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Open LLVM Projects

Written by the LLVM Team

Welcome prospective Google Summer of Code 2018 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.

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. You must propose your idea to the LLVM community through our developers' mailing list (llvm-dev@lists.llvm.org or specific subproject mailing list). Feedback from the community is a requirement for your proposal to be considered and hopefully accepted.

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 website.

Description of the project: After instruction selection LLVM uses the MI (Machine Instruction) representation for programs. We recently added support for reading and writing this representation to disk (http://llvm.org/docs/MIRLangRef.html). Usage of this format for writing tests is growing and so is the desire to improve the format, tools and workflow. Possible projects:

  • Create a single consistent format instead of the current mix of YAML + IR + MIR
  • Helper passes: Strip IR information, rename blocks and values, debug information, extract functions or blocks...
  • Create a bugpoint mode (or a new tool) to reduce .mir test cases
  • Create tool to remove unnecessary information from existing tests

Confirmed Mentor: Matthias Braun

Desirable skills: Intermediate knowledge of C++.

Description of the project: Debugging optimized code can be frustrating. Variables may appear as "<value optimized out>" in the debugger, or may not appear at all. Line numbers in stack traces may disappear, or worse, become inaccurate. To improve the situation, we have to teach more LLVM optimization passes how to preserve debug info. The primary focus will be on mid-level IR passes which fail to pass verification by the Debugify utility. This utility can identify passes which drop debug info in a targeted way and can simplify test case generation.

Expected Results:This project has two goals. Initially, the student will gather metrics on debug info loss for individual llvm passes. This will let us measure subsequent improvements. The second goal is to incrementally fix as many debug info loss bugs as possible, with a focus on areas of the compiler which are the hottest.

Confirmed Mentor: Vedant Kumar and Davide Italiano

Desirable skills: Intermediate knowledge of C++.

Description of the project: The llvm project has a lot of tools that can be used to inspect binaries, as any other toolchain project does. Many people are accustomed to existing tools and so having a command line compatible shell and we'd like to make that easy for them. Bonus points for producing similar output so that automated tools can continue to work reliably.

Expected Results:This project has one goal - produce binary tools that are drop in compatible with GNU binutils. The student will be expected to focus on a single tool at a time so that we can count each one as "done" as much as possible.

Confirmed Mentor: Eric Christopher and Sterling Augustine

Desirable skills: Intermediate knowledge of C++.

Description of the project: Dominance relation is used widely in many compiler analyses and optimizations. LLVM provides an implementation of the (Semi-NCA) Depth Based Search algorithm to incrementally update Dominator and PostDominator Trees. It it possible to use it directly or through a lazy updater object -- DeferredDominance. The current API is fragmented and different analyzes, transforms, and utilities (e.g. Local.cpp, LoopUnroll.cpp) have to decide how to perform incremental updates.

The fix would be to design and implement a new class for abstracting away how tree updates are performed (eagerly or lazily) and which trees are actually being updated (none, only DomTree, only PostDomTree, both). With this, performing faster incremental updates will become possible by first updating DomTree, and then using the result to prune unnecessary updates to PostDomTree.

Expected Results:

  • Create a new class that will store Dominator Trees to update and allow to specify update policy.
  • Convert existing API to use the new updater object instead of working directly with DomTree/DeferredDominance.
  • Design and implement a new algorithm to prune unnecessary PostDomTree updates based on updated DomTree.
  • (Optional) Explore more strategies for performing lazy updates (e.g. by exposing .dominates() and performing updates when called).

Confirmed Mentor: Jakub Kuderski, Tobias Grosser (?)

Desirable skills: Intermediate knowledge of C++. Familiarity with basic tree/graph algorithms and data structures.

Description of the project: LLVM functions can be tagged with several attributes such as the function only reads memory, or the function cannot throw exceptions. These attributes are used by many optimizations when deciding if a particular transformation is valid or not. Functions attributes can be either given by the frontend or be inferred by LLVM.
The goal of this project is to improve current function attributes inference algorithms, and to infer attributes that are not inferred right now. This will be accomplished via intra- and/or inter-procedural analyses. See this email for a list of oportunities for improvement.

Confirmed Mentor: Nuno Lopes

Desirable skills: Intermediate knowledge of C++. Optional familiarity with static analysis/abstract interpretation.

Description of the project: Clang has a newly implemented autocompletion feature which details can be found at LLVM blog. We would like to improve this by adding more flags to autocompletion, supporting more shells (currently it supports only bash) and exporting this feature to other projects such as llvm-opt. Accepted student will be working on Clang Driver, LLVM Options and shell scripts.

