Stealing from Biologists to Compile Haskell Faster

GHC developers have integrated an algorithm borrowed from RNA folding prediction techniques used by biologists to optimize the scheduling of independent computations, potentially enabling faster compilation times for Haskell programs.

The issue originated from the default implementation of ApplicativeDo in GHC, which is slow due to the complexity of finding the optimal grouping of independent statements for parallel execution. The existing greedy algorithm often results in more rounds of network calls, increasing latency.

Researchers and developers identified that the problem of scheduling independent computations can be modeled similarly to the RNA folding problem, where biologists predict the structure of RNA strands by minimizing energy states through dynamic programming. This insight led to adopting a similar algorithmic approach to optimize the ordering of independent computations in GHC.

While the original optimal algorithm for this scheduling problem had a cubic time complexity (O(n³)), making it impractical for large code blocks, recent work has simplified the process. By focusing only on the first and last statements in tangled groups, the new method reduces the complexity to O(n²), enabling near-optimal scheduling in reasonable time.

The practical impact is a potential reduction in the number of rounds needed to execute independent fetches, which can cut down overall compilation and runtime latency, especially in data-heavy applications like biostatistics and large-scale data processing.

Why It Matters

This development is significant because it demonstrates a successful cross-disciplinary application of algorithms, where biology-inspired methods improve programming language compiler performance. Faster compilation and execution can benefit developers working on large, complex Haskell projects, especially those involving data-intensive computations.

Moreover, this approach highlights the potential for further innovations by exploring algorithms from other scientific fields, fostering collaboration across disciplines to solve computational problems more efficiently.

Background

The challenge originated from the implementation of ApplicativeDo in GHC, which allows writing code with less boilerplate but can be slow when optimizing for minimal execution rounds. Previous solutions relied on greedy algorithms that often resulted in suboptimal scheduling, especially in large code blocks with many dependencies.

Biologists have long used dynamic programming algorithms to predict RNA structures by minimizing energy states, which involves similar dependency resolution problems. Recognizing this similarity opened the door to applying these algorithms to compiler optimization, an approach that had not been previously considered in this context.

The original paper describing the optimal scheduling algorithm for ApplicativeDo demonstrated that, while theoretically faster, the cubic complexity made it impractical for large code blocks, leading to its limited use. The recent breakthrough simplifies the process, making it feasible for real-world applications.

“Leveraging RNA folding algorithms for code scheduling is an unexpected but promising approach that could significantly speed up compilation times.”

— Haskell compiler developer

“The algorithms we use to predict RNA structures are surprisingly adaptable to solving dependency scheduling problems in programming languages.”

— Biology researcher involved in RNA folding

What Remains Unclear

It is not yet clear how widely this new approach will be adopted in GHC or other compilers, and whether it will consistently outperform existing heuristics across all code bases. Further testing and optimization are ongoing.

What’s Next

Developers plan to integrate the O(n²) algorithm into the main GHC branch and evaluate its performance across various large-scale Haskell projects. Additional research may explore further biological algorithms for other compiler optimization challenges.

Key Questions

How does the RNA folding algorithm improve GHC’s performance?

The algorithm finds near-optimal groupings of independent computations, reducing the number of execution rounds and thus lowering latency during compilation and runtime.

Is this approach applicable to other programming languages?

Potentially, yes. The dependency scheduling problem exists in many languages, and biological algorithms could be adapted for similar optimization tasks.

Will this change affect the correctness of GHC’s generated code?

No. The algorithm ensures the correctness of dependency resolution while optimizing the execution order for speed.

Source: Hacker News