Summary Fortran Performance Optimization and Auto-Parallelisation with MLIR-Based Abstractions arxiv.org
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MLIR optimizes Fortran code by extracting stencils and adding them to the intermediate code, improving performance and enabling auto-parallelization on GPUs without altering the source code.
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Key Points
- MLIR (Multi-Level Intermediate Representation) is a flexible framework that allows for the representation of intermediate code at different abstraction levels, enabling automated program optimization and parallelization.
- The authors combine the Flang MLIR general-purpose compiler with the Open Earth Compiler's MLIR stencil dialect to optimize Fortran code.
- Transformations are developed to extract stencils from Fortran code and demonstrate a significant improvement in performance compared to using Flang alone.
- The approach enables auto-parallelization targeting multi-threaded and distributed memory parallelism, as well as optimized execution on GPUs without modifying the original Fortran source code.
- MLIR provides flexibility in representing intermediate code and enables transformations between different dialects, which can be leveraged for automated program optimization and parallelization.
- The authors highlight the potential of MLIR to enrich existing programming languages and development by leveraging domain-specific information and transformations.
- Future work includes improving single CPU performance, enhancing distributed memory parallelism, and exploring integration with other MLIR-based compilers.
Summaries
35 word summary
MLIR is used to optimize Fortran code by extracting stencils and adding them to the intermediate code. This improves performance and enables auto-parallelization on GPUs without modifying the source code. Future research directions are discussed.
66 word summary
This paper explores using MLIR to optimize Fortran code with domain-specific abstractions. By combining Flang MLIR compiler with Open Earth Compiler's MLIR stencil dialect, the authors extract stencils from Fortran code and add them to the intermediate code. This approach significantly improves performance, enables auto-parallelization, and optimized execution on GPUs without modifying the source code. The paper concludes by summarizing contributions and discussing future research directions.
110 word summary
This paper explores the use of MLIR to optimize Fortran code by leveraging domain-specific abstractions. MLIR allows for the representation of intermediate code at different levels, enabling program optimization and parallelization. The authors combine the Flang MLIR compiler with the Open Earth Compiler's MLIR stencil dialect, developing transformations to extract stencils from Fortran code. These stencils are added to the intermediate code, resulting in a significant performance improvement compared to using Flang alone. The approach also enables auto-parallelization and optimized execution on GPUs without modifying the original source code. The paper concludes by summarizing its contributions, highlighting the potential of MLIR to enrich existing compilers, and discussing future research directions.
401 word summary
This paper explores the use of MLIR (Multi-Level Intermediate Representation) to optimize Fortran code by leveraging domain-specific abstractions. MLIR is a flexible framework that allows for the representation of intermediate code at different abstraction levels, enabling automated program optimization and parallelization. The authors focus on combining the Flang MLIR general-purpose compiler with the Open Earth Compiler's MLIR stencil dialect. They develop transformations to extract stencils from Fortran code and demonstrate a significant improvement in performance compared to using Flang alone. The approach also enables auto-parallelization targeting multi-threaded and distributed memory parallelism, as well as optimized execution on GPUs without modifying the original Fortran source code.
The paper begins by introducing MLIR and its popularity since its open-source release in 2019. MLIR provides flexibility in representing intermediate code and enables transformations between different dialects, which can be leveraged for automated program optimization and parallelization. The authors highlight the use of MLIR in general-purpose compilers like Flang, but note that these compilers often do not fully exploit domain-specific abstractions found in some MLIR dialects.
The authors propose combining the Flang MLIR compiler with the Open Earth Compiler's MLIR stencil dialect. They develop transformations to identify and extract stencils from Fortran code, which are then specialized using the stencil dialect. The extracted stencils are added to the intermediate code, leveraging existing MLIR transformations to target different architectures and forms of parallelism. The authors demonstrate a 2-10 times performance improvement for their benchmarks compared to using Flang alone on a Cray supercomputer. They also show that the approach enables auto-parallelization and optimized execution on GPUs without modifying the original Fortran source code.
The paper concludes by summarizing its contributions, including improved performance compared to Flang alone, the ability to target multiple HPC architectures and forms of parallelism, and the benefits of using MLIR dialects to complement general-purpose compilers. The authors highlight the potential of MLIR to enrich existing programming languages and development by leveraging domain-specific information and transformations. They also discuss future work, such as improving single CPU performance, enhancing distributed memory parallelism, and exploring integration with other MLIR-based compilers.
In summary, this paper presents a novel approach to optimizing Fortran code by leveraging MLIR-based domain-specific abstractions. The authors demonstrate improved performance and the ability to target different architectures and forms of parallelism without modifying the original source code. The paper highlights the potential of MLIR in enhancing existing compilers and discusses future research directions.