[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-315875-105":59,"doc-detail-315875-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","statamp-performance-report","Stata/MP Performance Report","","Stata/MP is designed to exploit multicore and multiprocessor computers by distributing computationally demanding tasks across all available cores, delivering significantly faster execution than single-core Stata. The report quantifies overall efficiency at about 74% and about 79% efficiency for estimation commands, including speedups across 2, 4, and 8 cores. It also explains why perfect scalability is not achievable, outlines how performance is measured relative to single-core speed, and summarizes command-level results and variability across platforms.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/statamp-performance-report/315875/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/statamp-performance-report/315875.png","ImageObject",300,407,{"name":92,"@type":93},"Eden","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-05","2026-09-20",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",11,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What problem does Stata/MP address, and how does it improve speed?","Question",{"text":113,"@type":114},"Stata/MP targets multicore and multiprocessor systems by partitioning work across multiple cores. By distributing demanding tasks, it runs substantially faster than single-core execution.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How is performance measured in the report?",{"text":118,"@type":114},"Performance is measured as speed relative to speed on a single core. A value of 2 indicates twice as fast as a single core, and 4 indicates four times as fast.",{"name":120,"@type":111,"acceptedAnswer":121},"Why doesn’t Stata/MP achieve perfect scalability when adding more cores?",{"text":122,"@type":114},"Perfect scalability is limited because some computations cannot be partitioned into parallel processes, partitioning may require additional computing time, and multicore/multiprocessor systems duplicate cores/processors but not all other resources.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},315875,1790028038,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":145,"read_time":146,"preview_page_count":4},1374391974468,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","1 Summary  \nStata/MP 1 is the version of Stata that is programmed to take full advantage of multicore and multiprocessor computers. Stata/MP distributes many of Stata’s most computationally demanding tasks across all the cores in your computer and thereby runs faster—much faster.  \nIn a perfect world, software would run 2 times faster on 2 cores, 3 times faster on 3 cores, and soon. Stata/MP achieves about 74% efficiency. It runs 1.6 times faster on 2 cores, 2.3 times faster on 4 cores, and 2.9 times faster on 8 cores (see figure 1) . Half the commands run faster than that. The other half run slower than the median speedup, and some of those commands are not sped up at all,  \neither because they are inherently sequential (most time-series commands) or because they have not been parallelized (graphics, mixed) .  \nIn terms of evaluating average performance improvement, commands that take longer to run—such as estimation commands—are of greater importance. When estimation commands are taken as a group, Stata/MP achievesan even greater efficiency of approximately 79% . Taken at the median, estimation commands run 1.7 times faster on 2 cores, 2.6 times faster on 4 cores, and 3.4 times faster on 8 cores. Stata/MP supports up to 64 cores.  \nThis paper provides a detailed report on the performance of Stata/MP. Command-bycommand performance assessments are provided in section 8.  \n\n| Speed relat ive to speed of single core | 8\u003Cbr>4\u003Cbr>2\u003Cbr>1 | \u003Cbr>\u003Cbr>\u003Cbr>Possible performance region Theoretical upper bound\u003Cbr>Logistic regression\u003Cbr>Median\u003Cbr>(estimation)\u003Cbr>Median\u003Cbr>(all commands)\u003Cbr>Lower bound (no improvement)\u003Cbr>\u003Cbr>\u003Cbr>1 2 4 8 |\n| --- | --- | --- |\n| Number of cores |  |  |\n\nFigure 1 . Performance of Stata/MP. Speed on multiple cores relative to speed on a single core.  \n1. Support for this effort was partially provided by the U.S. National Institutes of Health, National Institute on Aging grants 1R43AG019542-01A1, 2R44AG019542-02, and 5R44AG019542-03 . We also thank Cornell Institute for Social and Economic Research (CISER) at Cornell University for graciously providing access to several highly parallel SMP platforms. CISER staff, in particular John Abowd, Kim Burlingame, Janet Heslop, and Lars Vilhuber, were exceptionally helpful in scheduling time and helping with configuration. The views expressed here do not necessarily reflect those of any of the parties thanked above.  \nRevision 3.4.0 25sep2023  \nStata/MP Performance Report Table of contents (2)  \n2 Table of contents  \n1 Summary 1  \n2 Table of contents 2  \n3 Introduction 3  \n4 Parallel computing hardware 4  \n5 Constructing Stata/MP 5  \n6 Measuring Stata/MP’s performance 5  \n7 Performance summary 7  \n8 Stata/MP performance, command by command 14  \n9 Performance variability across computing platforms 53  \n10 Hyperthreading—single-and multiple-processor platforms 53  \nA Performance assessment graphs for desktop computers 56  \nB Performance assessment graphs for high-end servers 222  \nC Command names and descriptions 296  \nD Problem sizes 331  \nE Commands not assessed 367  \nF Mata 369  \nG GLLAMM 370  \nRevision 3.4.0 25sep2023  \n3 Introduction  \nStata/MP was designed to take advantage of computers with multiple cores and multiple processors by partitioning the work among the multiple cores. From the outset, Stata/MP was required to be 100% compatible with all other flavors of Stata, including Stata/SE and Stata/BE. Stata/MP was also required to run all scripts, user-written programs, and analyses that run under existing Stata without any change or special action on the user’s part.  \nStata/MP runs on multicore and multiprocessor computers, including computers running MS Windows, Intel-based Mac OS computers, and Linux computers.  \nWith multiple cores, one might expect to achieve the theoretical upper bound of doubling the speed by doubling the number of cores—2 cores run twice as fast as 1, 4 run twice as fast as 2, and so on. However, there are three reasons why such perfect sc","cbCaihedZdQU5Y0k","https://ap.wps.com/l/cbCaihedZdQU5Y0k","pdf",8652318,378,"English","# Summary\n# Table of contents\n# Introduction\n# Parallel computing hardware\n# Constructing Stata/MP\n# Measuring Stata/MP’s performance\n# Performance summary\n# Stata/MP performance, command by command\n# Performance variability across computing platforms\n# Hyperthreading—single-and multiple-processor platforms\n# Appendix: Performance assessment graphs for desktop computers\n# Appendix: Performance assessment graphs for high-end servers","[{\"question\":\"What problem does Stata/MP address, and how does it improve speed?\",\"answer\":\"Stata/MP targets multicore and multiprocessor systems by partitioning work across multiple cores. By distributing demanding tasks, it runs substantially faster than single-core execution.\"},{\"question\":\"How is performance measured in the report?\",\"answer\":\"Performance is measured as speed relative to speed on a single core. A value of 2 indicates twice as fast as a single core, and 4 indicates four times as fast.\"},{\"question\":\"Why doesn’t Stata/MP achieve perfect scalability when adding more cores?\",\"answer\":\"Perfect scalability is limited because some computations cannot be partitioned into parallel processes, partitioning may require additional computing time, and multicore/multiprocessor systems duplicate cores/processors but not all other resources.\"}]","Stata/MP Performance Report | PDF",1789907523,953]