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<!--#include virtual="header.incl" -->
<div class="www_sectiontitle">Open LLVM Projects</div>
<ul>
<li>Google Summer of Code Ideas & Projects
<ul>
<li>
<a href="#gsoc23">Google Summer of Code 2023</a>
<ul>
<li>
<b>LLVM Core</b>
<ul>
<li><a href="#llvm_new_jitlink_reopt">Re-optimization using JITLink</a></li>
<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><a href="#clang-modules-build-daemon">Modules build daemon: build system agnostic support for explicitly built modules</a></li>
<li><a href="#clang-extract-api-categories">ExtractAPI Objective-C categories</a></li>
<li><a href="#clang-extract-api-cpp-support">ExtractAPI C++ Support</a></li>
<li><a href="#clang-extract-api-while-building">ExtractAPI while building</a></li>
</li>
</ul>
</li>
<li>
<b>LLD</b>
<ul>
<li><a href="#llvm_lld_embedded">LLD Linker Improvements for Embedded Targets</a></li>
</ul>
</li>
<li>
<b>MLIR</b>
<ul>
<li><a href="#llvm_mlir_presburger_opt">Optimizing MLIR’s Presburger library</a></li>
</ul>
</li>
<li>
<b>Code Coverage</b>
<ul>
<li><a href="#llvm_code_coverage">Support a hierarchical directory structure in generated coverage html reports</a></li>
</ul>
</li>
</ul>
</li>
<li>
<a href="#gsoc22">Google Summer of Code 2022</a>
<ul>
<li>
<b>LLVM Core</b>
<ul>
<li><a href="#llvm_shared_jitlink">Implement a shared-memory based JITLinkMemoryManager for out-of-process JITting</a></li>
<li><a href="#llvm_build_jit_tutorial">Modernize the LLVM "Building A JIT" tutorial series</a></li>
<li><a href="#llvm_jit_new_format">Write JITLink support for a new format/architecture</a></li>
<li><a href="#llvm_instrumentaion_for_compile_time">Instrumentation of Clang/LLVM for Compile Time</a></li>
<li><a href="#llvm_lto_dependency_info">Richer symbol dependency information for LTO</a></li>
<li><a href="#llvm_mlgo_passes">Machine Learning Guided Ordering of Compiler Optimization Passes</a></li>
<li><a href="#llvm_mlgo_loop">Learning Loop Transformation Heuristics</a></li>
<li><a href="#llvm_module_inliner">Evaluate and Expand the Module-Level Inliner</a></li>
<li><a href="#llvm_undef_load">Remove undef: move uninitialized memory to poison</a></li>
<li><a href="#llvm_abi_export">Add ABI/API export annotations to the LLVM build</a></li>
</ul>
</li>
<li><a href="http://clang.llvm.org/"><b>Clang</b></a>
<ul>
<li><a href="#clang-template-instantiation-sugar">Extend clang AST to
provide information for the type as written in template
instantiations</a>
</li>
<li><a href="#clang-sa-structured-bindings">Implement support for
C++17 structured bindings in the Clang Static Analyzer</a>
</li>
<li><a href="#clang-improve-diagnostics">Improve Clang Diagnostics</a>
</li>
</ul>
</li>
<li>
<a href="https://polly.llvm.org"><b>Polly</b></a>
<ul>
<li><a href="#polly_npm">Completely switch to new pass manager</a></li>
</ul>
</li>
<li>
<b><a href="https://enzyme.mit.edu">Enzyme</a></b>
<ul>
<li><a href="#enzyme_tblgen">Move Enzyme Instruction Transformation Rules to Tablegen</a></li>
<li><a href="#enzyme_vector">Vector Reverse-Mode Automatic Differentiation</a></li>
<li><a href="#enzyme_pm">Enable The New Pass Manager</a></li>
</ul>
</li>
</ul>
</li>
<li>
<a href="#gsoc21">Google Summer of Code 2021</a>
<ul>
<li>
<b>LLVM Core</b>
<ul>
<li><a href="#llvm_distributing_lit">Distributed lit testing</a></li>
<li><a href="#llvm_loop_heuristics">Learning Loop Transformation Heuristics</a></li>
<li><a href="#llvm_ir_fuzzing">Fuzzing LLVM-IR Passes</a></li>
<li><a href="#llvm_ir_assume"><tt>llvm.assume</tt> the missing pieces</a></li>
<li><a href="#llvm_shared_jitlink">Implement a shared-memory based JITLinkMemoryManager for out-of-process JITting</a></li>
<li><a href="#llvm_build_jit_tutorial">Modernize the LLVM "Building A JIT" tutorial series</a></li>
<li><a href="#llvm_jit_new_format">Write JITLink support for a new format/architecture</a></li>
<li><a href="#llvm_ir_issues">Fix fundamental issues in LLVM's IR</a></li>
<li><a href="#llvm_utilize_loopnest">Utilize LoopNest Pass</a></li>
</ul>
</li>
<li><a href="http://clang.llvm.org/"><b>Clang</b></a>
<ul>
<li><a href="#clang-template-instantiation-sugar">Extend clang AST to
provide information for the type as written in template
instantiations</a>
</li>
</ul>
</li>
<li>
<b>OpenMP</b>
<ul>
<li><a href="#openmp_gpu_jit">JIT-ing OpenMP GPU kernels transparently</a></li>
</ul>
</li>
<li>
<b>OpenACC</b>
<ul>
<li><a href="#openacc_rt_diagnostics">OpenACC Diagnostics from the OpenMP Runtime</a></li>
</ul>
</li>
<li>
<b><a href="https://polly.llvm.org">Polly</a></b>
<ul>
<li><a href="#polly_isl_bindings">Use official isl C++ bindings</a></li>
</ul>
</li>
<li>
<b><a href="https://enzyme.mit.edu">Enzyme</a></b>
<ul>
<li><a href="#enzyme_blas">Integrate custom derivatives of BLAS, Eigen, and similar routines into Enzyme</a></li>
