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The Design of LLVM


LLVM Target Description Files

The "mix and match" approach allows target authors to choose what makes sense for their architecture and permits a large amount of code reuse across different targets. This brings up another challenge: each shared component needs to be able to reason about target specific properties in a generic way. For example, a shared register allocator needs to know the register file of each target and the constraints that exist between instructions and their register operands. LLVM's solution to this is for each target to provide a target description in a declarative domain-specific language (a set of .td files) processed by the tblgen tool. The (simplified) build process for the x86 target is shown in Figure 5.



Figure 5: Simplified x86 target definition.

The different subsystems supported by the .td files allow target authors to build up the different pieces of their target. For example, the x86 back end defines a register class that holds all of its 32-bit registers named GR32 (in the .td files, target specific definitions are all caps) like this:

def GR32 : RegisterClass<[i32], 32,
  [EAX, ECX, EDX, ESI, EDI, EBX, EBP, ESP,
   R8D, R9D, R10D, R11D, R14D, R15D, R12D, R13D]> { … }

This definition says that registers in this class can hold 32-bit integer values (i32), prefer to be 32-bit aligned, have the specified 16 registers (which are defined elsewhere in the .td files) and have some more information to specify preferred allocation order and other things. Given this definition, specific instructions can refer to this, using it as an operand. For example, the "complement a 32-bit register" instruction is defined as:

let Constraints = "$src = $dst" in
def NOT32r : I<0xF7, MRM2r,
               (outs GR32:$dst), (ins GR32:$src),
               "not{l}\t$dst",
               [(set GR32:$dst, (not GR32:$src))]>;

This definition says that NOT32r is an instruction (it uses the I tblgen class), specifies encoding information (0xF7, MRM2r), specifies that it defines an "output" 32-bit register $dst and has a 32-bit register "input" named $src (the GR32 register class defined above defines which registers are valid for the operand), specifies the assembly syntax for the instruction (using the {} syntax to handle both AT&T and Intel syntax), specifies the effect of the instruction and provides the pattern that it should match on the last line. The let constraint on the first line tells the register allocator that the input and output register must be allocated to the same physical register.

This definition is a very dense description of the instruction, and the common LLVM code can do a lot with information derived from it (by the tblgen tool). This one definition is enough for instruction selection to form this instruction by pattern matching on the input IR code for the compiler. It also tells the register allocator how to process it, is enough to encode and decode the instruction to machine code bytes, and is enough to parse and print the instruction in a textual form. These capabilities allow the x86 target to support generating a standalone x86 assembler (which is a drop-in replacement for the "gas" GNU assembler) and disassemblers from the target description as well as handle encoding the instruction for the JIT.

In addition to providing useful functionality, having multiple pieces of information generated from the same "truth" is good for other reasons. This approach makes it almost infeasible for the assembler and disassembler to disagree with each other in either assembly syntax or in the binary encoding. It also makes the target description easily testable: instruction encodings can be unit tested without having to involve the entire code generator.

While we aim to get as much target information as possible into the .td files in a nice declarative form, we still don't have everything. Instead, we require target authors to write some C++ code for various support routines and to implement any target specific passes they might need (like X86FloatingPoint.cpp, which handles the x87 floating point stack). As LLVM continues to grow new targets, it becomes more and more important to increase the amount of the target that can be expressed in the .td file, and we continue to increase the expressiveness of the .td files to handle this. A great benefit is that it gets easier and easier write targets in LLVM as time goes on.

Interesting Capabilities Provided by a Modular Design

Besides being a generally elegant design, modularity provides clients of the LLVM libraries with several interesting capabilities. These capabilities stem from the fact that LLVM provides functionality, but lets the client decide most of the policies on how to use it.

As mentioned earlier, LLVM IR can be efficiently (de)serialized to/from a binary format known as LLVM bitcode. Since LLVM IR is self-contained, and serialization is a lossless process, we can do part of compilation, save our progress to disk, then continue work at some point in the future. This feature provides a number of interesting capabilities including support for link-time and install-time optimization, both of which delay code generation from "compile time".