Expected Results: Autocompletion working on bash and zsh, support llvm-opt options.

Confirmed Mentor: Yuka Takahashi and Vassil Vassilev

Desirable skills: Intermediate knowledge of C++ and shell scripting

Description of the project: Just as LLVM is a library to build compilers, LLDB is a library to build debuggers. LLDB vends a stable, public SB API. Due to historic reasons the LLDB command line interface is currently implemented on top of LLDB's private API and it duplicates a lot of functionality that is already implemented in the public API. Rewriting LLDB's command line interface on top of the public API would simplify the implementation, eliminate duplicate code, and most importantly reduce the testing surface.

This work will also provide an opportunity to clean up the SB API of commands that have accrued too many overloads over time and convert them to make use of option classes to both gather up all the variants and also future-proof the APIs.

Confirmed Mentor:Adrian Prantl and Jim Ingham

Desirable skills: Intermediate knowledge of C++.

Description of the project: LLDB's data formatters allow it to pretty-print objects such as std::vector (from the C++ standard library), or String (from the Swift standard library). These data formatters are implemented in C++ and reside within the debugger, but the data structures are defined in other projects. This means that when the data structures change, lldb's data formatters may not be updated in sync. This also means that it's difficult for projects to define and test custom data formatters for special kinds of objects.

Expected results: The goal of this project would be to define a DSL which makes it possible to implement lldb data formatters for standard C++ containers. These formatters would be moved into libc++ and tested there.

Confirmed Mentor: Vedant Kumar and Davide Italiano

Desirable skills: Intermediate knowledge of C++.

Description of the project: lldb-mi implements a machine-readable interface that is supported by many IDEs and text editors. The current support is incomplete and does not implement enough commands to work with most text editors. More importantly, it isn't using the right abstraction layer: Instead of executing textual commands via handleCommand() and scraping LLDB's textual output, it should be using the methods and data structures provided by the public SB API.

Confirmed Mentor: Adrian Prantl

Desirable skills: Intermediate knowledge of C++.

Description of the project: One of the tensions in the testsuite is that spinning up a process and getting it to some point is not a cheap operation, so you'd like to do a bunch of tests when you get there. But the current testsuite bails at the first failure, so you don't want to do many tests since the failure of one fails all the others. On the other hand, there are some individual test assertions where the failure of the assertion should cause the whole test to fail. For example, if you fail to stop at a breakpoint where you want to check some variable values, then the whole test should fail. But if your test then wants to check the value of five independent locals, it should be able to do all five, and then report how many of the five variable assertions failed. We could do this by adding Start and End markers for a batch of tests, do all the tests in the batch without failing the whole test, and then report the error and fail the whole test if appropriate. There might also be a nice way to do this in Python using scoped objects for the test sections.

Confirmed Mentor: Jim Ingham

Desirable skills: Intermediate knowledge of Python.

Description of the project: apt.llvm.org provides Debian and Ubuntu repositories for every maintained version of these distributions. LLVM, Clang, clang extra tools, compiler-rt, polly, LLDB and LLD packages are generated for the stable, stabilization and development branches.
These packages are also shipped as part of Debian and Ubuntu without any changes.
Debian and Ubuntu have separate packages for libc++ and OpenMP.
The goal of this project is to merge libc++ and OpenMP packages as part of the llvm-toolchain packages.
The difficulty of this project is to make different versions of these libraries co-installable while remaining usable for developers. This project will also aim to limit the impact on existing usage of these libraries.

If the project is completed early, the student will also work on the full bootstrap of the llvm-toolchain (ie building it with a newly built clang binary).

Expectation: The student must have demonstrated some experience with Debian/Ubuntu packaging. Debian maintains a list of good first bugs. Please mention any packaging related contribution in the GSoC application.

Confirmed Mentor: Sylvestre Ledru

Description of the project: apt.llvm.org provides Debian and Ubuntu repositories for every maintained version of these distributions. LLVM, Clang, clang extra tools, compiler-rt, polly, LLDB and LLD packages are generated for the stable, stabilization and development branches.
Currently, the packages are build using a Jenkins instance and Jenkins-Debian-Glue.
The goal of this project is to deploy an instance of Open Build Service (OBS) and port the various scripts and packages to this platform.

In theory, apt.llvm.org should not have any change from the user perspective.

Expectation: The student must have demonstrated some experience with Debian/Ubuntu packaging. Debian maintains a list of good first bugs. Please mention any packaging related contribution in the GSoC application.

