{"id":2725,"date":"2008-09-06T13:14:11","date_gmt":"2008-09-06T12:14:11","guid":{"rendered":"https:\/\/www.tecnomagazine.it\/tech\/?p=2725"},"modified":"2008-09-21T21:00:13","modified_gmt":"2008-09-21T20:00:13","slug":"folding-home-raggiunge-un-traguardo-storico-grazie-a-nvidia-cuda","status":"publish","type":"post","link":"https:\/\/www.tecnomagazine.it\/tech\/2725\/folding-home-raggiunge-un-traguardo-storico-grazie-a-nvidia-cuda\/","title":{"rendered":"Folding@home raggiunge un traguardo storico grazie a NVIDIA CUDA"},"content":{"rendered":"<div class=\"4032636bd480207a9641f06a0353772e\" data-index=\"1\" style=\"float: none; margin:10px 0 10px 0; text-align:center;\">\n<!--COOKIE_POLICY_BEGIN-->\r\n<script async src=\"\/\/pagead2.googlesyndication.com\/pagead\/js\/adsbygoogle.js\"><\/script>\r\n<!-- Tecnomagine Grande -->\r\n<ins class=\"adsbygoogle\"\r\n     style=\"display:block\"\r\n     data-ad-client=\"ca-pub-4884298544160548\"\r\n     data-ad-slot=\"6528327022\"\r\n     data-ad-format=\"auto\"><\/ins>\r\n<script>\r\n(adsbygoogle = window.adsbygoogle || []).push({});\r\n<\/script>\r\n<!--COOKIE_POLICY_END-->\n<\/div>\n<p style=\"150%;\" align=\"justify\"><a href=\"https:\/\/www.tecnomagazine.it\/tech\/wp-content\/uploads\/2008\/09\/nvidia_logo.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\"alignright size-full wp-image-2925\" src=\"https:\/\/www.tecnomagazine.it\/tech\/wp-content\/uploads\/2008\/09\/nvidia_logo.jpg\" alt=\"\" width=\"274\" height=\"263\" srcset=\"https:\/\/www.tecnomagazine.it\/tech\/wp-content\/uploads\/2008\/09\/nvidia_logo.jpg 625w, https:\/\/www.tecnomagazine.it\/tech\/wp-content\/uploads\/2008\/09\/nvidia_logo-300x288.jpg 300w\" sizes=\"auto, (max-width: 274px) 100vw, 274px\" \/><\/a>\r\n<div class=\"4032636bd480207a9641f06a0353772e\" data-index=\"1\" style=\"float: none; margin:10px 0 10px 0; text-align:center;\">\n<!--COOKIE_POLICY_BEGIN-->\r\n<script async src=\"\/\/pagead2.googlesyndication.com\/pagead\/js\/adsbygoogle.js\"><\/script>\r\n<!-- Tecnomagine Grande -->\r\n<ins class=\"adsbygoogle\"\r\n     style=\"display:block\"\r\n     data-ad-client=\"ca-pub-4884298544160548\"\r\n     data-ad-slot=\"6528327022\"\r\n     data-ad-format=\"auto\"><\/ins>\r\n<script>\r\n(adsbygoogle = window.adsbygoogle || []).push({});\r\n<\/script>\r\n<!--COOKIE_POLICY_END-->\n<\/div>\n\r\nLe <strong>GPU NVIDIA<\/strong> stanno offrendo <strong>pi\u00f9 di 1 petaflop del potere di lavorazione<\/strong> alla <em><strong>Stanford University&#8217;s Folding@home<\/strong><\/em>, la quale ha distribuito applicazioni computing come quelle della settimana scorsa, secondo le statistiche pubblicate da <strong>Stanford<\/strong>. Le <strong>attivit\u00e0 di NVIDIA\u00ae GPU<\/strong> offrono <strong>pi\u00f9 di 1.25 petaflops<\/strong>, ovvero il <strong>42% del totale potere di lavorazione dell&#8217;applicazione<\/strong> che cerca di capire come le modifiche delle proteine colpiscono il corpo umano.<\/p>\n<p style=\"150%;\" align=\"justify\">Il <strong>contributo di NVIDIA<\/strong>, quasi la met\u00e0 del potere di lavorazione su <strong>Folding@home<\/strong>, \u00e8 offerto da solamente <strong>11,370 dei microprocessori attivi totali usati nel progetto.<\/strong> Per avere un termine di paragone, <strong>208,268 CPU che supportano Windows attivi<\/strong>, offrono <strong>198 teraflops<\/strong> &#8211; <strong>solo 6% del potere di lavorazione totale nel progetto<\/strong>.