n\n\n\n<li>\u30b9\u30ec\u30c3\u30c9\u4e26\u5217\u6027\u306e\u5206\u6790<\/li>\n\n\n\n<li>\u30ad\u30e3\u30c3\u30b7\u30e5\u4f7f\u7528\u52b9\u7387\u306e\u8a55\u4fa1<\/li>\n\n\n\n<li>NUMA\u6700\u9069\u5316\u306e\u63d0\u6848<\/li>\n<\/ul>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Linux perf<\/strong><\/li>\n<\/ol>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\"># \u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u30ab\u30a6\u30f3\u30bf\u306e\u53ce\u96c6\nperf record -e cpu-cycles,cache-misses,branch-misses .\/my_program\n\n# \u7d50\u679c\u306e\u5206\u6790\nperf report<\/pre>\n\n\n\n<p>\u53ce\u96c6\u53ef\u80fd\u306a\u6307\u6a19\uff1a<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>CPU\u4f7f\u7528\u7387<\/li>\n\n\n\n<li>\u30ad\u30e3\u30c3\u30b7\u30e5\u30df\u30b9<\/li>\n\n\n\n<li>\u5206\u5c90\u4e88\u6e2c\u30df\u30b9<\/li>\n\n\n\n<li>\u30e1\u30e2\u30ea\u30a2\u30af\u30bb\u30b9\u30d1\u30bf\u30fc\u30f3<\/li>\n<\/ul>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>HPCToolkit<\/strong><\/li>\n<\/ol>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">#include &lt;hpctoolkit.h&gt;\n\nvoid profile_example() {\n    hpctoolkit_sampling_start();\n\n    #pragma omp parallel for\n    for(int i = 0; i &lt; 1000; ++i) {\n        heavy_computation(i);\n    }\n\n    hpctoolkit_sampling_stop();\n}<\/pre>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"i-25\">\u30dc\u30c8\u30eb\u30cd\u30c3\u30af\u306e\u7279\u5b9a\u3068\u89e3\u6c7a\u624b\u9806<\/h3>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>\u6027\u80fd\u6e2c\u5b9a\u7528\u306e\u30e9\u30c3\u30d1\u30fc\u30af\u30e9\u30b9<\/strong><\/li>\n<\/ol>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">class PerformanceMonitor {\npublic:\n    PerformanceMonitor(const std::string&amp; label) \n        : label_(label), start_(omp_get_wtime()) {}\n\n    ~PerformanceMonitor() {\n        double end = omp_get_wtime();\n        #pragma omp critical\n        {\n            std::cout &lt;&lt; label_ &lt;&lt; \": \" \n                     &lt;&lt; (end - start_) &lt;&lt; \" seconds\\n\";\n        }\n    }\n\nprivate:\n    std::string label_;\n    double start_;\n};\n\n\/\/ \u4f7f\u7528\u4f8b\nvoid measure_performance() {\n    {\n        PerformanceMonitor pm(\"Parallel Section 1\");\n        #pragma omp parallel for\n        for(int i = 0; i &lt; 1000; ++i) {\n            heavy_computation(i);\n        }\n    }\n}<\/pre>\n\n\n\n<ol start=\"2\" class=\"wp-block-list\">\n<li><strong>\u30b9\u30b1\u30fc\u30e9\u30d3\u30ea\u30c6\u30a3\u5206\u6790\u30c4\u30fc\u30eb<\/strong><\/li>\n<\/ol>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">class ScalabilityAnalyzer {\npublic:\n    static void analyze_scaling(std::function&lt;void()&gt; task, \n                              int max_threads) {\n        std::vector&lt;double&gt; times;\n\n        for(int threads = 1; threads &lt;= max_threads; ++threads) {\n            omp_set_num_threads(threads);\n\n            double start = omp_get_wtime();\n            task();\n            double end = omp_get_wtime();\n\n            times.push_back(end - start);\n\n            double speedup = times[0] \/ times.back();\n            std::cout &lt;&lt; \"Threads: \" &lt;&lt; threads \n                     &lt;&lt; \", Speedup: \" &lt;&lt; speedup \n                     &lt;&lt; \", Efficiency: \" &lt;&lt; (speedup \/ threads) \n                     &lt;&lt; std::endl;\n        }\n    }\n};<\/pre>\n\n\n\n<p>\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u6700\u9069\u5316\u306e\u4e3b\u306a\u30dd\u30a4\u30f3\u30c8\uff1a<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>\u30ed\u30fc\u30c9\u5206\u6563\u306e\u8a55\u4fa1<\/strong><\/li>\n<\/ol>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">void analyze_load_balance() {\n    std::vector&lt;double&gt; thread_times(omp_get_max_threads());\n\n    #pragma omp parallel\n    {\n        int tid = omp_get_thread_num();\n        double start = omp_get_wtime();\n\n        \/\/ \u51e6\u7406\n\n        thread_times[tid] = omp_get_wtime() - start;\n    }\n\n    \/\/ \u8ca0\u8377\u5206\u6563\u306e\u5206\u6790\n    double avg_time = std::accumulate(thread_times.begin(), \n                                    thread_times.end(), 0.0) \n                                    \/ thread_times.size();\n    double max_time = *std::max_element(thread_times.begin(), \n                                      thread_times.end());\n\n    std::cout &lt;&lt; \"Load imbalance: \" \n              &lt;&lt; (max_time \/ avg_time - 1.0) * 100 \n              &lt;&lt; \"%\" &lt;&lt; std::endl;\n}<\/pre>\n\n\n\n<ol start=\"2\" class=\"wp-block-list\">\n<li><strong>\u30e1\u30e2\u30ea\u30a2\u30af\u30bb\u30b9\u30d1\u30bf\u30fc\u30f3\u306e\u5206\u6790<\/strong><\/li>\n<\/ol>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">void analyze_memory_access() {\n    const int SIZE = 1000000;\n    std::vector&lt;double&gt; data(SIZE);\n\n    \/\/ \u30ad\u30e3\u30c3\u30b7\u30e5\u30df\u30b9\u306e\u8a08\u6e2c\n    #pragma omp parallel for schedule(static)\n    for(int i = 0; i &lt; SIZE; ++i) {\n        \/\/ \u30b9\u30c8\u30e9\u30a4\u30c9\u30a2\u30af\u30bb\u30b9\u306e\u30c6\u30b9\u30c8\n        data[(i * 16) % SIZE] = i;\n    }\n\n    \/\/ \u9023\u7d9a\u30a2\u30af\u30bb\u30b9\u306e\u30c6\u30b9\u30c8\n    #pragma omp parallel for schedule(static)\n    for(int i = 0; i &lt; SIZE; ++i) {\n        data[i] = i;\n    }\n}<\/pre>\n\n\n\n<p>\u6700\u9069\u5316\u306e\u305f\u3081\u306e\u30d9\u30b9\u30c8\u30d7\u30e9\u30af\u30c6\u30a3\u30b9\uff1a<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>\u30d7\u30ed\u30d5\u30a1\u30a4\u30ea\u30f3\u30b0\u306b\u57fa\u3065\u304f\u6700\u9069\u5316<\/li>\n<\/ol>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u30db\u30c3\u30c8\u30b9\u30dd\u30c3\u30c8\u306e\u7279\u5b9a\u3068\u96c6\u4e2d\u7684\u306a\u6700\u9069\u5316<\/li>\n\n\n\n<li>\u30b9\u30ec\u30c3\u30c9\u4e26\u5217\u6027\u306e\u30dc\u30c8\u30eb\u30cd\u30c3\u30af\u89e3\u6d88<\/li>\n<\/ul>\n\n\n\n<ol class=\"wp-block-list\">\n<li>\u30b7\u30b9\u30c6\u30de\u30c1\u30c3\u30af\u306a\u6027\u80fd\u8a55\u4fa1<\/li>\n<\/ol>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u30d9\u30fc\u30b9\u30e9\u30a4\u30f3\u306e\u78ba\u7acb<\/li>\n\n\n\n<li>\u5909\u66f4\u306e\u52b9\u679c\u3092\u5b9a\u91cf\u7684\u306b\u6e2c\u5b9a<\/li>\n\n\n\n<li>\u30b9\u30b1\u30fc\u30e9\u30d3\u30ea\u30c6\u30a3\u306e\u7d99\u7d9a\u7684\u306a\u8a55\u4fa1<\/li>\n<\/ul>\n\n\n\n<ol class=\"wp-block-list\">\n<li>\u30c4\u30fc\u30eb\u6d3b\u7528\u306e\u30dd\u30a4\u30f3\u30c8<\/li>\n<\/ol>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u9069\u5207\u306a\u30c4\u30fc\u30eb\u306e\u9078\u629e<\/li>\n\n\n\n<li>\u5b9a\u671f\u7684\u306a\u6027\u80fd\u6e2c\u5b9a\u306e\u5b9f\u65bd<\/li>\n\n\n\n<li>\u7d50\u679c\u306e\u8a73\u7d30\u306a\u5206\u6790\u3068\u5bfe\u7b56\u7acb\u6848<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"i-26\">OpenMP\u306e\u5c06\u6765\u5c55\u671b\u3068\u6700\u65b0\u30c8\u30ec\u30f3\u30c9<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"i-27\">OpenMP 5.0\u4ee5\u964d\u306e\u65b0\u6a5f\u80fd\u3068\u6d3b\u7528\u6cd5<\/h3>\n\n\n\n<p>OpenMP 