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Posted to commits@cassandra.apache.org by "Stefan Miklosovic (Jira)" <ji...@apache.org> on 2026/01/13 16:21:00 UTC
[jira] [Commented] (CASSANALYTICS-114) Investigate GPU accelerated processing, e.g. via Nvidia RAPIDS for Cassandra Analytics
[ https://issues.apache.org/jira/browse/CASSANALYTICS-114?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=18051567#comment-18051567 ]
Stefan Miklosovic commented on CASSANALYTICS-114:
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draft for inspiration https://github.com/apache/cassandra-analytics/pull/164
I might completely miss something though, I plan to return to this later.
> Investigate GPU accelerated processing, e.g. via Nvidia RAPIDS for Cassandra Analytics
> --------------------------------------------------------------------------------------
>
> Key: CASSANALYTICS-114
> URL: https://issues.apache.org/jira/browse/CASSANALYTICS-114
> Project: Apache Cassandra Analytics
> Issue Type: New Feature
> Components: Writer
> Reporter: Stefan Miklosovic
> Assignee: Stefan Miklosovic
> Priority: Normal
> Time Spent: 10m
> Remaining Estimate: 0h
>
> Users who happen to have GPUs to spare can leverage its computing power via Nvidia's project called RAPIDS which is a plugin to Apache Spark which maps data to VRAM and its processing is accelerated via CUDA.
> It would be nice to explore this in context of Cassandra Analytics so we can speed up the processing significantly.
> My preliminary investigation led to the realisation that we would likely need to process rows in a columnar fashion - the current processing would stay, it just about the way we present the data to Spark API / internals.
> (1) https://www.nvidia.com/en-us/ai-data-science/spark-ebook/getting-started-spark-3/#p7-s1?ncid=no-ncid
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