{"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\/it\/blog\/novosti-interneta\/otkryt-kod-sql-dvizhka-blazingsql-ispolzuyushhego-gpu-dlya-uskoreniya","title":{"rendered":"Rilasciato il codice del motore SQL BlazingSQL, che utilizza GPU per l'accelerazione","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Annunciato<\/a><\/noindex> sull'apertura dei codici sorgente del motore SQL BlazingSQL<\/a><\/noindex>, che utilizza GPU per accelerare l'elaborazione dei dati. BlazingSQL non \u00e8 un vero e proprio DBMS, ma si presenta come un motore per l'analisi e l'elaborazione di grandi set di dati, paragonabile per le sue funzioni a Apache Spark<\/a><\/noindex>. Il codice \u00e8 scritto in Python e \u00e8 aperto<\/a><\/noindex> sotto licenza Apache 2.0. <\/p>\n

BlazingSQL \u00e8 adatto per l'esecuzione di singole query analitiche su grandi set di dati (decine di gigabyte), conservati in formati tabellari (ad esempio, registri, statistiche NetFlow, ecc.). BlazingSQL pu\u00f2 eseguire query su file raw nei formati CSV e Apache Parquet, posizionati in sistemi di file di rete e cloud come HDFS e AWS S3, trasferendo direttamente il risultato nella memoria della GPU. Grazie alla parallelizzazione delle operazioni sulla GPU e all'utilizzo di memorie video pi\u00f9 veloci, le query in BlazingSQL vengono eseguite fino a 20 volte<\/a><\/noindex> pi\u00f9 velocemente rispetto ad Apache Spark.<\/p>\n

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

Per lavorare con GPU si utilizza un set di librerie<\/a><\/noindex> open source RAPIDS<\/a><\/noindex>, che consente di creare applicazioni per l'elaborazione dei dati e l'analisi, eseguite completamente sul lato GPU (\u00e8 fornito un interfaccia Python<\/a><\/noindex> per l'utilizzo dei primitivi a basso livello di CUDA e per la parallelizzazione dei calcoli). <\/p>\n

BlazingSQL offre la possibilit\u00e0 di utilizzare SQL invece delle API per l'elaborazione dei dati cuUDF<\/a><\/noindex> (basato su Apache Arrow<\/a><\/noindex>), utilizzato in RAPIDS. BlazingSQL \u00e8 uno strato aggiuntivo che opera sopra cuDF e utilizza la libreria cuIO per leggere i dati dal disco. Le query SQL vengono tradotte in chiamate di funzioni cuUDF, che consentono di caricare i dati nella GPU e di eseguire operazioni di join, aggregazione e filtraggio su di essi. Viene supportata la creazione di configurazioni distribuite che coprono migliaia di GPU. <\/p>\n

BlazingSQL semplifica notevolmente il lavoro con i dati \u2014 invece di centinaia di chiamate a funzioni cuDF, \u00e8 possibile utilizzare una sola query SQL. L'uso di SQL consente di garantire l'integrazione di RAPIDS con i sistemi di analisi esistenti, senza la necessit\u00e0 di scrivere gestori specifici e senza caricare i dati in un ulteriore DBMS.
\ncompleta compatibilit\u00e0 con tutte le parti di RAPIDS, traducendo le funzionalit\u00e0 esistenti in SQL e garantendo prestazioni equivalenti a quelle di cuDF. \u00c8 inclusa anche la supporto per l'integrazione con le librerie XGBoost<\/a><\/noindex> e cuML<\/a><\/noindex> per risolvere problemi di analisi e machine learning. <\/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 non \u00e8 un vero e proprio DBMS, ma si posiziona come un motore per l'analisi e l'elaborazione di grandi set di dati, comparabile per compiti a Apache Spark. Il codice \u00e8 scritto in Python ed \u00e8 aperto sotto la licenza Apache 2.0. 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