<li><a href="#enzyme_swift">Integrate Enzyme into Swift to provide high-performance differentiation in Swift</a></li>
<li><a href="#enzyme_fixed">Differentiation of Fixed-Point Arithmetic</a></li>
<li><a href="#enzyme_rust">Integrate Enzyme into Rust to provide high-performance differentiation in Rust</a></li>
</ul>
</li>
<li>
<b>Clang Static Analyzer</b>
<ul>
<li><a href="#static_analyzer_profling">Clang Static Analyzer performance profiling</a></li>
<li><a href="#static_analyzer_constraint_solver">Clang Static Analyzer constraint solver improvements</a></li>
</ul>
</li>
<li>
<b>LLDB</b>
<ul>
<li><a href="#lldb_diagnostics">A structured approach to diagnostics in LLDB</a></li>
</ul>
</li>
</ul>
</li>
<li>
<a href="#gsoc20">Google Summer of Code 2020</a>
<ul>
<li>
<b>LLVM Core</b>
<ul>
<li><a href="#llvm_optimized_debugging">Improve debugging of optimized code</a></li>
<li><a href="#llvm_ipo">Improve inter-procedural analyses and optimizations</a></li>
<li><a href="#llvm_par">Improve parallelism-aware analyses and optimizations</a></li>
<li><a href="#llvm_dbg_invariant">Make LLVM passes debug info invariant</a></li>
<li><a href="#llvm_mergesim">Improve MergeFunctions to incorporate MergeSimilarFunction patches and ThinLTO Support</a></li>
<li><a href="#llvm_dwarf_yaml2obj">Add DWARF support to yaml2obj</a></li>
<li><a href="#llvm_hotcold">Improve hot cold splitting to aggressively outline small blocks</a></li>
<li><a href="#llvm_pass_order">Advanced Heuristics for Ordering Compiler Optimization Passes</a></li>
<li><a href="#llvm_ml_scc">Machine learning and compiler optimizations: using inter-procedural analysis to select optimizations</a></li>
<li><a href="#llvm_postdominators">Add PostDominatorTree in LoopStandardAnalysisResults</a></li>
<li><a href="#llvm_loopnest">Create loop nest pass</a></li>
<li><a href="#llvm_instdump">Instruction properties dumper and checker</a></li>
<li><a href="#llvm_movecode">Unify ways to move code or check if code is safe to be moved</a></li>
</ul>
<li><a href="http://clang.llvm.org/"><b>Clang</b></a>
<ul>
<li><a href="#clang-template-instantiation-sugar">Extend clang AST to
provide information for the type as written in template
instantiations</a>
</li>
<li><a href="#clang-sa-cplusplus-checkers">Find null smart pointer dereferences
with the Static Analyzer</a>
</li>
</ul>
</li>
<li><a href="http://lldb.llvm.org/"><b>LLDB</b></a></li>
<ul>
<li><a href="#lldb-autosuggestions">Support autosuggestions in LLDB's command line</a></li>
<li><a href="#lldb-more-completions">Implement the missing tab completions for LLDB's command line</a></li>
<li><a href="#lldb-reimplement-lldb-cmdline">Reimplement LLDB's command-line commands using the public SB API.</a></li>
<li><a href="#lldb-batch-testing">Add support for batch-testing to the LLDB testsuite.</a></li>
</ul>
<li>
<b>MLIR</b>
<ul>
<li>See the <a href="https://mlir.llvm.org/getting_started/openprojects/">MLIR open project list</a></li>
</ul>
</li>
</ul>
</li>
<li>
<a href="#gsoc19">Google Summer of Code 2019</a>
<ul>
<li>
<b>LLVM Core</b>
<ul>
<li><a href="#debuginfo_codegen_mismatch">Debug Info should have no
effect on codegen</a></li>
<li><a href="#llvm_function_attributes">Improve (function) attribute
inference</a></li>
<li><a href="#improve_binary_utilities">Improve LLVM binary utilities
</a></li>
</ul>
</li>
<li><a href="http://clang.llvm.org/"><b>Clang</b></a>
<ul>
<li><a href="#clang-astimporter-fuzzer">Implement an ASTImporter
fuzzer</a>
</li>
<li><a href="#improve-autocompletion">Improve shell autocompletion
for Clang</a>
</li>
<li><a href="#analyze-llvm">Apply the Clang Static Analyzer to LLVM-based
Projects</a>
</li>
<li><a href="#header-generation">Generate annotated sources based on
LLVM-IR analyses</a>
</li>
</ul>
</li>
</ul>
</li>
<li><a href="#gsoc18">Google Summer of Code 2018</a></li>
<li><a href="#gsoc17">Google Summer of Code 2017</a></li>
</ul>
</li>
<li><a href="#what">What is this?</a></li>
<li><a href="#subprojects">LLVM Subprojects: Clang and more</a></li>
<li><a href="#improving">Improving the current system</a>
<ol>
<li><a href="#target-desc">Factor out target descriptions</a></li>
<li><a href="#code-cleanups">Implementing Code Cleanup bugs</a></li>
<li><a href="#programs">Compile programs with the LLVM Compiler</a></li>
<li><a href="#llvmtest">Add programs to the llvm-test suite</a></li>
<li><a href="#benchmark">Benchmark the LLVM compiler</a></li>
<li><a href="#statistics">Benchmark Statistics and Warning System</a></li>
<li><a href="#coverage">Improving Coverage Reports</a></li>
<li><a href="#misc_imp">Miscellaneous Improvements</a></li>
</ol></li>
<li><a href="#new">Adding new capabilities to LLVM</a>
<ol>
<li><a href="#llvm_ir">Extend the LLVM intermediate representation</a></li>
<li><a href="#pointeranalysis">Pointer and Alias Analysis</a></li>
<li><a href="#profileguided">Profile-Guided Optimization</a></li>
