Matei Zaharia

Results: 131



#Item
11

Weld: A Common Runtime for High Performance Data Analytics Shoumik Palkar, James J. Thomas, Anil Shanbhag† , Deepak Narayanan, Holger Pirk† , Malte Schwarzkopf† , Saman Amarasinghe† , Matei Zaharia Stanford InfoL

Add to Reading List

Source URL: cs.stanford.edu

- Date: 2017-07-27 16:16:19
    12

    Vuvuzela a scalable private messaging system David Lazar Jelle van den Hooff, Matei Zaharia, Nickolai Zeldovich

    Add to Reading List

    Source URL: davidlazar.org

    - Date: 2015-10-26 09:55:38
      13

      Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing Matei Zaharia, Mosharaf Chowdhury, Tathagata Das, Ankur Dave, Justin Ma, Murphy McCauley, Michael J. Franklin, Scott Shenker, I

      Add to Reading List

      Source URL: www-bcf.usc.edu

      - Date: 2012-03-16 18:09:41
        14

        Scaling Spark in the Real World: Performance and Usability Michael Armbrust, Tathagata Das, Aaron Davidson, Ali Ghodsi, Andrew Or, Josh Rosen, Ion Stoica, Patrick Wendell, Reynold Xin, Matei Zaharia† Databricks Inc.

        Add to Reading List

        Source URL: cs.stanford.edu

        - Date: 2016-12-17 20:42:06
          15

          Vuvuzela: Scalable Private Messaging Resistant to Traffic Analysis ∗ Jelle van den Hooff, ∗ David Lazar, Matei Zaharia, and Nickolai Zeldovich MIT CSAIL

          Add to Reading List

          Source URL: davidlazar.org

          - Date: 2016-11-15 20:23:30
            16

            Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing Matei Zaharia, Mosharaf Chowdhury, Tathagata Das, Ankur Dave, Justin Ma, Murphy McCauley, Michael J. Franklin, Scott Shenker, I

            Add to Reading List

            Source URL: www.cs.utah.edu

            - Date: 2016-08-24 16:44:11
              17Computing / Hadoop / Apache Software Foundation / Parallel computing / Apache Spark / Cluster computing / Java platform / Apache Hadoop / Data-intensive computing / MapReduce / Apache HBase / PageRank

              Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing Matei Zaharia, Mosharaf Chowdhury, Tathagata Das, Ankur Dave, Justin Ma, Murphy McCauley, Michael J. Franklin, Scott Shenker, I

              Add to Reading List

              Source URL: nil.csail.mit.edu

              Language: English - Date: 2015-01-05 06:37:34
              18Computing / Hadoop / Apache Software Foundation / Parallel computing / Cluster computing / Java platform / Apache Spark / MapReduce / Data-intensive computing / Apache Hadoop / Apache Hive / Scala

              Spark: Cluster Computing with Working Sets Matei Zaharia, Mosharaf Chowdhury, Michael J. Franklin, Scott Shenker, Ion Stoica University of California, Berkeley Abstract MapReduce/Dryad job, each job must reload the data

              Add to Reading List

              Source URL: people.csail.mit.edu

              Language: English - Date: 2016-08-21 15:09:53
              19Computing / Concurrent computing / Parallel computing / Hadoop / Distributed computing architecture / Cloud infrastructure / Apache Software Foundation / MapReduce / Apache Spark / MapR / Data-intensive computing / Apache Hadoop

              Large-Scale Numerical Computation Using a Data Flow Engine Matei Zaharia Outline

              Add to Reading List

              Source URL: mmds-data.org

              Language: English - Date: 2014-06-24 03:07:59
              20Computing / Mathematics / Apache Software Foundation / Hadoop / Combinatorics / Apache Spark / Cluster computing / Java platform / MapReduce / Apache Hadoop / Partition / RDD

              Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing Matei Zaharia, Mosharaf Chowdhury, Tathagata Das, Ankur Dave, Justin Ma, Murphy McCauley, Michael J. Franklin, Scott Shenker, I

              Add to Reading List

              Source URL: www.cs.princeton.edu

              Language: English - Date: 2013-03-09 18:36:36
              UPDATE