Confirmed Mentor: Sylvestre Ledru

Description of the project: The C++ std::string class provides a c_str() method that returns a raw pointer to a string's inner character buffer. When a std::string is destroyed, the character buffer is deallocated. A common bug is to access a dangling raw pointer to the buffer after string deallocation. These "use after free" bugs can cause crashes or other unexpected behavior.
This project will add a new checker to the static analyzer to find when a dangling inner string pointer is used. This will help find bugs not only with std::string and c_str() but also with LLVM's StringRef class and the new C++17 std::string_view.

Confirmed Mentor: Artem Dergachev

Desirable skills: Intermediate knowledge of C++.

Description of the project: The static analyzer finds bugs by exploring many possible paths through a program. To reduce false positives, it uses a very fast but imprecise custom constraint manager to rule out infeasible paths that cannot actually be executed at run time.
This project will extend the analyzer to use the Z3 SMT solver to rule out additional infeasible paths by postprocessing bug reports. This will help the analyzer reduce false positives when the path involves complicated branches that the built-in constraint manager cannot reason about.

Confirmed Mentor: George Karpenkov

Desirable skills: Intermediate knowledge of C++ and basic familiarity with the capabilities SMT solvers

Description of the project: Clang-doc is a new tool for generating documentation for C/C++ code with a modular and extensible approach. It aims to simplify the overhead of generating documentation, leveraging the clang AST to produce results from existing comments and code. The main part of the tool produces an intermediate representation of the docs, which is consumed by a generator targeting a specific output format. Current and in-progress generators emit documentation in YAML and Markdown formats, but we’d like to have another one for HTML format.

Confirmed Mentor: Julie Hockett

Desirable skills: Intermediate knowledge of C++.

Google Summer of Code 2017 contributed a lot to the LLVM project. Below is a list of some projects that were offerred during GSoC 2017. For the list of accepted and completed projects, please take a look into Google Summer of Code website.

Description of the project:

Description of the project: The optimizer is 25-30% slower when debug info are enabled, it'd be nice to track all the places where we don't do a good job about ignoring them!

Description of the project: ThinLTO is a cool new technology to perform Link-Time Optimization (see this talk for more info). It is fairly new and there are multiple improvements about cross-module optimizations that can be made there.

Description of the project: Adding Debug Info (compiling with `clang -g`) shouldn't change the generated code at all. Unfortunately we have bugs… These are usually not too hard to fix and a good way to discover new part of the codebase! A starting point could be the test-suite. We suggest building object files both ways and disassembling the text sections, which will give cleaner diffs than comparing .s files.

Description of the project:

Description of the project: See this talk for a starting point. Please take a look at the list of enhancements and bugs in bugzilla.

Description of the project: Smarter way of dumping LLVM ir with -emit-after-all - dump only if it differs from last pass. Maybe small color the IR?

Description of the project: After instruction selection LLVM uses the MI (Machine Instruction) representation for programs. We recently added support for reading and writing this representation to disk (http://llvm.org/docs/MIRLangRef.html). Usage of this format for writing tests is growing and so is the desire to improve the format, tools and workflow. Improvements would be welcome:

  • Create a single consistent format instead of the current mix of YAML + IR + MIR
  • Do not print unnecessary information (we often print default values where the reader could deduce them)
  • The format of things like MachineInstr/MachineBasicBlock::dump() should be the same or very close to the .mir format => change the dump functions.
  • Allow the representation to deduce successors of a basic block in common cases
  • Allow symbolic names instead of only numbers for virtual registers
  • Helper passes: Strip IR information, rename blocks and values, debug information, ...
  • Create a bugpoint mode (or a new tool) to reduce .mir test cases
  • Write recommendations and guides for .mir based tests

Confirmed Mentor: Matthias Braun

Description of the project: When instantiating a template, the template arguments are canonicalized before being substituted into the template pattern. Clang does not preserve type sugar when subsequently accessing members of the instantiation.

    std::vector<std::string> vs;
    int n = vs.front(); // bad diagnostic: [...] aka 'std::basic_string<char>' [...]

    template<typename T> struct Id { typedef T type; };
    Id<size_t>::type // just 'unsigned long', 'size_t' sugar has been lost
    
Clang should "re-sugar" the type when performing member access on a class template specialization, based on the type sugar of the accessed specialization. The type of vs.front() should be std::string, not std::basic_string<char, [...]>.