<li><a href="#compaction">Code Compaction</a></li>
<li><a href="#xforms">New Transformations and Analyses</a></li>
<li><a href="#codegen">Code Generator Improvements</a></li>
<li><a href="#misc_new">Miscellaneous Additions</a></li>
</ol></li>
<li><a href="#using">Project using LLVM</a>
<ol>
<li><a href="#machinemodulepass">Add a MachineModulePass</a></li>
<li><a href="#encodeanalysis">Encode Analysis Results in MachineInstr IR</a></li>
<li><a href="#codelayoutjit">Code Layout in the LLVM JIT</a></li>
<li><a href="#fieldlayout">Improved Structure Splitting and Field Reordering</a></li>
<li><a href="#slimmer">Finish the Slimmer Project</a></li>
</ol></li>
</ul>
<div class="doc_author">
<p>Written by the <a href="/">LLVM Team</a></p>
</div>
<!-- *********************************************************************** -->
<div class="www_sectiontitle">
<a name="gsoc23">Google Summer of Code 2023</a>
</div>
<!-- *********************************************************************** -->
<div class="www_text">
<p>
Welcome prospective Google Summer of Code 2023 Students! This document is
your starting point to finding interesting and important projects for LLVM,
Clang, and other related sub-projects. This list of projects is not only
developed for Google Summer of Code, but open projects that really need
developers to work on and are very beneficial for the LLVM community.
</p>
<p>We encourage you to look through this list and see which projects excite
you and match well with your skill set. We also invite proposals not on this
list. More information and discussion about GSoC can be found in
<a href="https://discourse.llvm.org/c/community/gsoc" target="_blank">
discourse
</a>. If you have questions about a particular project please find the
relevant entry in discourse, check previous discussion and ask. If there is
no such entry or you would like to propose an idea please create a new
entry. Feedback from the community is a requirement for your proposal to be
considered and hopefully accepted.
</p>
<p>The LLVM project has participated in Google Summer of Code for several years
and has had some very successful projects. We hope that this year is no
different and look forward to hearing your proposals. For information on how
to submit a proposal, please visit the Google Summer of Code main
<a href="https://summerofcode.withgoogle.com/">website.</a>
</p>
</div>
<!-- *********************************************************************** -->
<div class="www_subsection">
<a>LLVM</a>
</div>
<!-- *********************************************************************** -->
<!-- *********************************************************************** -->
<div class="www_subsubsection">
<a name="llvm_new_jitlink_reopt">Re-optimization using JITLink</a>
</div>
<!-- *********************************************************************** -->
<div class="www_text">
<p><b>Description of the project:</b>
In Just-In-Time compilers we often choose a low optimization level to
minimize compile time and improve launch times and latencies, however some
functions (which we call hot functions) are used very frequently and for
these functions it is worth optimizing more heavily. In general hot
functions can only be identified at runtime (different inputs will cause
different functions to become hot), so the aim of the reoptimization project
is to build infrastructure to (1) detect hot functions at runtime and (2)
compile them a second time at a higher optimization level, hence the name
"re-optimization".
<br /><br />
There are many possible approaches to both parts of this problem. E.g. hot
functions could be identified by sampling, or using existing profiling
infrastructure, or by implementing custom instrumentation. Reoptimization
could be applied to whole functions, or outlining could be used to enable
optimization of portions of functions. Re-entry into the JIT infrastructure
from JIT’d code might be implemented on top of existing lazy compilation, or
via a custom path.
<br /><br />
Whatever design is adopted, the goal is that the infrastructure should be
generic so that it can be used by other LLVM API clients, and should support
out-of-process JIT-compilation (so some of the solution will be implemented
in the ORC runtime).
<p><b>Expected result:</b>
<ul>
<li>Improve ergonomics of indirection – ideally all forms of indirection
(for re-optimization, lazy compilation, and procedure-linkage-tables)
should be able to share a single stub (and/or binary rewriting metadata)
at runtime.</li>
<li>Implement basic re-optimization on top of the tidied up
indirection.</li>
<li>(Stretch goal) Garbage-collect unoptimized code that is no longer
needed once the optimized version is available.</li>
</ul>
<p><b>Desirable skills:</b>
Intermediate C++; Understanding of LLVM and the LLVM JIT in particular.
