{"id":1056,"date":"2024-03-25T14:44:22","date_gmt":"2024-03-25T05:44:22","guid":{"rendered":"https:\/\/www.aqua-informatics.jp\/?p=1056"},"modified":"2024-03-25T14:47:32","modified_gmt":"2024-03-25T05:47:32","slug":"raspberry-pi-5-%e3%81%ae-unixbench","status":"publish","type":"post","link":"https:\/\/www.aqua-informatics.jp\/?p=1056","title":{"rendered":"Raspberry pi 5 \u306e Unixbench"},"content":{"rendered":"\n<pre class=\"wp-block-code\"><code>========================================================================\n   BYTE UNIX Benchmarks (Version 5.1.3)\n\n   System: raspberrypi5b-beta: GNU\/Linux\n   OS: GNU\/Linux -- 6.6.20+rpt-rpi-2712 -- #1 SMP PREEMPT Debian 1:6.6.20-1+rpt1 (2024-03-07)\n   Machine: aarch64 (unknown)\n   Language: en_US.utf8 (charmap=\"ANSI_X3.4-1968\", collate=\"ANSI_X3.4-1968\")\n   CPU 0:  (108.0 bogomips)\n          \n   CPU 1:  (108.0 bogomips)\n          \n   CPU 2:  (108.0 bogomips)\n          \n   CPU 3:  (108.0 bogomips)\n          \n   13:42:08 up 13:25,  2 users,  load average: 0.08, 0.02, 0.01; runlevel Mar\n\n------------------------------------------------------------------------\nBenchmark Run: Mon Mar 25 2024 13:42:08 - 14:10:08\n4 CPUs in system; running 1 parallel copy of tests\n\nDhrystone 2 using register variables       35766571.6 lps   (10.0 s, 7 samples)\nDouble-Precision Whetstone                     6992.7 MWIPS (9.9 s, 7 samples)\nExecl Throughput                               5377.4 lps   (30.0 s, 2 samples)\nFile Copy 1024 bufsize 2000 maxblocks        904805.4 KBps  (30.0 s, 2 samples)\nFile Copy 256 bufsize 500 maxblocks          300050.3 KBps  (30.0 s, 2 samples)\nFile Copy 4096 bufsize 8000 maxblocks       1291091.2 KBps  (30.0 s, 2 samples)\nPipe Throughput                             1498109.5 lps   (10.0 s, 7 samples)\nPipe-based Context Switching                 211146.2 lps   (10.0 s, 7 samples)\nProcess Creation                               7808.9 lps   (30.0 s, 2 samples)\nShell Scripts (1 concurrent)                  10101.8 lpm   (60.0 s, 2 samples)\nShell Scripts (8 concurrent)                   2387.3 lpm   (60.0 s, 2 samples)\nSystem Call Overhead                        1060662.4 lps   (10.0 s, 7 samples)\n\nSystem Benchmarks Index Values               BASELINE       RESULT    INDEX\nDhrystone 2 using register variables         116700.0   35766571.6   3064.8\nDouble-Precision Whetstone                       55.0       6992.7   1271.4\nExecl Throughput                                 43.0       5377.4   1250.6\nFile Copy 1024 bufsize 2000 maxblocks          3960.0     904805.4   2284.9\nFile Copy 256 bufsize 500 maxblocks            1655.0     300050.3   1813.0\nFile Copy 4096 bufsize 8000 maxblocks          5800.0    1291091.2   2226.0\nPipe Throughput                               12440.0    1498109.5   1204.3\nPipe-based Context Switching                   4000.0     211146.2    527.9\nProcess Creation                                126.0       7808.9    619.8\nShell Scripts (1 concurrent)                     42.4      10101.8   2382.5\nShell Scripts (8 concurrent)                      6.0       2387.3   3978.8\nSystem Call Overhead                          15000.0    1060662.4    707.1\n                                                                   ========\nSystem Benchmarks Index Score                                        1488.9\n\n------------------------------------------------------------------------\nBenchmark Run: Mon Mar 25 2024 14:10:08 - 14:38:09\n4 CPUs in system; running 4 parallel copies of tests\n\nDhrystone 2 using register variables      143065661.3 lps   (10.0 s, 7 samples)\nDouble-Precision Whetstone                    27992.6 MWIPS (9.9 s, 7 samples)\nExecl Throughput                              10179.2 lps   (30.0 s, 2 samples)\nFile Copy 1024 bufsize 2000 maxblocks       1466600.5 KBps  (30.0 s, 2 samples)\nFile Copy 256 bufsize 500 maxblocks         1156288.5 KBps  (30.0 s, 2 samples)\nFile Copy 4096 bufsize 8000 maxblocks       1324433.5 KBps  (30.0 s, 2 samples)\nPipe Throughput                             5951631.5 lps   (10.0 s, 7 samples)\nPipe-based Context Switching                 787295.0 lps   (10.0 s, 7 samples)\nProcess Creation                              15263.8 lps   (30.0 s, 2 samples)\nShell Scripts (1 concurrent)                  