[www] r344819 - Update the speaker list for the Glow talk.

[Nadav Rotem via llvm-commits]

Author: nadav
Date: Fri Oct 19 16:41:06 2018
New Revision: 344819

URL: http://llvm.org/viewvc/llvm-project?rev=344819&view=rev
Log:
Update the speaker list for the Glow talk. 


Modified:
    www/trunk/devmtg/2018-10/index.html
    www/trunk/devmtg/2018-10/talk-abstracts.html

Modified: www/trunk/devmtg/2018-10/index.html
URL: http://llvm.org/viewvc/llvm-project/www/trunk/devmtg/2018-10/index.html?rev=344819&r1=344818&r2=344819&view=diff
==============================================================================
--- www/trunk/devmtg/2018-10/index.html (original)
+++ www/trunk/devmtg/2018-10/index.html Fri Oct 19 16:41:06 2018
@@ -130,7 +130,7 @@ Over the past year we have hosted panels
 
 <b>Keynotes</b>
 <ul>
-    <li><a href="talk-abstracts.html#keynote1">Glow: LLVM-based machine learning compiler</a> - Nadav Rotem, Jakob Olesen</li>
+    <li><a href="talk-abstracts.html#keynote1">Glow: LLVM-based machine learning compiler</a> - Nadav Rotem, Roman Levenstein</li>
     <li><a href="talk-abstracts.html#keynote2">The Future Direction of C++ and the Four Horsemen of Heterogeneous Computing</a> - Michael Wong</li>
 </ul>
 

Modified: www/trunk/devmtg/2018-10/talk-abstracts.html
URL: http://llvm.org/viewvc/llvm-project/www/trunk/devmtg/2018-10/talk-abstracts.html?rev=344819&r1=344818&r2=344819&view=diff
==============================================================================
--- www/trunk/devmtg/2018-10/talk-abstracts.html (original)
+++ www/trunk/devmtg/2018-10/talk-abstracts.html Fri Oct 19 16:41:06 2018
@@ -10,7 +10,7 @@
     <b>Keynote Talks</b>
 <ul>
     <li><a id="keynote1">Glow: LLVM-based machine learning compiler</a>
-	<br><i>Nadav Rotem, Jakob Olesen</i>
+	<br><i>Nadav Rotem, Roman Levenstein</i>
 	<p>
 Glow is an LLVM-based machine learning compiler for heterogeneous hardware that's developed as part of the PyTorch project. It is a pragmatic approach to compilation that enables the generation of highly optimized code for CPUs, GPUs and accelerators. Glow lowers the traditional neural network data-flow graph into a two-phase strongly-typed intermediate representation (inspired by SIL). Finally Glow emits LLVM-IR and uses the LLVM code generator to generate highly-optimized code. In this talk we'll describe the structure of machine learning programs and how Glow is designed to compile these graphs into multiple targets. We'll explain how we use the LLVM infrastructure and go over some of the techniques that we use to generate high-performance code using LLVM.
 	</p>