<\/p>\n<p style=\"150%;\" align=\"justify\"><strong>Stanford University <\/strong>ha rilasciato un <strong>Folding@home client appositamente per NVIDIA GPU a giugno<\/strong> cos\u00ec da raggiungere nuovi vertici prestazionali insolamente alcuni mesi. Sviluppato usando <strong>NVIDIA CUDA\u2122<\/strong>, un <strong>ambiente di programmazione di linguaggio C<\/strong> per molti centri di architetture parallele, il <strong>porting del Folding@home client\u00a0 in\u00a0 CUDA <\/strong> ha offerto pi\u00f9 potere di lavorazione rispetto ad altre architetture nella storia del progetto.<\/p>\n<p style=\"150%;\" align=\"justify\"><!--more-->&#8220;<em>Come mostrano queste statistiche, l&#8217;impatto di NVIDIA GPU sulle simulazioni di protein folding sono state straordinarie<\/em>&#8220;, afferma <strong>Vijay Pande<\/strong>, professore associato di chimica alla <strong>Stanford University<\/strong> e direttore del progetto <strong>Folding@home<\/strong>. &#8220;<em>Squadre che utilizzano il folding con NVIDIA GPU stanno avendo spinte enormi nella loro ricerca e questo sta aiutando ad accelerare significativamente i progetti<\/em>&#8220;.<\/p>\n<p style=\"150%;\" align=\"justify\">&#8220;<em>Applicazioni come <strong>Folding@home<\/strong> sono solo l&#8217;inizio, noi stiamo vedendo ogni giorno sempre pi\u00f9 esempi di problemi computing che sono risolti da CUDA e le nostre tecnologie GPU<\/em>&#8220;, sostiene <em>Michael Steele<\/em>,\u00a0 general manager di visual consumer solution di <em><strong>NVIDIA<\/strong><\/em>. &#8220;<em>Io so che ognuno in NVIDIA sta seguendo da vicino i progressi del progetto Folding@home dal rilascio di CUDA\u00a0 per le nostre GPU e noi ci siamo soddisfatti nel poter osservare quale contributo significativo loro siano in grado di portare<\/em>&#8220;<\/p>\n<p style=\"150%;\" align=\"justify\">Il <strong>programma computing Folding@home distribuito dalla Stanford University<\/strong> \u00e8 diventato il <strong>punto di forza nella ricerca di cure per malattie mortali come il cancro, la fibrosi cistica e il morbo di Parkinson<\/strong>, combinando la <strong>potenza del computing di milioni di microprocessori per simulare il protein folding.<\/strong> Il<strong> progetto Folding@home <\/strong>\u00e8 l&#8217;ultimo esempio dell&#8217;<strong>ampio elenco delle applicazioni non-gaming per unit\u00e0 di lavorazione di grafiche (GPU).<\/strong> Applicando il <strong>Folding@home client su una GPU NVIDIA<\/strong>, le <strong>simulazioni protein folding<\/strong> possono essere fatte <strong>140 volte pi\u00f9 velocemente rispetto ad alcune tradizionali CPU di oggi.<\/strong><\/p>\n<p style=\"150%;\" align=\"justify\"><em><strong>Tavola completa delle statistiche:<\/strong><\/em><\/p>\n<p style=\"150%;\" align=\"justify\">\n<table style=\"131px;\" border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"600\">\n<col width=\"152\"><\/col>\n<col width=\"160\"><\/col>\n<col width=\"160\"><\/col>\n<col width=\"154\"><\/col>\n<tbody>\n<tr valign=\"top\">\n<td width=\"152\" bgcolor=\"#000000\">\n<p style=\"1pt medium 1pt 1pt solid none solid solid #000000 -moz-use-text-color #000000 #000000;\" lang=\"en-US\"><span style=\"#ffffff;\"><span style=\"Times New Roman;\"><span style=\"x-small;\"><strong>OS \t\t\tType<\/strong><\/span><\/span><\/span><\/p>\n<\/td>\n<td