<p><b>Project size:</b> Large.</p>
<p><b>Difficulty:</b> Medium</p>
<p><b>Confirmed Mentor:</b>
<a href=https://github.com/vgvassilev>Vassil Vassilev</a>,
<a href=https://github.com/lhames>Lang Hames</a></p>
</p>
<p><b>Discourse:</b> <a href="https://discourse.llvm.org/t/re-optimization-using-jitlink/68260">URL</a>
</div>
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<div class="www_subsubsection">
<a name="llvm_new_jitlink_backends">JITLink new backends</a>
</div>
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<div class="www_text">
<p><b>Description of the project:</b>
JITLink is LLVM's new JIT linker API -- the low-level API that transforms
compiler output (relocatable object files) into ready-to-execute bytes in
memory. To do this JITLink’s generic linker algorithm needs to be
specialized to support the target object format (COFF, ELF, MachO), and
architecture (arm, arm64, i386, x86-64). LLVM already has mature
implementations of JITLink for MachO/arm64 and MachO/x86-64, and a
relatively new implementation for ELF/x86-64. Write a JITLink implementation
for a missing target such as PowerPC or BPF. If you choose to implement
support for a new architecture using the ELF or MachO formats then you will
be able to re-use the existing generic code for these formats. If you want
to implement support for a new target using the COFF format then you will
need to write both the generic COFF support code and the architecture
support code for your chosen architecture.
<br />
<p><b>Expected result:</b>
Write a JITLink specialization for a not-yet-supported format/architecture
such as BPF or PowerPC.
<p><b>Desirable skills:</b>
Intermediate C++; Understanding of LLVM and the LLVM JIT in particular;
familiarity with your chosen format/architecture, and basic linker concepts
(e.g. sections, symbols, and relocations).
<p><b>Project size:</b> Large.</p>
<p><b>Difficulty:</b>Medium</p>
<p><b>Confirmed Mentor:</b>
<a href=https://github.com/vgvassilev>Vassil Vassilev</a>,
<a href=https://github.com/lhames>Lang Hames</a></p>
</p>
<p><b>Discourse:</b> <a href="https://discourse.llvm.org/t/jitlink-new-backends/68223">URL</a>
</div>
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<div class="www_subsubsection">
<a name="llvm_improving_compile_times">Improving compile times</a>
</div>
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<div class="www_text">
<p><b>Description of the project:</b>
While the primary job of a compiler is to produce fast code (good run-time
performance), it is also important that optimization doesn’t take too much
time (good compile-time performance). The goal of this project is to improve
compile-time without hurting optimization quality.
<br />
The general approach to this project is:
<ol>
<li>Pick a workload to optimize. For example, this could be a file from
<a href="https://github.com/llvm/llvm-test-suite/tree/main/CTMark">CTMark</a>
compiled in a certain build configuration (e.g. <code>-O0 -g</code> or
<code>-O3 -flto=thin</code>).</li>
<li>Collect profiling information. This could involve compiler options like
<code>-ftime-report</code> or <code>-ftime-trace</code> for a high-level
overview, as well as <code>perf record</code> or
<code>valgrind --tool=callgrind</code> for a detailed profile.</li>
<li>Identify places that are unexpectedly slow. This is heavily workload
dependent.</li>
<li>Try to optimize an identified hotspot, ideally without impacting generated
code. The <a href="https://llvm-compile-time-tracker.com/">compile-time tracker</a>
can be used to quickly evaluate impact on CTMark.</li>
</ol>
As a disclaimer, it should be noted that outside of pathological cases,
compilation doesn’t tend to have a convenient hotspot where 90% of the time
is spent, instead it is spread out across many passes. As such, individual
improvements also tend to have only small impact on overall compile-time.
Expect to do 10 improvements of 0.2% each, rather than one improvement of 2%.
</p>
<p><b>Expected result:</b>
Substantial improvements on some individual files (multiple percent), and a
small improvement on overall geomean compile-time.</p>
<p><b>Desirable skills:</b>
Intermediate C++. Familiarity with profiling tools (especially if you are
not on Linux, in which case I won’t be able to help).</p>
<p><b>Project size:</b> Either medium or large.</p>
<p><b>Difficulty:</b> Medium</p>
<p><b>Confirmed Mentor:</b> <a href="https://github.com/nikic">Nikita Popov</a></p>
<p><b>Discourse:</b> <a href="https://discourse.llvm.org/t/llvm-improving-compile-times/68094">URL</a>
</div>
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<div class="www_subsubsection">
<a name="llvm_addressing_rust_optimization_failures">Addressing Rust optimization failures</a>
</div>
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<div class="www_text">
<p><b>Description of the project:</b>
The <a href="https://www.rust-lang.org/">Rust programming language</a> uses
LLVM for code generation, and heavily relies on LLVM’s optimization
capabilities. However, there are many cases where LLVM fails to optimize
typical code patterns that are emitted by rustc. Such issues are reported
using the <a href="https://github.com/rust-lang/rust/issues?q=is%3Aopen+is%3Aissue+label%3AI-slow">I-slow</a>
and/or <a href="https://github.com/rust-lang/rust/issues?q=is%3Aopen+is%3Aissue+label%3AA-LLVM">A-LLVM</a> labels.
<br />
The usual approach to fixing these issues is:
<ol>
<li>Inspect the <code>--emit=llvm-ir</code> output on
<a href="https://rust.godbolt.org/">Godbolt</a>.</li></li>
<li>Create an LLVM IR test case that is not optimized when run through
<code>opt -O3</code>.</li>
<li>Identify a minimal missing transform and prove its correctness
using <a href="https://alive2.llvm.org/ce/">alive2</a>.</li>
<li>Identify which LLVM pass or passes could perform the transform.</li>
<li>Add necessary test coverage and implement the transform.</li>
<li>(Much later: Check that the issue is really resolved after the next
major LLVM version upgrade in Rust.)</li>
</ol>
The goal of this project is to address some of the less hard optimization
failures. This means that in some cases, the process would stop after step 3
or 4 without proceeding to implementation, because it’s unclear how the issue
could be addressed, or it would take a large amount of effort. Having an
analysis of the problem is still valuable in that case.