Suggested design approach: add a new type node to represent template argument sugar, and implicitly create an instance of this node whenever a member of a class template specialization is accessed. When performing a single-step desugar of this node, lazily create the desugared representation by propagating the sugared template arguments onto inner type nodes (and in particular, replacing Subst*Parm nodes with the corresponding sugar). When printing the type for diagnostic purposes, use the annotated type sugar to print the type as originally written.

For good results, template argument deduction will also need to be able to deduce type sugar (and reconcile cases where the same type is deduced twice with different sugar).

Expected results: Diagnostics preserve type sugar even when accessing members of a template specialization. T<unsigned long> and T<size_t> are still the same type and the same template instantiation, but T<unsigned long>::type single-step desugars to 'unsigned long' and T<size_t>::type single-step desugars to 'size_t'.

Confirmed Mentor: Vassil Vassilev, Richard Smith

Desirable skills: Good knowledge of clang API, clang's AST, intermediate knowledge of C++.

Description of the project: Bash and other shells support typing a partial command and then automatically completing it for the user (or at least providing suggestions how to complete) when pressing the tab key. This is usually only supported for popular programs such as package managers (e.g. pressing tab after typing "apt-get install late" queries the APT package database and lists all packages that start with "late"). As of now clang's frontend isn't supported by any common shell.

Suggested design approach: The main goal is to support a variety of terminals. It would be preferable to keep each shell plugin minimal, enabling easy addition of new plugins. The implementation ought to extend the clang driver switches with a flag to request auto-completion of a partial shell command.

The final auto-completion support should contain features such as:

  • Searching/correcting all flags. For example:
    • typing clang -tri[tab] should complete to -trigraphs
    • typing clang -cc1 -dump-decls[tab] should correct -dump-decls to -dump-deserialized-decls
  • Searching for valid arguments for the flags. For example:
    • typing clang -cc1 -analyze -analyzer-checker=[tab] -fsyntax-only should list all available static analyzers (eg. core.DivideZero)
    • typing clang -std=[tab] should list the available language standards.
  • When the auto-completion for a partial command is ambiguous, all possible completions should be displayed with the relevant description from the "--help" output beside it ( example of this in fish with the command "ls").

Expected results: Bash supports abovementioned auto-complete examples on at least OS X and Linux.

Confirmed Mentor: Vassil Vassilev, Raphael Isemann

Desirable skills: Intermediate knowledge of C++ and shell scripting, Intermediate knowledge of the clang driver.

Description of the project: Every developer has to interact with diff tools daily. The algorithms are usually based on detecting "longest common subsequences", which is agnostic to the file type content. A tool that would understand the structure of the code may provide a better diff experience by being robust against, for example, clang-format changes.

Confirmed Mentor: Mehdi Amini

Description of the project: Find dereference of pointer before checking for nullptr, like:

      Int * p = foo();
      *p = 42;
      If (p != nullptr) { // p != nullptr is considered as always true
      }
      If (!p) { // !p is considered as always false
      }
    


This check should be easier to write in clang-tidy than in Clang Static Analyzer, specially because that we don't care about inlining (as long as it doesn't modify pointer). More details in the Bugzilla feature request

Confirmed Mentor: Alexander Kornienko, Piotr Padlewski

Description of the project: Implement a path-sensitive checker that warns if virtual calls are made from constructors and destructors, which is not valid in case of pure virtual calls and could be a sign of user error in non-pure calls.
The current virtual calls checker, implemented in VirtualCallChecker.cpp, needs to be re-implemented in a path-sensitive way. The lack of path-sensitive reasoning may result in false positives in the inter-procedural mode, which is disabled now for that reason. The false positives could happen when a called function uses a member variable flag to track whether initialization is complete and relies on the flag to ensure that the virtual member function is not called during initialization. Further, the path diagnostic should be used to highlight both the virtual call and the path from the constructor. Last, we will need to evaluate if the warning should be issued for both calls to pure virtual functions (which is always an error) and non-pure virtual functions (which is more of a code smell and may be a false positive).

Confirmed Mentor: Anna Zaks

Description of the project: Enhance the clang static analyzer by adding models of C++11 and C11 atomic operations, such as std::atomic_compare_exchange_*. Currently, these operations are being treated opaquely, which results in loss of precision when analyzing the code that uses these instructions. To address the problem, one would need to programmatically construct AST that simulates these APIs to the BodyFarm of the analyzer. BodyFarm is the API used for modeling system APIs. Finally, the work would also include writing tests for the various APIs and checking that the analyzer correctly models atomics.