18098.8 lpm   (60.0 s, 2 samples)\nShell Scripts (8 concurrent)                   2333.7 lpm   (60.1 s, 2 samples)\nSystem Call Overhead                        4220325.5 lps   (10.0 s, 7 samples)\n\nSystem Benchmarks Index Values               BASELINE       RESULT    INDEX\nDhrystone 2 using register variables         116700.0  143065661.3  12259.3\nDouble-Precision Whetstone                       55.0      27992.6   5089.6\nExecl Throughput                                 43.0      10179.2   2367.2\nFile Copy 1024 bufsize 2000 maxblocks          3960.0    1466600.5   3703.5\nFile Copy 256 bufsize 500 maxblocks            1655.0    1156288.5   6986.6\nFile Copy 4096 bufsize 8000 maxblocks          5800.0    1324433.5   2283.5\nPipe Throughput                               12440.0    5951631.5   4784.3\nPipe-based Context Switching                   4000.0     787295.0   1968.2\nProcess Creation                                126.0      15263.8   1211.4\nShell Scripts (1 concurrent)                     42.4      18098.8   4268.6\nShell Scripts (8 concurrent)                      6.0       2333.7   3889.6\nSystem Call Overhead                          15000.0    4220325.5   2813.6\n                                                                   ========\nSystem Benchmarks Index Score                                        3594.4\n<\/code><\/pre>\n\n\n\n<p>CPU\u6e29\u5ea6\u306f50\u5ea6\u3092\u8d85\u3048\u305f\u3002\u30d5\u30a1\u30f3\u304c\u56de\u3063\u305f\u3093\u3060\u308d\u3046\u306d\u3002ssh\u3067\u63a5\u7d9a\u3057\u3066\u5b9f\u884c\u3057\u305f\u306e\u3067\u3001\u5b9f\u7269\u3092\u898b\u3066\u3044\u306a\u3044\u3002<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"644\" src=\"https:\/\/www.aqua-informatics.jp\/wp-content\/uploads\/2024\/03\/image-1-1024x644.png\" alt=\"\" class=\"wp-image-1059\" srcset=\"https:\/\/www.aqua-informatics.jp\/wp-content\/uploads\/2024\/03\/image-1-1024x644.png 1024w, https:\/\/www.aqua-informatics.jp\/wp-content\/uploads\/2024\/03\/image-1-300x189.png 300w, https:\/\/www.aqua-informatics.jp\/wp-content\/uploads\/2024\/03\/image-1-768x483.png 768w, https:\/\/www.aqua-informatics.jp\/wp-content\/uploads\/2024\/03\/image-1.png 1418w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n","protected":false},"excerpt":{"rendered":"<p>CPU\u6e29\u5ea6\u306f50\u5ea6\u3092\u8d85\u3048\u305f\u3002\u30d5\u30a1\u30f3\u304c\u56de\u3063\u305f\u3093\u3060\u308d\u3046\u306d\u3002ssh\u3067\u63a5\u7d9a\u3057\u3066\u5b9f\u884c\u3057\u305f\u306e\u3067\u3001\u5b9f\u7269\u3092\u898b\u3066\u3044\u306a\u3044\u3002<\/p>\n","protected":false},"author":1,"featured_media":92,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_vk_print_noindex":"","sitemap_hide":"","_veu_custom_css":"","veu_display_promotion_alert":"common","vkexunit_cta_each_option":"","_lightning_design_setting":{"layout":"default"},"footnotes":""},"categories":[7],"tags":[],"class_list":["post-1056","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-linux"],"veu_head_title_object":{"title":"","add_site_title":""},"_links":{"self":[{"href":"https:\/\/www.aqua-informatics.jp\/index.php?rest_route=\/wp\/v2\/posts\/1056","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.aqua-informatics.jp\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.aqua-informatics.jp\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.aqua-informatics.jp\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.aqua-informatics.jp\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1056"}],"version-history":[{"count":2,"href":"https:\/\/www.aqua-informatics.jp\/index.php?rest_route=\/wp\/v2\/posts\/1056\/revisions"}],"predecessor-version":[{"id":1060,"href":"https:\/\/www.aqua-informatics.jp\/index.php?rest_route=\/wp\/v2\/posts\/1056\/revisions\/1060"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.aqua-informatics.jp\/index.php?rest_route=\/wp\/v2\/media\/92"}],"wp:attachment":[{"href":"https:\/\/www.aqua-informatics.jp\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1056"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.aqua-informatics.jp\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1056"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.aqua-informatics.jp\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1056"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}