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<p class="MsoNormal"><span style="font-size:11.0pt">Here is the Zoom link for the presentation:
<a href="https://uchicago.zoom.us/j/94379397186?pwd=aWxyVHgvVnBtK2ZkTzY1WTZPa2NxQT09">
https://uchicago.zoom.us/j/94379397186?pwd=aWxyVHgvVnBtK2ZkTzY1WTZPa2NxQT09</a><o:p></o:p></span></p>
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<p class="MsoNormal"><span style="font-size:11.0pt">Thank you everyone who asked!<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt">Jinjin<o:p></o:p></span></p>
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<p class="MsoNormal" style="margin-bottom:12.0pt"><b><span style="font-size:12.0pt;color:black">From:
</span></b><span style="font-size:12.0pt;color:black">cs <cs-bounces+j2zhao=cs.uchicago.edu@mailman.cs.uchicago.edu> on behalf of Megan Woodward <meganwoodward@uchicago.edu><br>
<b>Date: </b>Tuesday, May 24, 2022 at 9:03 AM<br>
<b>To: </b>cs@cs.uchicago.edu <cs@cs.uchicago.edu>, colloquium@cs.uchicago.edu <colloquium@cs.uchicago.edu><br>
<b>Subject: </b>[CS] Jin Jin Zhao MS Presentation/May 25, 2022<o:p></o:p></span></p>
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<p class="MsoNormal"><span style="font-size:11.0pt">This is an announcement of Jin Jin Zhao's MS Presentation<br>
===============================================<br>
Candidate: Jin Jin Zhao<br>
<br>
Date: Wednesday, May 25, 2022<br>
<br>
Time: 3 pm CST<br>
<br>
Location: JCL 298<br>
<br>
M.S. Paper Title: AUTOMATED PROVENANCE CAPTURE IN ARRAY-PROGRAMMING FRAMEWORKS<br>
<br>
Abstract: This paper presents DSLog, a system that efficiently capture and represent fine-grained data<br>
provenance in array-programming frameworks for black box functions. It uses a technique<br>
called annotated execution to capture “physical” provenance, automatically without user<br>
specification. We describe a low-level implementation for arrays up to<br>
100 million (and more) cells. This implementation also improves capture performance up to 34x over a high level<br>
baseline. Additionally, we contribute a new compression algorithm, named ProvRC, that compresses such<br>
relations. We show that the ProvRC results in a significant storage reduction over functions<br>
with simple spatial regularity, beating alternative baselines by many orders of magnitude.<br>
Finally, we present the concepts of dimensional and generalized views over these compressed<br>
relational representation, which allows DSLog to recognize previously seen function (with<br>
only input array dimension information, and no input array information respectively), and<br>
re-use pre-existing materialized provenance views. We demonstrate that these views cover<br>
92% and 73% respectively of 136 tested numpy functions, and preliminary results show that<br>
using the views have a marked improvement over pure naive annotated execution.<br>
<br>
Advisors: Sanjay Krishnan<br>
<br>
Committee Members: Sanjay Krishnan, Raul Castro Fernandez, Blase Ur, and Nick Feamster<o:p></o:p></span></p>
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