Confirmed Mentor: Anna Zaks

Description of the project: Many of the projects in compiler-rt are only supported on Linux.
Here are some examples: CFI, DFSan, XSan, LSan, XRay. Porting any of them to other platforms, for example, Mac OS, would be great!

Confirmed Mentor: Kuba Mracek, Anna Zaks

LLD

Description of the project: The goal for the project is trying to improve the layout/performances of the generated executable. The primary object format considered for the project is ELF but this can be extended to other object formats. The project will touch both LLVM and lld.

  • Warm-up: lld already provides an option to (--symbol-ordering file) which takes a symbol ordering file (presumably collected from a profiler) and builds a layout. This aims to reduce startup times. It would be nice to provide scripts to profile the applications/process various profilers output to produce an order file/evaluate the impact of the feature (as it has been tested only on a small class of applications). There's already some work in the area but nothing has been integrated in the LLVM build system for ELF. Ideally a motivated student would do the benchmarking/analysis before the GSoC starts to familiarize with the problem.
  • The meat: Use/extend profile information generated by LLVM to help the linker lay out functions. An example way (what gcc uses, [1]) is to pass values to the linker using special `.note` sections. The linker then can reconstruct the call graph and apply an algorithm like the one described in [2] (this is a starting point, other alternatives can be explored).

Possible extension: Xray can be used to provide data (it's unclear whether this is feasible easily, see David's comment in [3]).

Confirmed Mentor: Davide Italiano

Description of the project: Even though Polly's compile time is today not a lot higher than other non-trial IR passes, the need to version code in many situations and the lack of static knowledge about loop iteration counts, hotness of functions, and parameter requires Polly to be significantly more conservative than it would need to be. The goal of this project is to connect Polly with the LLVM profiling infrastructure to exploit profiling information to decide: 1) when to run Polly, 2) how aggressive to version the code, 3) which code version to emit, and 4) which assumptions to take. As a result, Polly should can in profile guided builds become more aggressive, while still having a lower compile time and code size impact.

Confirmed Mentor: Tobias Grosser

Description of the project:

Over the last years Chandler Carruth and others introduced a new pass manager to LLVM which uses a new caching based architecture to allow analysis results to be computed on demand. Besides resolving many engineering problems, the new pass manager has three interesting properties: 1) analysis results for multiple objects (e.g., functions) can be made available at the same time, 2) it is possible to access the analysis result from one function in another function or the analysis results from a function pass in a call-graph pass. 3) new pass managers can be instantiated easily.

The goal of this project is to port Polly to the new pass manager and use this opportunity to improve the overall pass design of Polly. The first step will be to make Polly future proof by providing the same functionality Polly already has with the old pass manager, in the context of the new pass manager. Next, facilities of the new pass manger can be exploited to remove Polly's dependence on the RegionPass infrastructure, and replace it with a Polly specific scop-pass manager, that executes scop-model only passes without the need to piggy-pack on some IR level analysis. Finally, the student thinks about how analysis results can be made available across functions.

If the project is completed early, the student might look into exploiting the availability of analysis results from multiple functions to perform GPU code generation across functions

Confirmed Mentor: Tobias Grosser

This document is meant to be a sort of "big TODO list" for LLVM. Each project in this document is something that would be useful for LLVM to have, and would also be a great way to get familiar with the system. Some of these projects are small and self-contained, which may be implemented in a couple of days, others are larger. Several of these projects may lead to interesting research projects in their own right. In any case, we welcome all contributions.

If you are thinking about tackling one of these projects, please send a mail to the LLVM Developer's mailing list, so that we know the project is being worked on. Additionally this is a good way to get more information about a specific project or to suggest other projects to add to this page.

The projects in this page are open-ended. More specific projects are filed as unassigned enhancements in the LLVM bug tracker. See the list of currently outstanding issues if you wish to help improve LLVM.

In addition to hacking on the main LLVM project, LLVM has several subprojects, including Clang and others. If you are interested in working on these, please see their "Open projects" page:

Improvements to the current infrastructure are always very welcome and tend to be fairly straight-forward to implement. Here are some of the key areas that can use improvement...

Currently, both Clang and LLVM have a separate target description infrastructure, with some features duplicated, others "shared" (in the sense that Clang has to create a full LLVM target description to query specific information).

This separation has grown in parallel, since in the beginning they were quite different and served disparate purposes. But as the compiler evolved, more and more features had to be shared between the two so that the compiler would behave properly. An example is when targets have default features on speficic configurations that don't have flags for. If the back-end has a different "default" behaviour than the front-end and the latter has no way of enforcing behaviour, it won't work.

