Introduction to Compiler Parallelizability

The question of how parallelizable are compilers EX LLVM is a critical consideration in compiler design and optimization. This article explores the various aspects of compiler parallelizability, focusing on the BUILD process, common compilation units, and the broader implications for compiler optimizations and performance.

Understanding Compilation Units and the BUILD Process

The BUILD process, which often involves thousands of compilation units or source code files, is distinct from parallelizing the compilation of a single unit. While the BUILD process can benefit from parallel execution, the individual compilation units themselves are usually on the human scale, rarely exceeding a few thousand lines of code. Parallelizing such small units is not always worth the overhead of scheduling and management.

Special Cases for Large Compilation Units

Certain development models, such as those involving automatic code generation, can produce compilation units of tens of millions of lines of code. In these cases, parallelization becomes more worthwhile, especially in contexts like code optimization and code generation. Techniques like C templates, operator overloading, and function overloading offer opportunities for parallel execution, particularly for removing unnecessary code. However, even in these cases, the compilation unit size remains relatively small in terms of core utilization.

Test Cases and Experiences

My experience with automatically generated compilation units has been limited to situations where the original problem was not fully understood or resources were insufficient to write a correct compiler. These cases also involved experimental approaches to problem solving. Therefore, while parallelization in these scenarios can be beneficial, it is not a standard practice due to the complexity and specialized nature of the problems at hand.

Indisputable Factors for Parallelization

Highlighted in the de facto standard compiler toolchains, much of the work done by a compiler is inherently parallelizable. Parsing, while not parallelizable within a file, can be parallelized across files once the initial parsing is complete. This leads to a multitude of tasks that can be worked on independently, yielding significant benefits in terms of execution speed. Traditional compiler toolchains often face bottlenecks due to IO, particularly with spinning disks, which are a significant limiting factor.

Optimizing IO and Memory Utilization

By excluding debug information and utilizing incremental compilation with an in-memory system, the IO component can be largely avoided. Modern workstations have vast amounts of RAM, making this approach feasible. Modern compilers often output smaller or comparable-sized object files, reducing the impact of IO.

Whole Program and Profile-Based Optimizations

Whole program optimizations and profile-based optimizations, while critical, are generally not parallelizable or are only marginally so. Linking, on the other hand, is mostly parallelizable, but transitive graph closures, if necessary, may not be sufficiently parallelizable to be beneficial. In cases where parallelization is feasible, IO-related issues can dominate the actual performance gains.

Conclusion and Future Directions

In conclusion, while compilers are highly parallelizable in many aspects, the level of parallelizability varies widely depending on the specific context. Automatic code generation and specific development models can indeed benefit from parallelization for large compilation units, but the standard human-scale compilation units do not require such extensive parallelization due to their size and structure. As technologies evolve, the focus should remain on optimizing the IO bottleneck and developing more efficient parallel compilation techniques.