Link-Time Optimization (LTO) addresses the problem where the compiler traditionally only sees one translation unit (for example, a .c file with all its headers) at a time and therefore cannot do optimizations (like inlining) across file boundaries. LLVM compilers like Clang support this with the -flto or -O4 command line option. This option instructs the compiler to emit LLVM bitcode to the .o file instead of writing out a native object file, and delays code generation to link time, shown in Figure 6.



Figure 6: LTO.

Details differ depending on which operating system you're on, but the important bit is that the linker detects that it has LLVM bitcode in the .o files instead of native object files. When it sees this, it reads all the bitcode files into memory, links them together, then runs the LLVM optimizer over the aggregate. Since the optimizer can now see across a much larger portion of the code, it can inline, propagate constants, do more aggressive dead code elimination, and more across file boundaries. While many modern compilers support LTO, most of them (for example, GCC, Open64, the Intel compiler, etc.) do so by having an expensive and slow serialization process. In LLVM, LTO falls out naturally from the design of the system, and works across different source languages (unlike many other compilers) because the IR is truly source language neutral.

Install-time optimization is the idea of delaying code generation even later than link time, all the way to install time, as shown in Figure 7. Install time is a very interesting time (in cases when software is shipped in a box, downloaded, uploaded to a mobile device, etc.), because this is when you find out the specifics of the device you're targeting. In the x86 family for example, there are broad variety of chips and characteristics. By delaying instruction choice, scheduling, and other aspects of code generation, you can pick the best answers for the specific hardware an application ends up running on.



Figure 7: Install-Time Optimization

Compilers are very complicated, and quality is important, therefore testing is critical. For example, after fixing a bug that caused a crash in an optimizer, a regression test should be added to make sure it doesn't happen again. The traditional approach to testing this is to write a .c file (for example) that is run through the compiler, and to have a test harness that verifies that the compiler doesn't crash. This is the approach used by the GCC test suite, for example.

The problem with this approach is that the compiler consists of many different subsystems and even many different passes in the optimizer, all of which have the opportunity to change what the input code looks like by the time it gets to the previously buggy code in question. If something changes in the front end or an earlier optimizer, a test case can easily fail to test what it is supposed to be testing.

By using the textual form of LLVM IR with the modular optimizer, the LLVM test suite has highly focused regression tests that can load LLVM IR from disk, run it through exactly one optimization pass, and verify the expected behavior. Beyond crashing, a more complicated behavioral test wants to verify that an optimization is actually performed. Here is a simple test case that checks to see that the constant propagation pass is working with add instructions:

 ; RUN: opt < %s -constprop -S | FileCheck %s
define i32 @test() {
  %A = add i32 4, 5
  ret i32 %A
  ; CHECK: @test()
  ; CHECK: ret i32 9
}

The RUN line specifies the command to execute: In this case, the opt and FileCheck command line tools. The opt program is a simple wrapper around the LLVM pass manager, which links in all the standard passes (and can dynamically load plugins containing other passes) and exposes them through to the command line. The FileCheck tool verifies that its standard input matches a series of CHECK directives. In this case, this simple test is verifying that the constprop pass is folding the add of 4 and 5 into 9.

While this might seem like a really trivial example, this is very difficult to test by writing .c files: front ends often do constant folding as they parse, so it is very difficult and fragile to write code that makes its way downstream to a constant folding optimization pass. Because we can load LLVM IR as text and send it through the specific optimization pass we're interested in, then dump out the result as another text file, it is really straightforward to test exactly what we want, both for regression and feature tests.

When a bug is found in a compiler or other client of the LLVM libraries, the first step to fixing it is to get a test case that reproduces the problem. Once you have a test case, it is best to minimize it to the smallest example that reproduces the problem, and also narrow it down to the part of LLVM where the problem happens, such as the optimization pass at fault. While you eventually learn how to do this, the process is tedious, manual, and particularly painful for cases where the compiler generates incorrect code but does not crash.