An alternative would be to create flags for all little quirks, but first, Clang is not the only front-end or tool that uses LLVM's middle/back ends, and second, that's what "default behaviour" is there for, so we'd be missing the point.

Several ideas have been floating around to fix the Clang driver WRT recognizing architectures, features and so on (table-gen it, user-specific configuration files, etc) but none of them touch the critical issue: sharing that information with the back-end.

Recently, the idea to factor out the target description infrastructure from both Clang and LLVM into its own library that both use, has been floating around. This would make sure that all defaults, flags and behaviour are shared, but would also reduce the complexity (and thus the cost of maintenance) a lot. That would also allow all tools (lli, llc, lld, lldb, etc) to have the same behaviour across the board.

The main challenges are:

  • To make sure the transition doesn't destroy the delicate balance on any target, as some defaults are implicit and, some times, unknown.
  • To be able to migrate one target at a time, one tool at a time and still keep the old infrastructure intact.
  • To make it easy for detecting target's features for both front-end and back-end features, and to merge both into a coherent set of properties.
  • To provide a bridge to the new system for tools that haven't migrated, especially the off-the-tree ones, that will need some time (one release, at least) to migrate..

The LLVM bug tracker occasionally has "code-cleanup" bugs filed in it. Taking one of these and fixing it is a good way to get your feet wet in the LLVM code and discover how some of its components work. Some of these include some major IR redesign work, which is high-impact because it can simplify a lot of things in the optimizer.

Some specific ones that would be great to have:

Additionally, there are performance improvements in LLVM that need to get fixed. These are marked with the slow-compile keyword. Use this Bugzilla query to find them.

The llvm-test testsuite is a large collection of programs we use for nightly testing of generated code performance, compile times, correctness, etc. Having a large testsuite gives us a lot of coverage of programs and enables us to spot and improve any problem areas in the compiler.

One extremely useful task, which does not require in-depth knowledge of compilers, would be to extend our testsuite to include new programs and benchmarks. In particular, we are interested in cpu-intensive programs that have few library dependencies, produce some output that can be used for correctness testing, and that are redistributable in source form. Many different programs are suitable, for example, see this list for some potential candidates.

We are always looking for new testcases and benchmarks for use with LLVM. In particular, it is useful to try compiling your favorite C source code with LLVM. If it doesn't compile, try to figure out why or report it to the llvm-bugs list. If you get the program to compile, it would be extremely useful to convert the build system to be compatible with the LLVM Programs testsuite so that we can check it into SVN and the automated tester can use it to track progress of the compiler.

When testing a code, try running it with a variety of optimizations, and with all the back-ends: CBE, llc, and lli.

Find benchmarks either using our test results or on your own, where LLVM code generators do not produce optimal code or where another compiler produces better code. Try to minimize the test case that demonstrates the issue. Then, either submit a bug with your testcase and the code that LLVM produces vs. the code that it should produce, or even better, see if you can improve the code generator and submit a patch. The basic idea is that it's generally quite easy for us to fix performance problems if we know about them, but we generally don't have the resources to go finding out why performance is bad.

The LNT perf database has some nice features like detect moving average, standard deviations, variations, etc. But the report page give too much emphasis on the individual variation (where noise can be higher than signal), eg. this case.

The first part of the project would be to create an analysis tool that would track moving averages and report:

  • If the current result is higher/lower than the previous moving average by more than (configurable) S standard deviations
  • If the current moving average is more than S standard deviations of the Base run
  • If the last A moving averages are in constant increase/decrease of more than P percent

The second part would be to create a web page which would show all related benchmarks (possibly configurable, like a dashboard) and show the basic statistics with red/yellow/green colour codes to show status and links to more detailed analysis of each benchmark.

A possible third part would be to be able to automatically cross reference different builds, so that if you group them by architecture/compiler/number of CPUs, this automated tool would understand that the changes are more common to one particular group.

The LLVM Coverage Report has a nice interface to show what source lines are covered by the tests, but it doesn't mentions which tests, which revision and what architecture is covered.

A project to renovate LCOV would involve:

  • Making it run on a buildbot, so that we know what commits / architectures are covered
  • Update the web page to show that information
  • Develop a system that would report every buildbot build into the web page in a searchable database, like LNT

Another idea is to enable the test suite to run all built backends, not only the host architecture, so that coverage report can be built in a fast machine and have one report per commit without needing to update the buildbots.

