I have had the code on github for quite some time, so it seems like I should say something about my LLVM program analysis tools. The primary repository is llvm-analysis, which provides a Haskell interface for analyzing the LLVM IR. The LLVM IR is a high-level assembly language for a virtual machine with infinite registers. This is a virtual machine as in a piece of hardware that does not exist rather than a JVM-style virtual machine that programs run on. LLVM IR is converted directly to machine code once a target architecture is chosen. The LLVM IR is a convenient analysis target when the details of a programming language’s AST are not required. The IR has a relatively modest ~50 instructions and is fairly similar to a RISC architecture (only three our four instructions actually touch memory), so reasoning about it is simple. Once you compile your source file to the LLVM IR (persisted in a bitcode file), you can load it and analyze the instruction stream.
LLVM itself is a C++ library, and writing a program analysis in C++ is unpleasant to say the least. I wanted to be able to use a rich functional language and I chose Haskell. LLVM has an official C interface, llvm-c, which provides access to many LLVM facilities to other languages. Bryan O’Sullivan created some excellent LLVM bindings to this C library. Unfortunately, the C interface is really only suitable for generating code. Analyzing existing bitcode does not really seem to be possible through the C interface (and thus through Bryan’s library).
If the C interface does not expose enough information to inspect existing bitcode, what can we do? I chose to write a layer to translate the IR from its C++ representation directly to a Haskell data type. The resulting data type is pure and can be analyzed with normal Haskell pattern matching facilities. This was slightly tricky because the object graph of the LLVM IR can contain cycles (due to phi nodes and a few other constructs). Translating this into a Haskell data type without something like IORefs to break the cycles required tying the knot. For the interested, the code to perform this translation lives in my llvm-data-interop library; it relies on a bit of C++ code to convert the C++ LLVM IR into a FFI-friendly format (using plain C structs instead of C++ classes). The Haskell portion of the library ties the knot while translating the C structures into their Haskell equivalent. This library simply exposes the LLVM IR as a simple Haskell data type.
The main library is llvm-analysis, which provides some higher level program analysis infrastructure:
- An implementation of Andersen’s points-to analysis
- Control flow graphs
- Call graphs
- Control dependence graphs
- Dominator and postdominator trees
- A dataflow analysis framework
- A simple framework for analyzing call graphs in bottom-up strongly-connected component order
- A class hierarchy analysis for C++
I use all of this daily - hopefully someone else might find it useful one day, too. I have not put this code on hackage yet because the API is still unstable and there are a few big issues that I would still like to address.
On-demand Metadata Parsing
Currently, all metadata is translated and loaded into memory eagerly. This is fine for most programs that you might want to analyze, but it makes analyzing some larger programs impossible due to memory bloat. Notably, eagerly loading all of the metadata for something the size of Firefox is completely infeasible - it takes more than 32GB of RAM with the Haskell representation of the IR (which is much less compact than the C++ equivalent).
The solution will be to just load metadata on-demand. This will require a bit of unsafePerformIO magic. It will result in redundant translation work, but most uses of metadata only require a tiny fraction of it, so it should not be a problem. Perhaps on-demand versus eager loading could be turned into a parse-time option.
I am still modifying the API as I use it more and discover things that are missing or that are just inconvenient. As my Haskell-fu improves, I sometimes also discover more efficient or safer ways to handle things that sometimes require minor API changes. This sort of change has been becoming rarer, however.
There is also some instability inherent to following LLVM versions - the LLVM IR itself changes and a high-level library like this can only mask so much of that. That said, this library can mask many of the very annoying changes that land in LLVM (like the various string types that seem to come and go from the LLVM codebase).
Another ongoing issue has been my struggle to build a scalable graph library. I started off using fgl, but ran into issues with large graphs. Some of these issues have been fixed by just making smaller graphs, but occasionally it cannot be avoided. Traversing a large fgl graph can become a bottleneck if the graph is highly-connected (which can be common if you are taking the transitive closure of a graph).
I have been playing with alternative implementations of purely functional graphs, but the improvements I have seen are not quite what I was hoping for (though some have been significant). Some of my experiments can be seen in my hbgl library.