5.0\u4ee5\u964d\u3067\u5c0e\u5165\u3055\u308c\u305f\u65b0\u6a5f\u80fd\u306f\u3001\u4e26\u5217\u30d7\u30ed\u30b0\u30e9\u30df\u30f3\u30b0\u306e\u53ef\u80fd\u6027\u3092\u5927\u304d\u304f\u5e83\u3052\u3066\u3044\u307e\u3059\uff1a<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Loop Transformations<\/strong><\/li>\n<\/ol>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">#include &lt;omp.h&gt;\n#include &lt;vector&gt;\n\nvoid demonstrate_loop_transformation() {\n    const int N = 1000;\n    std::vector&lt;double&gt; A(N), B(N), C(N);\n\n    \/\/ \u30bf\u30a4\u30eb\u30eb\u30fc\u30d7\u5909\u63db\u306e\u6d3b\u7528\n    #pragma omp parallel for tile(32, 32)\n    for(int i = 0; i &lt; N; ++i) {\n        for(int j = 0; j &lt; N; ++j) {\n            A[i*N + j] = B[i*N + j] + C[i*N + j];\n        }\n    }\n\n    \/\/ Unroll\u30eb\u30fc\u30d7\u5909\u63db\n    #pragma omp parallel for unroll(4)\n    for(int i = 0; i &lt; N; ++i) {\n        A[i] = B[i] + C[i];\n    }\n}<\/pre>\n\n\n\n<ol start=\"2\" class=\"wp-block-list\">\n<li><strong>\u30e1\u30bf\u30c7\u30a3\u30ec\u30af\u30c6\u30a3\u30d6\u306e\u6d3b\u7528<\/strong><\/li>\n<\/ol>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">void demonstrate_metadirectives() {\n    #pragma omp metadirective \\\n        when(implementation=vendor(nvidia): target teams distribute parallel for) \\\n        when(implementation=vendor(amd): target teams distribute parallel for) \\\n        default(parallel for)\n    for(int i = 0; i &lt; 1000; ++i) {\n        \/\/ \u5b9f\u884c\u74b0\u5883\u306b\u5fdc\u3058\u3066\u6700\u9069\u306a\u4e26\u5217\u5316\u65b9\u5f0f\u304c\u9078\u629e\u3055\u308c\u308b\n        heavy_computation(i);\n    }\n}<\/pre>\n\n\n\n<ol start=\"3\" class=\"wp-block-list\">\n<li><strong>\u62e1\u5f35\u3055\u308c\u305fTasking\u6a5f\u80fd<\/strong><\/li>\n<\/ol>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">class TaskingEnhancements {\npublic:\n    void demonstrate_task_features() {\n        #pragma omp parallel\n        #pragma omp single\n        {\n            \/\/ \u4f9d\u5b58\u95a2\u4fc2\u3092\u6301\u3064\u30bf\u30b9\u30af\u30b0\u30eb\u30fc\u30d7\n            #pragma omp taskgroup task_reduction(+:result_)\n            {\n                #pragma omp task in_reduction(+:result_)\n                compute_partial_result(0, 500);\n\n                #pragma omp task in_reduction(+:result_)\n                compute_partial_result(500, 1000);\n            }\n        }\n    }\n\nprivate:\n    double result_ = 0.0;\n    void compute_partial_result(int start, int end);\n};<\/pre>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"i-28\">GPGPU\u3068\u306e\u9023\u643a\u306b\u3088\u308b\u3055\u3089\u306a\u308b\u6700\u9069\u5316<\/h3>\n\n\n\n<p>OpenMP\u306eGPGPU\u30b5\u30dd\u30fc\u30c8\u306f\u3001\u9ad8\u6027\u80fd\u8a08\u7b97\u306e\u65b0\u305f\u306a\u53ef\u80fd\u6027\u3092\u958b\u304d\u307e\u3059\uff1a<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">class GPUAcceleration {\npublic:\n    \/\/ GPU\u5bfe\u5fdc\u306e\u884c\u5217\u4e57\u7b97\n    static void matrix_multiply_gpu(const std::vector&lt;float&gt;&amp; A,\n                                  const std::vector&lt;float&gt;&amp; B,\n                                  std::vector&lt;float&gt;&amp; C,\n                                  int N) {\n        #pragma omp target teams distribute parallel for collapse(2) \\\n                map(to: A[0:N*N], B[0:N*N]) map(from: C[0:N*N])\n        for(int i = 0; i &lt; N; ++i) {\n            for(int j = 0; j &lt; N; ++j) {\n                float sum = 0.0f;\n                for(int k = 0; k &lt; N; ++k) {\n                    sum += A[i*N + k] * B[k*N + j];\n                }\n                C[i*N + j] = sum;\n            }\n        }\n    }\n\n    \/\/ \u30cf\u30a4\u30d6\u30ea\u30c3\u30c9CPU-GPU\u5b9f\u884c\n    static void hybrid_computation(std::vector&lt;float&gt;&amp; data) {\n        const int N = data.size();\n\n        #pragma omp parallel\n        {\n            \/\/ CPU\u90e8\u5206\u306e\u51e6\u7406\n            #pragma omp for\n            for(int i = 0; i &lt; N\/2; ++i) {\n                data[i] = cpu_heavy_computation(data[i]);\n            }\n\n            \/\/ GPU\u90e8\u5206\u306e\u51e6\u7406\n            #pragma omp target teams distribute parallel for \\\n                    map(tofrom: data[N\/2:N\/2])\n            for(int i = N\/2; i &lt; N; ++i) {\n                data[i] = gpu_heavy_computation(data[i]);\n            }\n        }\n    }\n};<\/pre>\n\n\n\n<p>\u5c06\u6765\u7684\u306a\u767a\u5c55\u65b9\u5411\uff1a<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>AI\u30ef\u30fc\u30af\u30ed\u30fc\u30c9\u306e\u6700\u9069\u5316<\/strong><\/li>\n<\/ol>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">class AIWorkloadOptimization {\npublic:\n    void neural_network_inference() {\n        \/\/ \u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u5c64\u306e\u4e26\u5217\u51e6\u7406\n        #pragma omp target teams distribute parallel for collapse(2)\n        for(int batch = 0; batch &lt; batch_size_; ++batch) {\n            for(int neuron = 0; neuron &lt; layer_size_; ++neuron) {\n                float sum = 0.0f;\n                for(int input = 0; input &lt; input_size_; ++input) {\n                    sum += weights_[neuron * input_size_ + input] * \n                           input_data_[batch * input_size_ + input];\n                }\n                output_[batch * layer_size_ + neuron] = \n                    activation_function(sum + biases_[neuron]);\n            }\n        }\n    }\n\nprivate:\n    int batch_size_, layer_size_, input_size_;\n    std::vector&lt;float&gt; weights_, biases_, input_data_, output_;\n    float activation_function(float x);\n};<\/pre>\n\n\n\n<ol start=\"2\" class=\"wp-block-list\">\n<li><strong>\u7570\u7a2e\u30b3\u30f3\u30d4\u30e5\u30fc\u30c6\u30a3\u30f3\u30b0\u306e\u30b5\u30dd\u30fc\u30c8\u5f37\u5316<\/strong><\/li>\n<\/ol>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"generic\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\">class HeterogeneousComputing {\npublic:\n    void optimize_for_multiple_devices() {\n        \/\/ \u30c7\u30d0\u30a4\u30b9\u306b\u5fdc\u3058\u305f\u6700\u9069\u306a\u5b9f\u884c\n        #pragma omp parallel\n        {\n            #pragma omp single\n            {\n                \/\/ GPU\u5411\u3051\u30bf\u30b9\u30af\n                #pragma omp task target device(gpu)\n                {\n                    gpu_specific_task();\n                }\n\n                \/\/ FPGA\u5411\u3051\u30bf\u30b9\u30af\n                #pragma omp task target device(fpga)\n                {\n                    fpga_specific_task();\n                }\n\n                \/\/ CPU\u5411\u3051\u30bf\u30b9\u30af\n                #pragma omp task\n                {\n                    cpu_specific_task();\n                }\n            }\n        }\n    }\n};<\/pre>\n\n\n\n<p>\u4eca\u5f8c\u4e88\u60f3\u3055\u308c\u308b\u767a\u5c55\uff1a<\/p>\n\n\n\n<ol 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