</p>
<p><b>Expected result:</b>
Fixes for a number of easy to medium Rust optimization failures. Preliminary
analysis for some failures even if no fix was implemented.</p>
<p><b>Desirable skills:</b>
Intermediate C++ for implementation. Some familiarity with LLVM (at least
ability to understand LLVM IR) for analysis. Basic Rust knowledge (enough
to read, but not write Rust).</p>
<p><b>Project size:</b> Either medium or large.</p>
<p><b>Difficulty:</b> Medium</p>
<p><b>Confirmed Mentor:</b> <a href="https://github.com/nikic">Nikita Popov</a></p>
<p><b>Discourse:</b> <a href="https://discourse.llvm.org/t/llvm-addressing-rust-optimization-failures-in-llvm/68096">URL</a>
</div>
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<div class="www_subsubsection">
<a name="remove_ub_tests">Remove undefined behavior from tests</a>
</div>
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<div class="www_text">
<p><b>Description of the project:</b>
Many of LLVM's unit tests have been reduced automatically from larger tests.
Previous-generation reduction tools used undef and poison as placeholders
everywhere, as well as introduced undefined behavior (UB).
Tests with UB are not desirable because 1) they are fragile since in the
future the compiler may start optimizing more aggressively and break the
test, and 2) it breaks translation validation tools such as
<a href="https://github.com/AliveToolkit/alive2/">Alive2</a> (since it's
correct to translate a fuction that is always UB into anything).
<br />
The major steps include:
<ol>
<li>Replace known patterns such as branch on undef/poison, memory accesses
with invalid pointers, etc with non-UB patterns.</li>
<li>Use Alive2 to detect further patterns (by searching for tests that are
always UB.</li>
<li>Report any LLVM bug found by Alive2 that is exposed when removing
UB.</li>
</ol>
</p>
<p><b>Expected result:</b>
The majority of LLVM's unit tests will be free of UB.</p>
<p><b>Project size:</b> Either medium or large.</p>
<p><b>Difficulty:</b> Medium</p>
<p><b>Confirmed Mentor:</b> <a href="https://web.ist.utl.pt/nuno.lopes/">Nuno Lopes</a></p>
</div>
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<div class="www_subsubsection">
<a name="clang-repl-out-of-process">Out-of-process execution for clang-repl</a>
</div>
<!-- *********************************************************************** -->
<div class="www_text">
<p><b>Description of the project:</b>
The Clang compiler is part of the LLVM compiler infrastructure and supports
various languages such as C, C++, ObjC and ObjC++. The design of LLVM and
Clang enables them to be used as libraries, and has led to the creation of
an entire compiler-assisted ecosystem of tools. The relatively friendly
codebase of Clang and advancements in the JIT infrastructure in LLVM further
enable research into different methods for processing C++ by blurring the
boundary between compile time and runtime. Challenges include incremental
compilation and fitting compile/link time optimizations into a more dynamic
environment.
<br /> <br />
Incremental compilation pipelines process code chunk-by-chunk by building an
ever-growing translation unit. Code is then lowered into the LLVM IR and
subsequently run by the LLVM JIT. Such a pipeline allows creation of
efficient interpreters. The interpreter enables interactive exploration and
makes the C++ language more user friendly. Clang-Repl is one example.
<br /> <br />
Clang-Repl uses the Orcv2 JIT infrastructure within the same process. That
design is efficient and easy to implement however it suffers from two
significant drawbacks. First, it cannot be used in devices which do not have
sufficient resources to host the entire infrastructure, such as the arduino
due (see this
<a href="https://compiler-research.org/meetings/#caas_10Mar2022">talk</a>
for more details). Second, crashes in user codes mean that the entire
process crashes, hindering overall reliability and ease of use.
<br /> <br />
This project aims to move Clang-Repl to an out-of-process execution model
in order to address both of these issues.
</p>
<p><b>Expected result:</b>
Implement an out-of-process execution of statements with Clang-Repl;
Demonstrate that Clang-Repl can support some of the ez-clang use-cases;
Research into approaches to restart/continue the session upon crash;
As a stretch goal design a versatile reliability approach for crash recovery;
</p>
<p><b>Project size:</b>Either medium or large.</p>
<p><b>Difficulty:</b> Medium</p>
<p><b>Confirmed Mentor:</b>
<a href=https://github.com/vgvassilev>Vassil Vassilev</a>,
<a href=https://github.com/weliveindetail>Stefan Gränitz</a>,
<a href=https://github.com/lhames>Lang Hames</a>
</p>
<p><b>Discourse:</b> <a href="https://discourse.llvm.org/t/clang-out-of-process-execution-for-clang-repl/68225">URL</a>
</div>
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<div class="www_subsubsection">
<a name="clang-repl-autocompletion">Implement autocompletion in clang-repl</a>
</div>
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<div class="www_text">
<p><b>Description of the project:</b>
The Clang compiler is part of the LLVM compiler infrastructure and supports
various languages such as C, C++, ObjC and ObjC++. The design of LLVM and
Clang enables them to be used as libraries, and has led to the creation of
an entire compiler-assisted ecosystem of tools. The relatively friendly
codebase of Clang and advancements in the JIT infrastructure in LLVM further
enable research into different methods for processing C++ by blurring the
boundary between compile time and runtime. Challenges include incremental
compilation and fitting compile/link time optimizations into a more dynamic
environment.
