{"id":36843,"date":"2019-10-31T22:14:11","date_gmt":"2019-10-31T19:14:11","guid":{"rendered":"https:\/\/prohoster.info\/blog\/otkryt-kod-sql-dvizhka-blazingsql-ispolzuyushhego-gpu-dlya-uskoreniya\/"},"modified":"2019-10-31T22:14:11","modified_gmt":"2019-10-31T19:14:11","slug":"otkryt-kod-sql-dvizhka-blazingsql-ispolzuyushhego-gpu-dlya-uskoreniya","status":"publish","type":"post","link":"https:\/\/prohoster.info\/da\/blog\/news\/otkryt-kod-sql-dvizhka-blazingsql-ispolzuyushhego-gpu-dlya-uskoreniya","title":{"rendered":"Koden til SQL-motoren BlazingSQL, der bruger GPU til acceleration, er blevet \u00e5bnet.","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Annonceret<\/a><\/noindex> om \u00e5bningen af SQL-motorens kildekoder BlazingSQL<\/a><\/noindex>, der bruger GPU til at accelerere datah\u00e5ndtering. BlazingSQL er ikke en fuldgyldig DBMS, men positioneres som en motor til analyse og behandling af store datas\u00e6t, som kan sammenlignes med Apache Spark<\/a><\/noindex>. Koden er skrevet i Python og \u00e5bnet<\/a><\/noindex> er under Apache 2.0-licensen. <\/p>\n

BlazingSQL er velegnet til at udf\u00f8re enkeltst\u00e5ende analyserapporter over store datas\u00e6t (titusinder af gigabytes), som opbevares i tabelformater (f.eks. logs, NetFlow-statistik osv.). BlazingSQL kan udf\u00f8re foresp\u00f8rgsler fra r\u00e5filer i CSV- og Apache Parquet-formater, der er placeret i netv\u00e6rks- og cloud-filsystemer som HDSF og AWS S3, og overf\u00f8re resultaterne direkte til GPU'en. Takket v\u00e6re parallelisering af operationer p\u00e5 GPU'en og brugen af hurtigere videohukommelse, udf\u00f8res foresp\u00f8rgsler i BlazingSQL op til 20 gange<\/a><\/noindex> hurtigere end i Apache Spark.<\/p>\n

\"Koden<\/a><\/noindex><\/center><\/p>\n

Til arbejde med GPU anvendes et s\u00e6t \u00e5bne<\/a><\/noindex> biblioteker RAPIDS<\/a><\/noindex>, som g\u00f8r det muligt at skabe applikationer til datah\u00e5ndtering og analyse, der udf\u00f8res helt p\u00e5 GPU-siden (et Python-interface<\/a><\/noindex> til brug af lave niveauer af CUDA-primitiver og parallelisering af beregninger tilbydes). <\/p>\n

BlazingSQL giver mulighed for at bruge SQL i stedet for datah\u00e5ndterings-API'en cuUDF<\/a><\/noindex> (baseret p\u00e5 Apache Arrow<\/a><\/noindex>), der anvendes i RAPIDS. BlazingSQL fungerer som et ekstra lag over cuDF og bruger cuIO-biblioteket til at l\u00e6se data fra disk. SQL-foresp\u00f8rgsler bliver oversat til kald til cuUDF-funktioner, som g\u00f8r det muligt at indl\u00e6se data p\u00e5 GPU'en og udf\u00f8re operationer som sammenl\u00e6gning, aggregering og filtrering. Underst\u00f8ttelse af oprettelse af distribuerede konfigurationer, der d\u00e6kker tusinder af GPU'er, er ogs\u00e5 til stede. <\/p>\n

BlazingSQL forenkler arbejdet med data betydeligt \u2014 i stedet for hundrede funktioner i cuDF kan man n\u00f8jes med \u00e9n SQL-foresp\u00f8rgsel. Brug af SQL muligg\u00f8r integration af RAPIDS med eksisterende analyisesystemer, uden at der skal skrives specifikke h\u00e5ndteringsprogrammer, og uden at der er behov for mellemlagring af data i en ekstra database, men
\nbevarer samtidig fuld kompatibilitet med alle dele af RAPIDS, ved at overs\u00e6tte eksisterende funktionalitet til SQL og sikre ydeevne p\u00e5 niveau med cuDF. Der er ogs\u00e5 sikret st\u00f8tte til integration med XGBoost<\/a><\/noindex> og cuML<\/a><\/noindex> til l\u00f8sning af analyse- og maskinl\u00e6ringsopgaver. <\/p>\n

<\/a><\/noindex><\/p>\n

Kilde: opennet.ru<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

\u041e\u0431\u044a\u044f\u0432\u043b\u0435\u043d\u043e \u043e\u0431 \u043e\u0442\u043a\u0440\u044b\u0442\u0438\u0438 \u0438\u0441\u0445\u043e\u0434\u043d\u044b\u0445 \u0442\u0435\u043a\u0441\u0442\u043e\u0432 SQL-\u0434\u0432\u0438\u0436\u043a\u0430 BlazingSQL, \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u044e\u0449\u0435\u0433\u043e GPU \u0434\u043b\u044f \u0443\u0441\u043a\u043e\u0440\u0435\u043d\u0438\u044f \u043e\u0431\u0440\u0430\u0431\u043e\u0442\u043a\u0438 \u0434\u0430\u043d\u043d\u044b\u0445. 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BlazingSQL \u043f\u043e\u0434\u0445\u043e\u0434\u0438\u0442 \u0434\u043b\u044f \u0432\u044b\u043f\u043e\u043b\u043d\u0435\u043d\u0438\u044f \u0435\u0434\u0438\u043d\u0438\u0447\u043d\u044b\u0445 \u0430\u043d\u0430\u043b\u0438\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0445 \u0437\u0430\u043f\u0440\u043e\u0441\u043e\u0432 \u043d\u0430\u0434 […]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":27603,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[702],"tags":[],"class_list":["post-36843","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\n","aioseo_head_json":{"title":"\ud83e\udd47Koden til SQL-motoren BlazingSQL, der bruger GPU til 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