width=\"160\" bgcolor=\"#000000\">\n<p style=\"1pt medium 1pt 1pt solid none solid solid #000000 -moz-use-text-color #000000 #000000;\" lang=\"en-US\" align=\"right\"><span style=\"#ffffff;\"><span style=\"Times New Roman;\"><span style=\"x-small;\"><strong>Current \t\t\tTFLOPS<\/strong><\/span><\/span><\/span><\/p>\n<\/td>\n<td width=\"160\" bgcolor=\"#000000\">\n<p style=\"1pt medium 1pt 1pt solid none solid solid #000000 -moz-use-text-color #000000 #000000;\" lang=\"en-US\" align=\"right\"><span style=\"#ffffff;\"><span style=\"Times New Roman;\"><span style=\"x-small;\"><strong>Active \t\t\tProcessors<\/strong><\/span><\/span><\/span><\/p>\n<\/td>\n<td width=\"154\" bgcolor=\"#000000\">\n<p style=\"0.05cm 0.19cm;\" lang=\"en-US\" align=\"right\"><span style=\"#ffffff;\"><span style=\"Times New Roman;\"><span style=\"x-small;\"><strong>Total \t\t\tProcessors<\/strong><\/span><\/span><\/span><\/p>\n<\/td>\n<\/tr>\n<tr valign=\"top\">\n<td width=\"152\">\n<p style=\"medium medium 1pt 1pt none none solid solid -moz-use-text-color -moz-use-text-color #000000 #000000;\" lang=\"en-US\"><span style=\"Times New Roman;\"><span style=\"x-small;\"><strong>Windows<\/strong><\/span><\/span><\/p>\n<\/td>\n<td width=\"160\">\n<p style=\"medium medium 1pt 1pt none none solid solid -moz-use-text-color -moz-use-text-color #000000 #000000;\" lang=\"en-US\" align=\"right\"><span style=\"Times New Roman;\"><span style=\"x-small;\">198<\/span><\/span><\/p>\n<\/td>\n<td width=\"160\">\n<p style=\"medium medium 1pt 1pt none none solid solid -moz-use-text-color -moz-use-text-color #000000 #000000;\" lang=\"en-US\" align=\"right\"><span style=\"Times New Roman;\"><span style=\"x-small;\">208,268<\/span><\/span><\/p>\n<\/td>\n<td width=\"154\">\n<p style=\"medium 1pt 1pt none solid solid -moz-use-text-color #000000 #000000;\" lang=\"en-US\" align=\"right\"><span style=\"Times New Roman;\"><span style=\"x-small;\">2,134,966<\/span><\/span><\/p>\n<\/td>\n<\/tr>\n<tr valign=\"top\">\n<td width=\"152\">\n<p style=\"medium medium 1pt 1pt none none solid solid -moz-use-text-color -moz-use-text-color #000000 #000000;\" lang=\"pt-BR\"><span style=\"Times New Roman;\"><span style=\"x-small;\"><strong>Mac OS X \/ Power PC<\/strong><\/span><\/span><\/p>\n<\/td>\n<td width=\"160\">\n<p style=\"medium medium 1pt 1pt none none solid solid -moz-use-text-color -moz-use-text-color #000000 #000000;\" align=\"right\"><span style=\"Times New Roman;\"><span style=\"x-small;\">7<\/span><\/span><\/p>\n<\/td>\n<td width=\"160\">\n<p style=\"medium medium 1pt 1pt none none solid solid -moz-use-text-color -moz-use-text-color #000000 #000000;\" align=\"right\"><span style=\"Times New Roman;\"><span style=\"x-small;\">8,226<\/span><\/span><\/p>\n<\/td>\n<td width=\"154\">\n<p style=\"medium 1pt 1pt none solid solid -moz-use-text-color #000000 #000000;\" align=\"right\"><span style=\"Times New Roman;\"><span style=\"x-small;\">118,817<\/span><\/span><\/p>\n<\/td>\n<\/tr>\n<tr valign=\"top\">\n<td width=\"152\">\n<p style=\"medium medium 1pt 1pt none none solid solid -moz-use-text-color -moz-use-text-color #000000 #000000;\"><span style=\"Times New Roman;\"><span style=\"x-small;\"><strong>Mac OS X \/ Intel<\/strong><\/span><\/span><\/p>\n<\/td>\n<td width=\"160\">\n<p style=\"medium medium 1pt 1pt none none solid solid -moz-use-text-color -moz-use-text-color #000000 #000000;\" align=\"right\"><span style=\"Times New Roman;\"><span style=\"x-small;\">19<\/span><\/span><\/p>\n<\/td>\n<td width=\"160\">\n<p style=\"medium medium 1pt 1pt none none solid solid -moz-use-text-color -moz-use-text-color #000000 #000000;\" align=\"right\"><span style=\"Times New Roman;\"><span style=\"x-small;\">6,264<\/span><\/span><\/p>\n<\/td>\n<td width=\"154\">\n<p style=\"medium 1pt 1pt none solid solid -moz-use-text-color #000000 #000000;\" align=\"right\"><span style=\"Times New Roman;\"><span style=\"x-small;\">58,856<\/span><\/span><\/p>\n<\/td>\n<\/tr>\n<tr valign=\"top\">\n<td width=\"152\">\n<p style=\"medium medium 1pt 1pt none none solid solid -moz-use-text-color -moz-use-text-color #000000 #000000;\"><span style=\"Times New Roman;\"><span style=\"x-small;\"><strong>Linux<\/strong><\/span><\/span><\/p>\n<\/td>\n<td width=\"160\">\n<p style=\"medium medium 1pt 1pt none none solid solid -moz-use-text-color -moz-use-text-color #000000 #000000;\" align=\"right\"><span style=\"Times New Roman;\"><span style=\"x-small;\">61<\/span><\/span><\/p>\n<\/td>\n<td width=\"160\">\n<p style=\"medium medium 1pt 1pt none none solid solid -moz-use-text-color -moz-use-text-color #000000 #000000;\" align=\"right\"><span style=\"Times New Roman;\"><span style=\"x-small;\">35,903<\/span><\/span><\/p>\n<\/td>\n<td width=\"154\">\n<p style=\"medium 1pt 1pt none solid solid -moz-use-text-color #000000 #000000;\" align=\"right\"><span style=\"Times New Roman;\"><span style=\"x-small;\">325,643<\/span><\/span><\/p>\n<\/td>\n<\/tr>\n<tr valign=\"top\">\n<td width=\"152\">\n<p style=\"medium medium 1pt 1pt none none solid solid -moz-use-text-color -moz-use-text-color #000000 #000000;\"><span style=\"Times New Roman;\"><span style=\"x-small;\"><strong>ATI GPU<\/strong><\/span><\/span><\/p>\n<\/td>\n<td width=\"160\">\n<p style=\"medium medium 1pt 1pt none none solid solid -moz-use-text-color -moz-use-text-color #000000 #000000;\" align=\"right\"><span style=\"Times New Roman;\"><span style=\"x-small;\">334<\/span><\/span><\/p>\n<\/td>\n<td width=\"160\">\n<p style=\"medium medium 1pt 1pt none none solid solid -moz-use-text-color -moz-use-text-color #000000 #000000;\" align=\"right\"><span style=\"Times New Roman;\"><span style=\"x-small;\">3,032<\/span><\/span><\/p>\n<\/td>\n<td width=\"154\">\n<p style=\"medium 1pt 1pt none solid solid -moz-use-text-color #000000 #000000;\" align=\"right\"><span style=\"Times New Roman;\"><span style=\"x-small;\">6,148<\/span><\/span><\/p>\n<\/td>\n<\/tr>\n<tr valign=\"top\">\n<td width=\"152\" bgcolor=\"#d9d9d9\">\n<p style=\"medium medium 1pt 1pt none none solid solid -moz-use-text-color -moz-use-text-color #000000 #000000;\"><span style=\"Times New Roman;\"><span style=\"x-small;\"><strong>NVIDIA GPU<\/strong><\/span><\/span><\/p>\n<\/td>\n<td width=\"160\" bgcolor=\"#d9d9d9\">\n<p style=\"medium medium 1pt 1pt none none solid solid -moz-use-text-color -moz-use-text-color #000000 #000000;\" align=\"right\"><span style=\"Times New Roman;\"><span style=\"x-small;\">1,251<\/span><\/span><\/p>\n<\/td>\n<td width=\"160\" bgcolor=\"#d9d9d9\">\n<p style=\"medium medium 1pt 1pt none none solid solid -moz-use-text-color -moz-use-text-color #000000 #000000;\" align=\"right\"><span style=\"Times New Roman;\"><span style=\"x-small;\">11,370<\/span><\/span><\/p>\n<\/td>\n<td width=\"154\" bgcolor=\"#d9d9d9\">\n<p