<br /> <br />
Incremental compilation pipelines process code chunk-by-chunk by building an
ever-growing translation unit. Code is then lowered into the LLVM IR and
subsequently run by the LLVM JIT. Such a pipeline allows creation of
efficient interpreters. The interpreter enables interactive exploration and
makes the C++ language more user friendly. The incremental compilation mode
is used by the interactive C++ interpreter, Cling, initially developed to
enable interactive high-energy physics analysis in a C++ environment.
<br /> <br />
<a href="https://compiler-research.org/">Our group</a> puts efforts to
incorporate and possibly redesign parts of Cling in Clang mainline through a
new tool, clang-repl. The project aims at the design and implementation of
robust autocompletion when users type C++ at the prompt of clang-repl.
For example:
<pre>
[clang-repl] class MyLongClassName {};
[clang-repl] My<tab>
// list of suggestions.
</pre>
</p>
<p><b>Expected result:</b>
There are several foreseen tasks:
<ul>
<li>Research the current approaches for autocompletion in clang such as
clang -code-completion-at=file:col1:col2.</li>
<li>Implement a version of the autocompletion support using the partial
translation unit infrastructure in clang’s libInterpreter.</li>
<li>Investigate the requirements for semantic autocompletion which takes
into account the exact grammar position and semantics of the code. Eg:
<pre>
[clang-repl] struct S {S* operator+(S&) { return nullptr;}};
[clang-repl] S a, b;
[clang-repl] v = a + <tab> // shows b as the only acceptable choice here.
</pre>
</li>
<li>Present the work at the relevant meetings and conferences.</li>
</p>
<p><b>Project size:</b>Large.</p>
<p><b>Difficulty:</b> Medium</p>
<p><b>Confirmed Mentor:</b>
<a href=https://github.com/vgvassilev>Vassil Vassilev</a>
</p>
<p><b>Discourse:</b> <a href="https://discourse.llvm.org/t/clang-repl-implement-autocompletion-in-clang-repl/60364">URL</a>
</div>
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<div class="www_subsubsection">
<a name="clang-modules-build-daemon">Modules build daemon: build system agnostic support for explicitly built modules</a>
</div>
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<div class="www_text">
<p><b>Description of the project:</b> Clang currently handles modules independently in each
<code>clang</code> instance using the filesystem for synchronization of which instance builds
a given module. This has many issues with soundness and performance due to tradeoffs made for
module reuse and filesystem contention.</p>
<p>Clang has another way of building modules, explicitly built modules, that currently requires
build system changes to adopt. Here the build system determines which modules are needed, for
example by using <a href="https://github.com/llvm/llvm-project/tree/main/clang/tools/clang-scan-deps">clang-scan-deps</a>,
and ensures those modules are built before running the <code>clang</code> compile task that
needs them.</p>
<p>In order to allow adoption of this new way of building modules without major build system work
we need a module build daemon. With a small change to the command line, clang will connect to
this daemon and ask for the modules it needs. The module build daemon then either returns an
existing valid module, or builds and then returns it.</p>
<p>There is an existing open source dependency scanning daemon that is in a llvm-project fork.
This only handles file dependencies, but has an IPC mechanism. This IPC system could be used as
a base for the modules build daemon, but does need to be extended to work on Windows.</p>
<p><b>Expected result:</b> A normal project using Clang modules with an existing build system
(like Make or CMake) can be built using only explicitly built modules via a modules build
daemon.</p>
<p><b>Desirable skills:</b> Intermediate C++ programming skills; familiarity with compilers;
familiarity with Clang is an asset, but not required.</p>
<p><b>Project size:</b> 175h or 350h depending on reuse of IPC</p>
<p><b>Difficulty:</b> medium</p>
<p><b>Confirmed Mentors:</b>
<a href="https://github.com/Bigcheese">Michael Spencer</a>,
<a href="https://github.com/jansvoboda11">Jan Svoboda</a>
</p>
<p><b>Discourse:</b> <a href="https://discourse.llvm.org/t/clang-modules-build-daemon-build-system-agnostic-support-for-explicitly-built-modules/68224">URL</a></p>
</div>
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<div class="www_subsubsection">
<a name="clang-extract-api-categories">ExtractAPI Objective-C categories</a>
</div>
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<div class="www_text">
<p><b>Description of the project:</b> <a href="https://github.com/apple/swift-docc">Swift-DocC</a> is
the canonical documentation compiler for the Swift OSS project. However
Swift-DocC is not Swift specific and
uses <a href="https://github.com/apple/swift-docc-symbolkit/blob/main/openapi.yaml">SymbolKit</a>'s
languaguage agnostic JSON-based symbol graph format to understand which
symbols are available in the code, this way any language can be supported by
Swift-DocC as long as there is a symbol graph generator.</p>
<p>Clang supports symbol graph generation for C and Objective-C as described
in <a href="https://discourse.llvm.org/t/rfc-clang-support-for-api-information-generation-in-json/58845">[RFC]
clang support for API information generation in JSON</a>. Today, support for
Objective-C categories is not complete, on one hand if the category extends a
type in the current module, the category members are assumed to belong to the
extended type itself. On the other hand, if the extended type belongs to
another module the category is ignored. Nonetheless, it is common to extend
types belonging to other modules in Objective-C as part of the public API of
the module. The goal of this project is to extend the symbol graph format to
accommodate Objective-C categories and to implement support for generating
this information both through clang and through libclang.</p>
