Stealing from Biologists to Compile Haskell Faster

Developers working on the Glasgow Haskell Compiler (GHC) have integrated a biological RNA folding algorithm to optimize code compilation, significantly reducing compile times under certain conditions.

The discovery originated from examining the limitations of GHC’s ApplicativeDo optimization flag, which is disabled by default due to its slow performance with the current algorithm. Researchers realized that the core problem—finding the optimal grouping of independent statements—closely resembles the RNA folding problem biologists have studied extensively. By adapting this biological algorithm, the compiler can now schedule independent computations more efficiently, reducing the number of required rounds and improving compile times.

Initially, the problem involved a computationally expensive O(n³) algorithm that could take over 55 seconds to process large code blocks. The new approach simplifies the problem to a dependency graph, where statements are nodes, and dependencies are edges. The algorithm then finds the minimal number of rounds needed to execute all statements, akin to RNA strand folding predictions, which also involve minimizing energy states through dynamic programming. This cross-disciplinary method enables the compiler to batch independent tasks more effectively, cutting down the number of network round-trips during compilation.

Why It Matters

This development matters because it demonstrates how biological algorithms can be repurposed to improve computer language compilers, potentially leading to faster build times for large Haskell projects. It also highlights a novel intersection between biology and software engineering, where insights from one field can solve longstanding technical challenges in another. For developers and users of GHC, this means more efficient compilation, especially for complex codebases with many independent operations, enhancing productivity and reducing wait times.

Background

The issue with ApplicativeDo arose because the default greedy algorithm for scheduling independent statements often results in suboptimal batching, creating unnecessary rounds and latency. The optimal algorithm, which considers all possible groupings, was previously too slow for practical use, especially with large code blocks. The discovery of the RNA folding algorithm as an analogy provided a new perspective, enabling the development of a more efficient method that approximates the optimal solution without incurring the high computational cost. This approach builds on prior research into dynamic programming for dependency resolution and has been under consideration for some time, but recent efforts have brought it to the forefront.

“Leveraging an RNA folding algorithm from biology allows us to schedule independent computations more efficiently, drastically reducing compile times.”

— Haskell compiler developer

“The problem of predicting RNA structure is fundamentally about minimizing energy states through dynamic programming, which turns out to be very similar to optimizing statement dependencies in a compiler.”

— Biologist specializing in RNA folding

What Remains Unclear

It is not yet clear how widely applicable this biological algorithm can be across different compiler optimizations or whether further refinements are needed for large-scale codebases. The long-term stability and integration into production GHC versions are still under testing.

What’s Next

Next steps include optimizing the implementation for broader use, integrating it into official GHC releases, and evaluating performance gains across diverse Haskell projects. Developers are also exploring whether similar biological algorithms can address other compiler bottlenecks.

Key Questions

How does the RNA folding algorithm improve Haskell compilation?

The algorithm helps schedule independent computations more efficiently, reducing the number of rounds needed and decreasing overall compile times.

Is this approach specific to Haskell or applicable elsewhere?

While currently applied to GHC’s ApplicativeDo, the underlying principles could be adapted to other compilers or systems involving dependency scheduling.

Will this change be included in the next GHC release?

It is under active development and testing; inclusion in upcoming releases depends on further validation and performance assessments.

Are there any risks or downsides to using the biological algorithm?

Potentially, the approximation may not always yield the absolute optimal schedule, but initial results suggest significant improvements with manageable trade-offs.

Source: Hacker News