arXiv subjects

Berke Ates

Publications and source records attributed to Berke Ates.

4 recordsLinked to original sources

arXiv2026

MLIR-Forge: A Modular Framework for Language Smiths

Optimizing compilers are essential for the efficient and correct execution of software across various scientific fields. Domain-specific languages (DSL) typically use higher level intermediate representations (IR) in their compiler pipelines for domain-specific optimizations. As these IRs add to complexity, it is crucial to test them thoroughly. Random program generators have proven to be an effective tool to test compilers through differential and fuzz testing. However, developing specialized program generators for compiler IRs is not straightforward and demands considerable resources. We introduce MLIR-Forge, a novel random program generator framework that leverages the flexibility of MLIR, aiming to simplify the creation of specialized program generators. MLIR-Forge achieves this by splitting the generation process into fundamental building blocks that are language specific, and reusable program creation logic that constructs random programs from these building blocks. This hides complexity and furthermore, even the language specific components can be defined using a set of common tools. We demonstrate MLIR-Forge's capabilities by generating MLIR with built-in dialects, WebAssembly, and a data-centric program representation, DaCe -- requiring less than a week of development time in total for each of them. Using the generated programs we conduct differential testing and find 9 MLIR, 15 WebAssembly, and 774 DaCe groups of bugs with the corresponding program generators, after running them until the rate of new bugs stagnates.

cs.PL↗

arXiv2026

MLIR-Smith: A Novel Random Program Generator for Evaluating Compiler Pipelines

Compilers are essential for the performance and correct execution of software and hold universal relevance across various scientific disciplines. Despite this, there is a notable lack of tools for testing and evaluating them, especially within the adaptable Multi-Level Intermediate Representation (MLIR) context. This paper addresses the need for a tool that can accommodate MLIR's extensibility, a feature not provided by previous methods such as Csmith. Here we introduce MLIR-Smith, a novel random program generator specifically designed to test and evaluate MLIR-based compiler optimizations. We demonstrate the utility of MLIR-Smith by conducting differential testing on MLIR, LLVM, DaCe, and DCIR, which led to the discovery of multiple bugs in these compiler pipelines. The introduction of MLIR-Smith not only fills a void in the realm of compiler testing but also emphasizes the importance of comprehensive testing within these systems. By providing a tool that can generate random MLIR programs, this paper enhances our ability to evaluate and improve compilers and paves the way for future tools, potentially shaping the wider landscape of software testing and quality assurance.

cs.PL↗

arXiv2024

A Priori Loop Nest Normalization: Automatic Loop Scheduling in Complex Applications

The same computations are often expressed differently across software projects and programming languages. In particular, how computations involving loops are expressed varies due to the many possibilities to permute and compose loops. Since each variant may have unique performance properties, automatic approaches to loop scheduling must support many different optimization recipes. In this paper, we propose a priori loop nest normalization to align loop nests and reduce the variation before the optimization. Specifically, we define and apply normalization criteria, mapping loop nests with different memory access patterns to the same canonical form. Since the memory access pattern is susceptible to loop variations and critical for performance, this normalization allows many loop nests to be optimized by the same optimization recipe. To evaluate our approach, we apply the normalization with optimizations designed for only the canonical form, improving the performance of many different loop nest variants. Across multiple implementations of 15 benchmarks using different languages, we outperform a baseline compiler in C on average by a factor of $21.13$, state-of-the-art auto-schedulers such as Polly and the Tiramisu auto-scheduler by $2.31$ and $2.89$, as well as performance-oriented Python-based frameworks such as NumPy, Numba, and DaCe by $9.04$, $3.92$, and $1.47$. Furthermore, we apply the concept to the CLOUDSC cloud microphysics scheme, an actively used component of the Integrated Forecasting System, achieving a 10% speedup over the highly-tuned Fortran code.

cs.PF↗

arXiv2023

Bridging Control-Centric and Data-Centric Optimization

With the rise of specialized hardware and new programming languages, code optimization has shifted its focus towards promoting data locality. Most production-grade compilers adopt a control-centric mindset - instruction-driven optimization augmented with scalar-based dataflow - whereas other approaches provide domain-specific and general purpose data movement minimization, which can miss important control-flow optimizations. As the two representations are not commutable, users must choose one over the other. In this paper, we explore how both control- and data-centric approaches can work in tandem via the Multi-Level Intermediate Representation (MLIR) framework. Through a combination of an MLIR dialect and specialized passes, we recover parametric, symbolic dataflow that can be optimized within the DaCe framework. We combine the two views into a single pipeline, called DCIR, showing that it is strictly more powerful than either view. On several benchmarks and a real-world application in C, we show that our proposed pipeline consistently outperforms MLIR and automatically uncovers new optimization opportunities with no additional effort.

cs.PL↗