<p><b>Expected result:</b> Adding the necessary support to clang's symbol graph
generator and in libclang for describing categories of symbols defined in
other modules. This might involve additions to SymbolKit that would need to be
discussed with that community.</p>
<p><b>Desirable skills:</b> Intermediate C++ programming skills; familiarity
with clang and Objective-C are assets but not required.</p>
<p><b>Project size:</b> Medium</p>
<p><b>Difficulty:</b> Medium</p>
<p><b>Confirmed Mentors:</b>
<a href="https://github.com/daniel-grumberg">Daniel Grumberg</a>,
<a href="https://github.com/zixu-w">Zixu Wang</a>,
<a href="https://github.com/ributzka">Juergen Ributzka</a>
</p>
<p><b>Discourse:</b> <a href="https://discourse.llvm.org/t/clang-extractapi-objective-c-categories/68370">URL</a></p>
</div>
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<div class="www_subsubsection">
<a name="clang-extract-api-cpp-support">ExtractAPI C++ Support</a>
</div>
<!-- *********************************************************************** -->
<div class="www_text">
<p><b>Description of the project:</b> <a href="https://github.com/apple/swift-docc">Swift-DocC</a> is
the canonical documentation compiler for the Swift OSS project. However
Swift-DocC is not Swift specific and
uses <a href="https://github.com/apple/swift-docc-symbolkit/blob/main/openapi.yaml">SymbolKit</a>'s
languaguage agnostic JSON-based symbol graph format to understand which
symbols are available in the code, this way any language can be supported by
Swift-DocC as long as there is a symbol graph generator.</p>
<p>Clang supports symbol graph generation for C and Objective-C as described
in <a href="https://discourse.llvm.org/t/rfc-clang-support-for-api-information-generation-in-json/58845">[RFC]
clang support for API information generation in JSON</a>.</p>
<p>Currently the emitted symbol graph format does not support various C++
constructs such as templates and exceptions and the symbol graph generator
does not fully understand C++. This project aims to introduce support for
various C++ constructs in the symbol graph format and to implement support for
generating this data in clang.</p>
<p><b>Expected result:</b> Adding the necessary support to clang's symbol graph
generator and in libclang for describing categories of symbols defined in
other modules. This will involve additions to SymbolKit that would need to be
discussed with that community.</p>
<p><b>Desirable skills:</b> Intermediate C++ programming skills; familiarity
with clang and Objective-C are assets but not required.</p>
<p><b>Project size:</b> Large</p>
<p><b>Difficulty:</b> Medium/Hard</p>
<p><b>Confirmed Mentors:</b>
<a href="https://github.com/daniel-grumberg">Daniel Grumberg</a>,
<a href="https://github.com/zixu-w">Zixu Wang</a>,
<a href="https://github.com/ributzka">Juergen Ributzka</a>
</p>
<p><b>Discourse:</b> <a href="https://discourse.llvm.org/t/extractapi-c-support/68371">URL</a></p>
</div>
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<div class="www_subsubsection">
<a name="clang-extract-api-while-building">ExtractAPI while building</a>
</div>
<!-- *********************************************************************** -->
<div class="www_text">
<p><b>Description of the project:</b> <a href="https://github.com/apple/swift-docc">Swift-DocC</a> is
the canonical documentation compiler for the Swift OSS project. However
Swift-DocC is not Swift specific and
uses <a href="https://github.com/apple/swift-docc-symbolkit/blob/main/openapi.yaml">SymbolKit</a>'s
languaguage agnostic JSON-based symbol graph format to understand which
symbols are available in the code, this way any language can be supported by
Swift-DocC as long as there is a symbol graph generator.</p>
<p>Clang supports symbol graph generation for C and Objective-C as described
in <a href="https://discourse.llvm.org/t/rfc-clang-support-for-api-information-generation-in-json/58845">[RFC]
clang support for API information generation in JSON</a>.</p>
<p>Currently users can use clang to generate symbol graph files using
the <code>clang -extract-api</code> command line interface or generating
symbol graphs for a specific symbol using the libclang interface. This project
would entail adding a third mode that would generate the symbol graph output
as a side-effect of a regular compilation job. This can enable using the
symbol graph format as a light weight alternative to clang Index or clangd
for code intelligence services.</p>
<p><b>Expected result:</b> Enable generating symbol graph files during a
regular compilation (or module build); provide a tool to merge symbol graph
files in the same way a static linker links individual object files; Extend
clang Index to support all the information contained by symbol graph
files.</p>
<p><b>Desirable skills:</b> Intermediate C++ programming skills; familiarity
with clang and Objective-C are assets but not required.</p>
<p><b>Project size:</b> Medium</p>
<p><b>Difficulty:</b> Medium/Hard</p>
<p><b>Confirmed Mentors:</b>
<a href="https://github.com/daniel-grumberg">Daniel Grumberg</a>,
<a href="https://github.com/zixu-w">Zixu Wang</a>,
<a href="https://github.com/ributzka">Juergen Ributzka</a>
</p>
<p><b>Discourse:</b> <a href="https://discourse.llvm.org/t/clang-extractapi-while-building/68372">URL</a></p>
</div>
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<div class="www_subsubsection">
<a id="llvm_lld_embedded">LLD Linker Improvements for Embedded Targets</a>
</div>
<!-- *********************************************************************** -->
<div class="www_text">
<p><b>Description of the project</b>
GNU toolchain is used widely for building embedded targets. There's a certain momentum in the
Clang/LLVM community towards improving the Clang toolchain to support embedded targets. Using
the Clang toolchain as an alternative can help us improve code quality, find and fix security
bugs, improve developer experience and take advantage of the new ideas and the momentum
surrounding the Clang/LLVM community in supporting embedded devices.
