[gsoc2023] bump entry for pass ordering project to 2023

[mtrofin]

OpenProjects.html

18

@asl , OK to just revive the discourse thread, i.e. keep it the same, just add a reply saying "this is ongoing 2023"?

[asl]

OpenProjects.html

18

Sure, why not. But in the Open Projects page I would prefer a fresh entry, so we'd have also complete picture without contents :) Thanks!

[mtrofin]

OpenProjects.html

18

ack, so can copy and paste 2022 then - unless @tarinduj @jdoerfert - want me to update that blurb from 2022 in any way?

(@asl - I can just clone today and update if Tarindu/Johannes want later?)

[asl]

OpenProjects.html

18

Sure, feel free to refine later. It's better than no entry at all ;)

[mtrofin]

cloned 2022->2023 entry, relabeled ID

[tarinduj]

OpenProjects.html

739–746

"However, these standard optimization pipelines are manually designed by compiler developers to perform “reasonably well” for any generic input. Therefore, these pipelines sometimes do not achieve the best optimization possible for a given program as they are not tailored for the program being optimized.

We want to improve the existing heuristics or replace the heuristics with machine learning-based models so that the LLVM compiler can provide a superior order of the passes customized per program."

This would be a bit clearer, I feel.

756

; come up with new optimization sequences through search or learning.

[mtrofin]

rebase & feedback

[mtrofin]

OpenProjects.html

739–746

ack, so s/application/program in 3 places, correct?

OpenProjects.html

<ul>
         <ul>
           <li>
             <b>LLVM Core</b>
             <ul>
                <li><a href="#llvm_new_jitlink_backends">JITLink new backends</a></li>
                <li><a href="#llvm_improving_compile_times">Improving compile times</a></li>
                <li><a href="#llvm_addressing_rust_optimization_failures">Addressing Rust optimization failures</a></li>
                <li><a href="#remove_ub_tests">Remove undefined behavior from tests</a></li>
                <li><a href="#llvm_mlgo_latency_model">Better performance models for MLGO training</a></li>
+               <li><a href="#llvm_mlgo_passes_2023">Machine Learning Guided Ordering of Compiler Optimization Passes</a></li>               
             </ul>
           </li>
           <li><a href="http://clang.llvm.org/"><b>Clang</b></a>
             <ul>
               <li><a href="#clang-repl-out-of-process">Out-of-process execution for clang-repl</a>
               <li><a href="#clang_analyzer_taint_analysis">Improve and Stabilize the Clang Static Analyzer's "Taint Analysis" Checks</a></li>
               <li><a href="#clang-repl-autocompletion">Implement autocompletion in clang-repl</a>
               </li>
<div class="www_subsubsection">
   <a id="llvm_mlgo_latency_model">Better performance models for MLGO training</a>
 </div>
 <!-- *********************************************************************** -->
 <div class="www_text">
   <p><b>Description of the project</b>
     We are using machine-guided compiler optimizations ("MLGO") for register allocation eviction and inlining for size, in
     real-life deployments. The ML models have been trained with reinforcement learning algorithms. Expanding to more
     performance areas is currently impeded by the poor prediction quality of our performance estimation models. Improving
     those is critical to the effectiveness of reinforcement learning training algorithms, and therefore to enabling applying
     MLGO systematically to more optimizations.
   </p>
   <p><b>Project size:</b> either 175 or 350 hr.</p>
   <p><b>Difficulty:</b> Medium</p>
   <p><b>Skills:</b> C/C++, some compiler experience, some Python. ML experience is a bonus.</p>
   <p><b>Expected outcomes</b>: Better modeling of the execution environment by including additional runtime/profiling
     information, such as additional PMU data, LLC miss probabilities or branch mispredictions. This involves (1) building
     a data collection pipeline that covers additional runtime information, (2) modifying the ML models to allow processing
     this data, and (3) modifying the training and inference process for the models to make use this data.
   <p>Today, the models are almost pure static analysis; they see the instructions, but they make one-size-fits-all
     assumptions about the execution environment and the runtime behavior of the code. The goal of this project is to move
     from static analysis towards more dynamic models that better represent code the way it actually executes.</p>
   <p><b>Mentors</b>
     Ondrej Sykora, Mircea Trofin, Aiden Grossman
   </p>
   <p>
   <b>Discourse</b>
   <a href="https://discourse.llvm.org/t/better-performance-models-for-mlgo-training/68219">URL</a>
   </p>
 </div>
 
 <!-- *********************************************************************** -->
 <div class="www_subsubsection">
   <a name="clang_analyzer_taint_analysis">Improve and Stabilize the Clang Static Analyzer's "Taint Analysis" Checks</a>
 </div>
<div class="www_text">
   <p><b>Confirmed Mentor:</b> <a href="https://github.com/haoNoQ">Artem Dergachev</a>,
   <a href=https://github.com/xazax-hun>Gábor Horváth</a></p>
 
   <p><b>Discourse:</b> <a href="https://discourse.llvm.org/t/clang-improve-and-stabilize-the-static-analyzers-taint-analysis-checks/68235">URL</a>
 </div>
 
 
 <!-- *********************************************************************** -->
+<div class="www_subsubsection">
+  <a id="llvm_mlgo_passes_2023">Machine Learning Guided Ordering of Compiler Optimization Passes</a>
+</div>
+<!-- *********************************************************************** -->
+<div class="www_text">
+  <p><b>Description of the project</b>
+  This continues the work of GSoC 2020 and <a href="https://summerofcode.withgoogle.com/archive/2021/projects/6411038932598784">2021</a>.
+
+  Developers generally use standard optimization pipelines like -O2 and -O3 to
+  optimize their code. Manually crafted heuristics are used to determine which
+  optimization passes to select and how to order the execution of those passes.
+  However, this process is not tailored for a particular program, or kind
+  of program, as it is designed to perform “reasonably well” for any input.
+
+  We want to improve the existing heuristics or replace the heuristics with
+  machine learning-based models so that the LLVM compiler can provide a superior
+  order of the passes customized per program.
+
+  The last milestone enabled feature extraction, and started investigating training
+  a policy for selecting a more appropriate pass pipeline.
+  </p>
+  <p><b>Project size:</b> either 175 or 350 hr.</p>
+  <p><b>Difficulty:</b> Medium</p>
+  <p><b>Skills:</b> C/C++, some compiler experience. ML experience is a bonus.</p>
+  <p><b>Expected outcomes</b>: Pre-trained model selecting the most economical
+    optimization pipeline, with no loss in performance; hook-up of model in LLVM;
+    (re-)training tool; come up with new optimization sequences through search or learning.</p>
+  <p><b>Mentors</b>
+    Tarindu Jayatilaka, Mircea Trofin, Johannes Doerfert
+  </p>
+  <p>
+  <b>Discourse</b>
+  <a href="https://discourse.llvm.org/t/machine-learning-guided-ordering-of-compiler-optimization-passes/60415">URL</a>
+  </p>
+</div>
+
+<!-- *********************************************************************** -->
 <div class="www_sectiontitle">
   <a name="gsoc22">Google Summer of Code 2022</a>
 </div>
 <!-- *********************************************************************** -->
 
 <div class="www_text">
   <p>
     Google Summer of Code 2022 was very successful for LLVM project. For the