BEGIN:VCALENDAR PRODID:-//Microsoft Corporation//Outlook 16.0 MIMEDIR//EN VERSION:2.0 METHOD:PUBLISH X-MS-OLK-FORCEINSPECTOROPEN:TRUE BEGIN:VTIMEZONE TZID:Pacific Standard Time BEGIN:STANDARD DTSTART:16011104T020000 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZOFFSETFROM:-0700 TZOFFSETTO:-0800 END:STANDARD BEGIN:DAYLIGHT DTSTART:16010311T020000 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZOFFSETFROM:-0800 TZOFFSETTO:-0700 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT CLASS:PUBLIC CREATED:20191004T172251Z DESCRIPTION:We’ll present BlazingSQL\, RAPIDS’ open source SQL engine. BlazingSQL eliminates the need to build and deploy a database\, enabling u sers to fully integrate high-performance SQL into their RAPIDS workflows.I t’s built entirely on the GPU Apache Arrow standard that underpins the R APIDS ecosystem and the primitives underneath the cuDF and cuIO libraries. BlazingSQL supports a myriad of data sources. Users can query Apache Parq uet and JSON in a data lake with in-memory data sources like Apache Arrow or Pandas in a single\, intuitive\, SQL query that feeds machine learning\ , deep learning\, or graph workloads. We’ll launch and run a series of e nd-to-end BlazingSQL and RAPIDS workloads distributed on a cluster of 64 G PUs.\n \nIf you haven’t registered for GTC DC yet\, you can do so here < https://nvda.ws/31PHBuo> .\n \n DTEND;TZID="Pacific Standard Time":20191105T135500 DTSTAMP:20191004T172251Z DTSTART;TZID="Pacific Standard Time":20191105T133000 LAST-MODIFIED:20191004T172251Z LOCATION:1300 Pennsylvania Ave NW\, Washington\, D.C. 20004 | Atrium Ballro om B PRIORITY:5 SEQUENCE:0 SUMMARY;LANGUAGE=en-us:BlazingSQL - The RAPIDS SQL Engine TRANSP:OPAQUE UID:040000008200E00074C5B7101A82E00800000000904696859D7AD501000000000000000 010000000E7D5F5F2A9045C47AB8D2B5E7FB1736D X-ALT-DESC;FMTTYPE=text/html:
< p class=MsoNormal>We’\;ll present BlazingSQL\, RAPIDS’\; open source SQL engine. BlazingSQL eliminates the need to build and deploy a database\, enabling users to fully integrate high-performance SQL into the ir RAPIDS workflows.It’\;s built entirely o n the GPU Apache Arrow standard that underpins the RAPIDS ecosystem and th e primitives underneath the cuDF and cuIO libraries. BlazingSQL suppor ts a myriad of data sources. Users can query Apache Parquet and JSON in a data lake with in-memory data sources like Apache Arrow or Pandas in a sin gle\, intuitive\, SQL query that feeds machine learning\, deep learning\, or graph workloads. We’\;ll launch and run a series of end-to-end BlazingSQL and RAPIDS workloads distributed on a cl uster of 64 GPUs.

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If you haven’\;t registered for GTC DC yet\, you can do so here.

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