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Open LLVM Projects
Welcome prospective Google Summer of Code 2016 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 just 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. However, you must propose your idea to the LLVM community through our developers' mailing list (firstname.lastname@example.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.
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 simply won't work.
Of course, 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:
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 simply 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:
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:
Another idea is to enable the test suite to run all built backends, not just 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.
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.
We have a strong base for development of both pointer analysis based optimizations as well as pointer analyses themselves. It seems natural to want to take advantage of this:
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:
Improvements to the existing support:
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.
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.
Several projects reuse LLVM's code generator infrastructure to analyze and optimize the machine code that the compiler generates. However, LLVM's code generator infrastructure does not provide a pass which allows inter-procedural analysis and optimization of the native code that the LLVM compiler generates. What is needed is an equivalent of the LLVM IR ModulePass (i.e., a MachineModulePass) that permits inter-procedural analysis and optimiztion of machine instructions (MachineIntr's).
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.
LLVM Compiler Infrastructure