  1. Completely rewrite bugpoint. In addition to being a mess, bugpoint suffers from a number of problems where it will "lose" a bug when reducing. It should be rewritten from scratch to solve these and other problems.
  2. Add support for transactions to the PassManager for improved bugpoint.
  3. Improve bugpoint to support running tests in parallel on MP machines.
  4. Add MC assembler/disassembler and JIT support to the SPARC port.
  5. Move more optimizations out of the -instcombine pass and into InstructionSimplify. The optimizations that should be moved are those that do not create new instructions, for example turning sub i32 %x, 0 into %x. Many passes use InstructionSimplify to clean up code as they go, so making it smarter can result in improvements all over the place.

Sometimes creating new things is more fun than improving existing things. These projects tend to be more involved and perhaps require more work, but can also be very rewarding.

Many proposed extensions and improvements to LLVM core are awaiting design and implementation.

  1. Improvements for Debug Information Generation
  2. EH support for non-call exceptions
  3. Many ideas for feature requests are stored in LLVM bugzilla. Searchfor bugs with a "new-feature" keyword.

We have a strong base for development of both pointer analysis based optimizations as well as pointer analyses themselves. We want to take advantage of this:

  1. The globals mod/ref pass does an inexpensive bottom-up context sensitive alias analysis. There are some inexpensive things that we could do to better capture the effects of functions that access pointer arguments. This can be really important for C++ methods, which spend lots of time accessing pointers off 'this'.
  2. The alias analysis API supports the getModRefBehavior method, which allows the implementation to give details analysis of the functions. For example, we could implement full knowledge of printf/scanf side effects, which would be useful. This feature is in place but not being used for anything right now.
  3. We need some way to reason about errno. Consider a loop like this:
        for ()
          x += sqrt(loopinvariant);
    

    We'd like to transform this into:

        t = sqrt(loopinvariant);
        for ()
          x += t;
    

    This transformation is safe, because the value of errno isn't otherwise changed in the loop and the exit value of errno from the loop is the same. We currently can't do this, because sqrt clobbers errno, so it isn't "readonly" or "readnone" and we don't have a good way to model this.

    The important part of this project is figuring out how to describe errno in the optimizer: each libc #defines errno to something different it seems. Maybe the solution is to have a __builtin_errno_addr() or something and change sys headers to use it.

  4. There are lots of ways to optimize out and improve handling of memcpy/memset.

We now have a unified infrastructure for writing profile-guided transformations, which will work either at offline-compile-time or in the JIT, but we don't have many transformations. We would welcome new profile-guided transformations as well as improvements to the current profiling system.

Ideas for profile-guided transformations:

  1. Superblock formation (with many optimizations)
  2. Loop unrolling/peeling
  3. Profile directed inlining
  4. Code layout
  5. ...

Improvements to the existing support:

  1. The current block and edge profiling code that gets inserted is very simple and inefficient. Through the use of control-dependence information, many fewer counters could be inserted into the code. Also, if the execution count of a loop is known to be a compile-time or runtime constant, all of the counters in the loop could be avoided.
  2. You could implement one of the "static profiling" algorithms which analyze a piece of code an make educated guesses about the relative execution frequencies of various parts of the code.
  3. You could add path profiling support, or adapt the existing LLVM path profiling code to work with the generic profiling interfaces.

LLVM aggressively optimizes for performance, but does not yet optimize for code size. With a new ARM backend, there is increasing interest in using LLVM for embedded systems where code size is more of an issue.

Someone interested in working on implementing code compaction in LLVM might want to read this article, describing using link-time optimizations for code size optimization.

