<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Spark on a.dev</title><link>https://aditya08.github.io/tags/spark/</link><description>Recent content in Spark on a.dev</description><generator>Hugo -- 0.148.0</generator><language>en</language><lastBuildDate>Mon, 13 Aug 2018 00:00:00 +0000</lastBuildDate><atom:link href="https://aditya08.github.io/tags/spark/index.xml" rel="self" type="application/rss+xml"/><item><title>Reducing Communication in Proximal Newton Methods for Sparse Least Squares Problems</title><link>https://aditya08.github.io/publications/proxnewton/</link><pubDate>Mon, 13 Aug 2018 00:00:00 +0000</pubDate><guid>https://aditya08.github.io/publications/proxnewton/</guid><description>This paper presents communication-reducing proximal Newton methods for sparse least-squares problems</description></item><item><title>Avoiding Communication in Proximal Methods for Convex Optimization Problems</title><link>https://aditya08.github.io/publications/proxmethods/</link><pubDate>Tue, 24 Oct 2017 00:00:00 +0000</pubDate><guid>https://aditya08.github.io/publications/proxmethods/</guid><description>This technical report studies communication-avoiding proximal methods for convex optimization problems</description></item><item><title>Matrix Factorizations at Scale: A Comparison of Scientific Data Analytics in Spark and C+MPI Using Three Case Studies</title><link>https://aditya08.github.io/publications/matrixfact/</link><pubDate>Mon, 05 Dec 2016 00:00:00 +0000</pubDate><guid>https://aditya08.github.io/publications/matrixfact/</guid><description>This paper compares Spark and C+MPI implementations of matrix factorizations for scientific data analytics</description></item></channel></rss>