Optimizing Layout of Recursive Datatypes with Marmoset

Abstract: While programmers know that the low-level memory representation of data structures can have significant effects on performance, compiler support to optimize the layout of those structures is an under-explored field. Prior work has optimized the layout of individual, non-recursive structures without considering how collections of those objects in linked or recursive data structures are laid out. This work introduces Marmoset, a compiler that optimizes the layouts of algebraic datatypes, with a special focus on producing highly optimized, packed data layouts where recursive structures can be traversed with minimal pointer chasing. Marmoset performs an analysis of how a recursive ADT is used across functions to choose a global layout that promotes simple, strided access for that ADT in memory. It does so by building and solving a constraint system to minimize an abstract cost model, yielding a predicted efficient layout for the ADT. Marmoset then builds on top of Gibbon, a prior compiler for packed, mostly-serial representations, to synthesize optimized ADTs. We show experimentally that Marmoset is able to choose optimal layouts across a series of microbenchmarks and case studies, outperforming both Gibbons baseline approach, as well as MLton, a Standard ML compiler that uses traditional pointer-heavy representations.

• The paper introduces a compiler optimization technique that reorders recursive datatype fields using a field access graph to minimize pointer chasing.
• It formulates the optimization as an integer linear program that balances layout costs to enhance memory traversal efficiency.
• Experimental benchmarks show up to 60x speedups, validating significant performance improvements in recursive data structure handling.

Optimizing Layout of Recursive Datatypes with Marmoset

The paper "Optimizing Layout of Recursive Datatypes with Marmoset" introduces a sophisticated compiler optimization technique aimed at enhancing the memory representation of algebraic datatypes (ADTs), particularly focusing on recursive structures. This optimization is achieved by aligning the layout of such structures with their access patterns as used across program functions.

Introduction and Motivation

Recursive data structures are pervasive in various programming paradigms, ranging from low-level system programming languages like C and Rust to high-level functional languages such as Haskell. Despite their ubiquity, recursive structures are inherently challenging for optimization, especially in terms of memory layout. Traditional representations with pointers induce significant performance overhead due to pointer-chasing, which is suboptimal for modern processors.

Current compiler techniques predominantly focus on optimizing the layout of non-recursive structures, leaving a gap in efficient handling of recursive data structures. The Gibbon compiler made strides by using dense, packed representations to reduce pointer chasing. However, its approach was static, sometimes resulting in inefficient layouts when traversal patterns misaligned with the data layout.

Contributions of the Work

This work builds upon the dense representation principles by introducing Marmoset, a solution that analyzes and optimizes the layout of ADTs based on their actual usage patterns within the program. The paper makes several substantial contributions:

1. Field Access Graph Construction: It introduces a novel analysis that captures the temporal access patterns of ADTs across various functions, summarized in a field access graph. This graph serves as the foundation for optimization by illustrating the order and frequency of field accesses.
2. Cost Model and ILP Formulation: A sophisticated cost model is employed to translate the field access graph into an optimization problem. This problem is then formulated as an integer linear program (ILP), balancing the costs associated with different data layouts to minimize backtracking and pointer chasing.
3. Code Transformation and Layout Synthesis: Marmoset extends Gibbon to generate optimized datatypes and transform the code accordingly. This includes reordering fields within datatypes and modifying code to align with the new layout, thereby ensuring efficient memory traversal.
4. Experimental Validation: Through a series of benchmarks, including microbenchmarks (e.g., linked list length computation and logical expression evaluation) and more complex case studies (e.g., blog management software), the paper demonstrates significant performance improvements. Marmoset's ability to outperform both the baseline Gibbon approach and other traditional compilers like Mlton is empirically validated.

Results and Analysis

The experimental results substantiate the efficacy of Marmoset. For instance, in the linked list benchmark, the optimized layout achieved a speedup of up to 60x compared to the suboptimal baseline layout. Similarly, the logical expression evaluation benchmark and various tree manipulations (e.g., add-one tree traversal, exponentiation, and copy operations) highlighted substantial reductions in execution time when the data layout matched the traversal patterns.

In the blog management case study, Marmoset was tested on three different traversals, each stressing different fields within a blog datatype. The results indicate that Marmoset's optimizations provided speedups of up to 54x over the baseline in certain scenarios, such as when the access patterns favored the new layout.

Implications and Future Work

The practical implications of this research are far-reaching. By automating the optimization of recursive data structures, Marmoset can significantly improve the performance of programs that heavily rely on such structures. This not only enhances runtime efficiency but also alleviates the burden on programmers to manually manage data layout optimizations.

Theoretically, this work opens up new avenues for research in compiler optimizations:

1. Dynamic Profiling Integration: Incorporating dynamic profiling to refine the accuracy of access pattern predictions could further enhance the optimization process.
2. Exploration of Hybrid Approaches: Combining static analysis with runtime adaptations may offer a balanced solution to handle evolving access patterns in long-running applications.
3. Extending to More Complex Structures: Expanding the techniques to support more complex, multi-level recursive structures and mutable data types will broaden the applicability of Marmoset.

In summary, the paper presents a comprehensive and rigorous approach to optimize the layout of recursive ADTs through Marmoset. By aligning data layouts with access patterns, it achieves remarkable performance improvements, setting a new benchmark for compiler optimizations in this domain. Future research inspired by this work promises to push these optimizations even further, empowering both developers and compilers to produce highly efficient, memory-access optimized programs.