From 19feff0b88c54dc789464109049d1064c80bb2c7 Mon Sep 17 00:00:00 2001
From: StanfordLegion GitHub Actions <StanfordLegion@users.noreply.github.com>
Date: Mon, 16 Dec 2024 19:04:40 +0000
Subject: [PATCH] Deploy Mon Dec 16 19:04:32 UTC 2024.

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diff --git a/pdfs/legion-manual.pdf b/pdfs/legion-manual.pdf
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diff --git a/publications/index.html b/publications/index.html
index 9b2e459a..d657bd8a 100644
--- a/publications/index.html
+++ b/publications/index.html
@@ -142,6 +142,7 @@ <h2 id="table-of-contents">Table of Contents</h2>
       <li><a href="#dcr2021">Dynamic Control Replication (PPoPP 2021)</a> [<a href="/pdfs/dcr2021.pdf">PDF</a>]</li>
       <li><a href="#idx2021">Index Launches (SC 2021)</a> [<a href="/pdfs/idx2021.pdf">PDF</a>]</li>
       <li><a href="#visibility2023">Visibility Algorithms (PPoPP 2023)</a> [<a href="/pdfs/visibility2023.pdf">PDF</a>]</li>
+      <li><a href="#autotrace2025">Automatic Tracing (ASPLOS 2025)</a> [<a href="/pdfs/autotrace2025.pdf">PDF</a>]</li>
     </ul>
   </li>
   <li>Programming Model:
@@ -178,6 +179,7 @@ <h2 id="table-of-contents">Table of Contents</h2>
       <li><a href="#spdistal2022">Sparse Tensor Algebra (SC 2022)</a> [<a href="/pdfs/spdistal2022.pdf">PDF</a>]</li>
       <li><a href="#legate-sparse2023">Legate Sparse (SC 2023)</a> [<a href="/pdfs/legate-sparse2023.pdf">PDF</a>]</li>
       <li><a href="#automap2023">AutoMap (SC 2023)</a> [<a href="/pdfs/automap2023.pdf">PDF</a>]</li>
+      <li><a href="#fusion2025">Task and Kernel Fusion (ASPLOS 2025)</a> [<a href="/pdfs/fusion2025.pdf">PDF</a>]</li>
     </ul>
   </li>
   <li>Applications:
@@ -829,6 +831,45 @@ <h2 id="papers">Papers</h2>
 worked and what didn’t, and why only the Pygion code eventually
 succeeded in running at scale.</p>
 
+<p><a name="autotrace2025"></a><strong>Automatic Tracing in Task-Based Runtime Systems</strong> <a href="/pdfs/autotrace2025.pdf">PDF</a> <br />
+<em>Rohan Yadav, Michael Bauer, David Broman, Michael Garland, Alex Aiken, and Fredrik Kjolstad</em> <br />
+In the Conference on Architectural Support for Programming Languages and Operating Systems (<a href="https://www.asplos-conference.org/asplos2025/">ASPLOS 2025</a>) <br />
+<strong>Abstract:</strong> Implicitly parallel task-based runtime systems often perform 
+dynamic analysis to discover dependencies in and extract parallelism from 
+sequential programs. Dependence analysis becomes expensive as task granularity 
+drops below a threshold. Tracing techniques have been developed where programmers 
+annotate repeated program fragments (traces) issued by the application, and the 
+runtime system memoizes the dependence analysis for those fragments, greatly 
+reducing overhead when the fragments are executed again. However, manual trace
+annotation can be brittle and not easily applicable to complex programs built 
+through the composition of independent components. We introduce Apophenia, a 
+system that automatically traces the dependence analysis of task-based runtime
+systems, removing the burden of manual annotations from programmers and enabling
+new and complex programs to be traced. Apophenia identifies traces dynamically
+through a series of dynamic string analyses, which find repeated program 
+fragments in the stream of tasks issued to the runtime system. We show that 
+Apophenia is able to come between 0.92x–1.03x the performance of manually 
+traced programs, and is able to effectively trace previously untraced programs
+to yield speedups of between 0.91x–2.82x on the Perlmutter and Eos supercomputers.</p>
+
+<p><a name="fusion2025"></a><strong>Composing Distributed Computations Through Task and Kernel Fusion</strong> <a href="/pdfs/fusion2025.pdf">PDF</a> <br />
+<em>Rohan Yadav, Shiv Sundram, Wonchan Lee, Michael Garland, Michael Bauer, Alex Aiken, and Fredrik Kjolstad</em> <br />
+In the Conference on Architectural Support for Programming Languages and Operating Systems (<a href="https://www.asplos-conference.org/asplos2025/">ASPLOS 2025</a>) <br />
+<strong>Abstract:</strong> We introduce Diffuse, a system that dynamically performs task 
+and kernel fusion in distributed, task-based runtime systems. The key component
+of Diffuse is an intermediate representation of distributed computation that
+enables the necessary analyses for the fusion of distributed tasks to be performed
+in a scalable manner. We pair task fusion with a JIT compiler to fuse together the
+kernels within fused tasks. We show empirically that Diffuse’s intermediate 
+representation is general enough to be a target for two real-world, task-based
+libraries (cuPyNumeric and Legate Sparse), letting Diffuse find optimization
+opportunities across function and library boundaries. Diffuse accelerates
+unmodified applications developed by composing task-based libraries by 1.86x
+on average (geo-mean), and by between 0.93x–10.7x on up to 128 GPUs. Diffuse
+also finds optimization opportunities missed by the original application
+developers, enabling high-level Python programs to match or exceed the 
+performance of an explicitly parallel MPI library.</p>
+
 <h2 id="theses">Theses</h2>
 
 <p><a name="bauer_thesis"></a><em>Note: The following thesis is a thorough guide to the Legion programming model and covers many implementation details that are not documented elsewhere.</em><br />