{"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\/pt\/blog\/news\/otkryt-kod-sql-dvizhka-blazingsql-ispolzuyushhego-gpu-dlya-uskoreniya","title":{"rendered":"Foi aberto o c\u00f3digo do motor SQL BlazingSQL, que utiliza GPU para acelera\u00e7\u00e3o","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Anunciado<\/a><\/noindex> sobre a abertura dos c\u00f3digos-fonte do motor SQL BlazingSQL<\/a><\/noindex>, que utiliza GPU para acelerar o processamento de dados. BlazingSQL n\u00e3o \u00e9 um SGBD completo, mas \u00e9 posicionado como um motor para an\u00e1lise e processamento de grandes conjuntos de dados, compar\u00e1vel em suas tarefas com Apache Spark<\/a><\/noindex>. O c\u00f3digo foi escrito na linguagem Python e \u00e9 aberto<\/a><\/noindex> est\u00e3o sob a licen\u00e7a Apache 2.0. <\/p>\n

BlazingSQL \u00e9 adequado para realizar consultas anal\u00edticas \u00fanicas em grandes conjuntos de dados (dezembros de gigabytes), armazenados em formatos tabulares (por exemplo, logs, estat\u00edsticas de NetFlow, etc.). BlazingSQL pode executar consultas em arquivos brutos nos formatos CSV e Apache Parquet, localizados em sistemas de arquivos de rede e na nuvem, como HDFS e AWS S3, transmitindo diretamente o resultado para a mem\u00f3ria GPU. Gra\u00e7as \u00e0 paraleliza\u00e7\u00e3o de opera\u00e7\u00f5es na GPU e ao uso de mem\u00f3ria de v\u00eddeo mais r\u00e1pida, a execu\u00e7\u00e3o de consultas no BlazingSQL \u00e9 realizada at\u00e9 20 vezes<\/a><\/noindex> mais r\u00e1pido do que no Apache Spark.<\/p>\n

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

Para trabalhar com GPUs, \u00e9 utilizado um conjunto de abertos<\/a><\/noindex> bibliotecas RAPIDS<\/a><\/noindex>, que permite criar aplica\u00e7\u00f5es para processamento de dados e an\u00e1lise, realizadas inteiramente do lado da GPU (um interfacie Python<\/a><\/noindex> para usar primitivos de baixo n\u00edvel do CUDA e paralelizar c\u00e1lculos). <\/p>\n

BlazingSQL oferece a possibilidade de usar SQL em vez da API de processamento de dados cuUDF<\/a><\/noindex> (baseado em Apache Arrow<\/a><\/noindex>), utilizado no RAPIDS. BlazingSQL \u00e9 uma camada adicional que opera sobre cuDF e utiliza a biblioteca cuIO para ler dados do disco. As consultas SQL s\u00e3o traduzidas em chamadas de fun\u00e7\u00f5es cuUDF, permitindo carregar dados na GPU e realizar opera\u00e7\u00f5es de jun\u00e7\u00e3o, agrega\u00e7\u00e3o e filtragem. \u00c9 suportada a cria\u00e7\u00e3o de configura\u00e7\u00f5es distribu\u00eddas cobrindo milhares de GPUs. <\/p>\n

BlazingSQL simplifica significativamente o trabalho com dados \u2014 em vez de uma centena de chamadas de fun\u00e7\u00f5es cuDF, \u00e9 poss\u00edvel resolver tudo com uma \u00fanica consulta SQL. A aplica\u00e7\u00e3o de SQL permite garantir a integra\u00e7\u00e3o do RAPIDS com sistemas de an\u00e1lise existentes, sem a necessidade de escrever manipuladores espec\u00edficos e sem recorrer ao carregamento intermedi\u00e1rio de dados em um banco de dados adicional, mas
\nmantendo plena compatibilidade com todas as partes do RAPIDS, traduzindo a funcionalidade existente em SQL e garantindo desempenho no n\u00edvel do cuDF. Incluindo suporte para integra\u00e7\u00e3o com bibliotecas XGBoost<\/a><\/noindex> e cuML<\/a><\/noindex> para resolver tarefas de an\u00e1lise e aprendizado de m\u00e1quina. <\/p>\n

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

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

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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\udd47C\u00f3digo aberto do mecanismo SQL BlazingSQL, que utiliza 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