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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>HiFrames: High Performance Data Frames in a Scripting Language · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1704.02341/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/1704.02341/feed.xml" rel="self" type="application/rss+xml" /><item><title>HiFrames: High Performance Data Frames in a Scripting Language — mentioned by tedmiston</title><link>https://news.ycombinator.com/item?id=15467587</link><guid isPermaLink="false">urn:hackerpapers:mention:1704.02341:15467587</guid><pubDate>Fri, 13 Oct 2017 17:24:46 GMT</pubDate><description>&lt;p&gt;The abstract from the paper about it HiFrames: High Performance Data Frames in a Scripting Language [1] describes HPAT better than the description in the repo: &amp;gt; Data frames in scripting languages are essential abstractions for processing structured data. However, existing data frame solutions are either not distributed (e.g., Pandas in Python) and therefore have limited scalability, or they are not tightly integrated with array computations (e.g., Spark SQL). This paper proposes a novel compiler-based approach where we integrate data frames into the High Performance Analytics Toolkit (HPAT)…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1704.02341/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.DC</category></item><item><title>HiFrames: High Performance Data Frames in a Scripting Language — mentioned by chrispeel</title><link>https://news.ycombinator.com/item?id=15379581</link><guid isPermaLink="false">urn:hackerpapers:mention:1704.02341:15379581</guid><pubDate>Sun, 01 Oct 2017 20:19:35 GMT</pubDate><description>&lt;p&gt;...and HiFrames [1] and HPAT [2] will kill Spark ;-) HiFrames and HPAT are built on Julia [3] and use the Julia packages ParallelAccelerator.jl [4]. [1] https://arxiv.org/abs/1704.02341 [2] https://github.com/IntelLabs/HPAT.jl [3] https://julialang.org/ [4] https://github.com/IntelLabs/ParallelAccelerator.jl&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1704.02341/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.DC</category></item></channel></rss>