style=\"medium 1pt 1pt none solid solid -moz-use-text-color #000000 #000000;\" align=\"right\"><span style=\"Times New Roman;\"><span style=\"x-small;\">17,152<\/span><\/span><\/p>\n<\/td>\n<\/tr>\n<tr valign=\"top\">\n<td width=\"152\">\n<p style=\"medium medium 1pt 1pt none none solid solid -moz-use-text-color -moz-use-text-color #000000 #000000;\"><span style=\"Times New Roman;\"><span style=\"x-small;\"><strong>PlayStation 3<\/strong><\/span><\/span><\/p>\n<\/td>\n<td width=\"160\">\n<p style=\"medium medium 1pt 1pt none none solid solid -moz-use-text-color -moz-use-text-color #000000 #000000;\" align=\"right\"><span style=\"Times New Roman;\"><span style=\"x-small;\">1,080<\/span><\/span><\/p>\n<\/td>\n<td width=\"160\">\n<p style=\"medium medium 1pt 1pt none none solid solid -moz-use-text-color -moz-use-text-color #000000 #000000;\" align=\"right\"><span style=\"Times New Roman;\"><span style=\"x-small;\">38,286<\/span><\/span><\/p>\n<\/td>\n<td width=\"154\">\n<p style=\"medium 1pt 1pt none solid solid -moz-use-text-color #000000 #000000;\" align=\"right\"><span style=\"Times New Roman;\"><span style=\"x-small;\">582,800<\/span><\/span><\/p>\n<\/td>\n<\/tr>\n<tr valign=\"top\">\n<td width=\"152\">\n<p style=\"medium medium 1pt 1pt none none solid solid -moz-use-text-color -moz-use-text-color #000000 #000000;\"><span style=\"Times New Roman;\"><span style=\"x-small;\"><strong>Total<\/strong><\/span><\/span><\/p>\n<\/td>\n<td width=\"160\">\n<p style=\"medium medium 1pt 1pt none none solid solid -moz-use-text-color -moz-use-text-color #000000 #000000;\" align=\"right\"><span style=\"Times New Roman;\"><span style=\"x-small;\">2,950<\/span><\/span><\/p>\n<\/td>\n<td width=\"160\">\n<p style=\"medium medium 1pt 1pt none none solid solid -moz-use-text-color -moz-use-text-color #000000 #000000;\" align=\"right\"><span style=\"Times New Roman;\"><span style=\"x-small;\">311,349<\/span><\/span><\/p>\n<\/td>\n<td width=\"154\">\n<p style=\"medium 1pt 1pt none solid solid -moz-use-text-color #000000 #000000;\" align=\"right\"><span style=\"Times New Roman;\"><span style=\"x-small;\">3,244,382<\/span><\/span><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p style=\"150%;\">Fonte: <strong><a href=\"http:\/\/fah-web.stanford.edu\/cgi-bin\/main.py?qtype=osstats\">http:\/\/fah-web.stanford.edu\/cgi-bin\/main.py?qtype=osstats<\/a><\/strong>, 19 Agosto 2008.<\/p>\n\n<div style=\"font-size: 0px; height: 0px; line-height: 0px; margin: 0; padding: 0; clear: both;\"><\/div>","protected":false},"excerpt":{"rendered":"<p>Le GPU NVIDIA stanno offrendo pi\u00f9 di 1 petaflop del<\/p>\n","protected":false},"author":16,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11,1,12],"tags":[1432,1433,1434],"class_list":["post-2725","post","type-post","status-publish","format-standard","hentry","category-computer","category-curiosita","category-hardware","tag-foldinghome","tag-nvidia-gpu","tag-petaflop"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Folding@home raggiunge un traguardo storico grazie a NVIDIA CUDA - TecnoMagazine<\/title>\n<meta name=\"description\" content=\"Le GPU NVIDIA stanno offrendo pi\u00f9 di 1 petaflop del potere di lavorazione alla Stanford University&#039;s Folding@home, la quale ha distribuito applicazioni\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link 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