The LLVM BugPoint tool uses the IR serialization and modular design of LLVM to automate this process. For example, given an input .ll or .bc file along with a list of optimization passes that causes an optimizer crash, BugPoint reduces the input to a small test case and determines which optimizer is at fault. It then outputs the reduced test case and the opt command used to reproduce the failure. It finds this by using techniques similar to "delta debugging" to reduce the input and the optimizer pass list. Because it knows the structure of LLVM IR, BugPoint does not waste time generating invalid IR to input to the optimizer, unlike the standard "delta" command line tool.

In the more complex case of a miscompilation, you can specify the input, code generator information, the command line to pass to the executable, and a reference output. BugPoint will first determine if the problem is due to an optimizer or a code generator, and will then repeatedly partition the test case into two pieces: one that is sent into the "known good" component and one that is sent into the "known buggy" component. By iteratively moving more and more code out of the partition that is sent into the known buggy code generator, it reduces the test case.

BugPoint is a very simple tool and has saved countless hours of test case reduction throughout the life of LLVM. No other open source compiler has a similarly powerful tool, because it relies on a well-defined intermediate representation. That said, BugPoint isn't perfect, and would benefit from a rewrite. It dates back to 2002, and is typically only improved when someone has a really tricky bug to track down that the existing tool doesn't handle well. It has grown over time, accreting new features (such as JIT debugging) without a consistent design or owner.

LLVM's modularity wasn't originally designed to directly achieve any of the goals described here. It was a self-defense mechanism: it was obvious that we wouldn't get everything right on the first try. The modular pass pipeline, for example, exists to make it easier to isolate passes so that they can be discarded after being replaced by better implementations (I often say that none of the subsystems in LLVM are really good until they have been rewritten at least once).

Another major aspect of LLVM remaining nimble (and a controversial topic with clients of the libraries) is our willingness to reconsider previous decisions and make widespread changes to APIs without worrying about backwards compatibility. Invasive changes to LLVM IR itself, for example, require updating all of the optimization passes and cause substantial churn to the C++ APIs. We've done this on several occasions, and though it causes pain for clients, it is the right thing to do to maintain rapid forward progress. To make life easier for external clients (and to support bindings for other languages), we provide C wrappers for many popular APIs (which are intended to be extremely stable) and new versions of LLVM aim to continue reading old .ll and .bc files.

Looking forward, we would like to continue making LLVM more modular and easier to subset. For example, the code generator is still too monolithic: It isn't currently possible to subset LLVM based on features. For example, if you'd like to use the JIT, but have no need for inline assembly, exception handling, or debug information generation, it should be possible to build the code generator without linking in support for these features. We are also continuously improving the quality of code generated by the optimizer and code generator, adding IR features to better support new language and target constructs, and adding better support for performing high-level language-specific optimizations in LLVM.

The LLVM project continues to grow and improve in numerous ways. It is really exciting to see the number of different ways that LLVM is being used in other projects and how it keeps turning up in surprising new contexts that its designers never even thought about. The new LLDB debugger is a great example of this: it uses the C/C++/Objective-C parsers from Clang to parse expressions, uses the LLVM JIT to translate these into target code, uses the LLVM disassemblers, and uses LLVM targets to handle calling conventions among other things. Being able to reuse this existing code allows people developing debuggers to focus on writing the debugger logic, instead of reimplementing yet another (marginally correct) C++ parser.

Despite its success so far, there is still a lot left to be done, as well as the ever-present risk that LLVM will become less nimble and more calcified as it ages. While there is no magic answer to this problem, I hope that the continued exposure to new problem domains, a willingness to reevaluate previous decisions, and to redesign and throw away code will help. After all, the goal isn't to be perfect, it is to keep getting better over time.


Chris Lattner is the primary author of the LLVM project and related projects, such as the clang compiler. This article was excerpted with permission from Volume 1 of the Architecture of Open Source Applications.


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