</p>
<p><b>A non-comprehensive list of improvements that can be made to LLD</b>:
<ul>
<li>
<p><b>--print-memory-usage support</b></p>
<p>"--print-memory-usage" in GCC provides a breakdown of the memory used in each memory region
defined in the linker file. Embedded developers use this flag to understand the impact on
memory. Often embedded systems define multiple memory regions with different space
constraints. Supporting this in Clang toolchain will help projects that wish to use Clang
toolchain for their projects.</p>
</li>
<li>
<p><b>Linkmap</b></p>
<p>Currently, the LLD linker's linkmap output is not as rich as the BFD linker output.
Achieving feature parity on linkmap output will be highly
useful in analyzing the binaries created by the LLD linker. Further, outputting linkmap in
different formats (current LLD output, BFD, and JSON) can help build automation tools for
investigating the artifacts produced by the linker.</p>
</li>
<li>
<p><b>--print-gc-sections improvement</b></p>
<p>When the "--print-gc-sections" flag is enabled, LLD prints the sections that were
discarded during the linking process. This information currently does not include the
mapping between the symbol and the section groups, which is useful for debugging.
Preserving this information during the linking process will require modifications to
internal linker data structures.</p>
</li>
</ul>
<p><b>Project size:</b> Medium or Large</p>
<p><b>Difficulty:</b> Medium/Hard</p>
<p><b>Skills:</b> C++</p>
<p><b>Expected result</b>:
<ul>
<li>Implementation of "--print-memory-usage" flag.</li>
<li>Support for new linkmap output formats 1. BFD and 2. JSON. </li>
<li>Improved "--print-gc-sections" output to include information about the surviving symbols.</li>
</ul>
</p>
<p><b>Confirmed Mentors:</b>
<a href="https://github.com/Prabhuk">Prabhu Rajasekaran</a>
<a href="https://github.com/petrhosek">Petr Hosek</a>
</p>
<p><b>Discourse:</b> <a href="https://discourse.llvm.org/t/lld-linker-improvements-for-embedded/68129">URL</a>
</div>
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<div class="www_subsubsection">
<a id="llvm_mlir_presburger_opt">Optimizing MLIR’s Presburger library </a>
</div>
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<div class="www_text">
<p><strong>Description</strong>: MLIR’s Presburger Library, FPL (<a href="https://grosser.science/FPL">https://grosser.science/FPL</a>), provides mathematical abstractions for polyhedral compilation and analysis. The main abstraction that the library provides is a set of integer tuples defined by a system of affine inequality constraints. The library supports standard set operations over such sets. The result will be a set defined by another constraint system, possibly having more constraints. When many set operations are performed in sequence, the constraint system may become very large, negatively impacting performance. There are several potential ways to simplify the constraint system; however, this involves performing additional computations. Thus, spending more time on more aggressive simplifications may make each individual operation slower, but at the same time, insufficient simplifications can make sequences of operations slow due to an explosion in constraint system size. The aim of this project is to find the right balance between the two.</p>
<p><strong>The goals of this project:</strong></p>
<ul>
<li>Understand the library's performance in terms of runtime and output size.</li>
<li>Optimize the library by finding the best output size and performance tradeoff.</li>
</ul>
<p><strong>Expected outcomes</strong>:</p>
<ul>
<li>Benchmarking the performance and output constraint complexity of the primary operations of the library.</li>
<li>Implementing simplification heuristics.</li>
<li>A better understanding of which simplification heuristics improve overall performance enough to be worth the additional computational cost.</li>
</ul>
<p><strong>Desirable skills</strong>: Intermediate C++, Experience in benchmarking</p>
<p><strong>Project size</strong>: Large</p>
<p><strong>Difficulty</strong>: Medium</p>
<p><strong>Confirmed mentors</strong>: <a href="https://github.com/Groverkss">Kunwar Grover</a></p>
<p><strong>Discourse</strong>: <a href="https://discourse.llvm.org/t/mlir-optimizing-mlir-s-presburger-library/68213/1">URL</a></p>
</div>
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<div class="www_subsubsection">
<a id="llvm_mlgo_latency_model">Better performance models for MLGO training</a>
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<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>
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<div class="www_subsubsection">
<a name="clang_analyzer_taint_analysis">Improve and Stabilize the Clang Static Analyzer's "Taint Analysis" Checks</a>
</div>
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<div class="www_text">
<p><b>Description of the project:</b>
The Clang static analyzer comes with an experimental implementation of
taint analysis, a security-oriented analysis technique built to warn
the user about flow of attacker-controlled ("tainted") data into
sensitive functions that may behave in unexpected and dangerous ways
if the attacker is able to forge the right input. The programmer can address
such warnings by properly "sanitizing" the tainted data in order to
eliminate these dangerous inputs. A common example of a problem that can be
caught this way is <a href="https://xkcd.com/327/">SQL injections</a>.
A much simpler example, which is arguably much more relevant to users