  1. Implement a Loop Dependence Analysis Infrastructure
    - Design some way to represent and query dep analysis
  2. Value range propagation pass
  3. More fun with loops: Predictive Commoning
  4. Type inference (aka. devirtualization)
  5. Value assertions (also PR810).
  1. Generalize target-specific backend passes that could be target-independent, by adding necessary target hooks and making sure all IR/MI features (such as register masks and predicated instructions) are properly handled. Enable these for other targets where doing so is demonstrably beneficial. For example:
    1. lib/Target/Hexagon/RDF*
    2. lib/Target/AArch64/AArch64AddressTypePromotion.cpp
  2. Merge the delay slot filling logic that is duplicated into (at least) the Sparc and Mips backends into a single target independent pass. Likewise, the branch shortening logic in several targets should be merged together into one pass.
  3. Implement 'stack slot coloring' to allocate two frame indexes to the same stack offset if their live ranges don't overlap. This can reuse a bunch of analysis machinery from LiveIntervals. Making the stack smaller is good for cache use and very important on targets where loads have limited displacement like ppc, thumb, mips, sparc, etc. This should be done as a pass before prolog epilog insertion. This is now done for register allocator temporaries, but not for allocas.
  4. Implement 'shrink wrapping', which is the intelligent placement of callee saved register save/restores. Right now PrologEpilogInsertion always saves every (modified) callee save reg in the prolog and restores it in the epilog, however, some paths through a function (e.g. an early exit) may not use all regs. Sinking the save down the CFG avoids useless work on these paths. Work has started on this, please inquire on llvm-dev.
  5. Implement interprocedural register allocation. The CallGraphSCCPass can be used to implement a bottom-up analysis that will determine the *actual* registers clobbered by a function. Use the pass to fine tune register usage in callers based on *actual* registers used by the callee.
  6. Add support for 16-bit x86 assembly and real mode to the assembler and disassembler, for use by BIOS code. This includes both 16-bit instruction encodings as well as privileged instructions (lgdt, lldt, ltr, lmsw, clts, invd, invlpg, wbinvd, hlt, rdmsr, wrmsr, rdpmc, rdtsc) and the control and debug registers.
  1. Port the Bigloo Scheme compiler, from Manuel Serrano at INRIA Sophia-Antipolis, to output LLVM bytecode. It seems that it can already output .NET bytecode, JVM bytecode, and C, so LLVM would ostensibly be another good candidate.
  2. Write a new frontend for some other language (Java? OCaml? Forth?)
  3. Random test vector generator: Use a C grammar to generate random C code, e.g., quest; run it through llvm-gcc, then run a random set of passes on it using opt. Try to crash opt. When opt crashes, use bugpoint to reduce the test case and post it to a website or mailing list. Repeat ad infinitum.
  4. Add sandbox features to the Interpreter: catch invalid memory accesses, potentially unsafe operations (access via arbitrary memory pointer) etc.
  5. Port Valgrind to use LLVM code generation and optimization passes instead of its own.
  6. Write LLVM IR level debugger (extend Interpreter?)
  7. Write an LLVM Superoptimizer. It would be interesting to take ideas from this superoptimizer for x86: paper #1 and paper #2 and adapt them to run on LLVM code.

    It would seem that operating on LLVM code would save a lot of time because its semantics are much simpler than x86. The cost of operating on LLVM is that target-specific tricks would be missed.

    The outcome would be a new LLVM pass that subsumes at least the instruction combiner, and probably a few other passes as well. Benefits would include not missing cases missed by the current combiner and also more easily adapting to changes in the LLVM IR.

    All previous superoptimizers have worked on linear sequences of code. It would seem much better to operate on small subgraphs of the program dependency graph.

In addition to projects that enhance the existing LLVM infrastructure, there are projects that improve software that uses, but is not included with, the LLVM compiler infrastructure. These projects include open-source software projects and research projects that use LLVM. Like projects that enhance the core LLVM infrastructure, these projects are often challenging and rewarding.

At least one project (and probably more) needs to use analysis information (such as call graph analysis) from within a MachineFunctionPass, however, most analysis passes operate at the LLVM IR level. In some cases, a value (e.g., a function pointer) cannot be mapped from the MachineInstr level back to the LLVM IR level reliably, making the use of existing LLVM analysis passes from within a MachineFunctionPass impossible (or at least brittle).

This project is to encode analysis information from the LLVM IR level into the MachineInstr IR when it is generated so that it is available to a MachineFunctionPass. The exemplar is call graph analysis (useful for control-flow integrity instrumentation, analysis of code reuse defenses, and gadget compilers); however, other LLVM analyses may be useful.

Implement an on-demand function relocator in the LLVM JIT. This can help improve code locality using runtime profiling information. The idea is to use a relocation table for every function. The relocation entries need to be updated upon every function relocation (take a look at this article). A (per-function) basic block reordering would be a useful extension.

The goal of this project is to implement better data layout optimizations using the model of reference affinity. This paper provides some background information.

Slimmer is a prototype tool, built using LLVM, that uses dynamic analysis to find potential performance bugs in programs. Development on Slimmer started during Google Summer of Code in 2015 and resulted in an initial prototype, but evaluation of the prototype and improvements to make it portable and robust are still needed. This project would have a student pick up and finish the Slimmer work. The source code of Slimmer and its current documentation can be found at its Github web page.


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