Mc2FOR: A tool for automatically translating MATLAB to FORTRAN 95

Li, Xu; Hendren, Laurie

doi:10.1109/csmr-wcre.2014.6747175

Cited by 8 publications

(6 citation statements)

References 7 publications

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“…Recently, Spiral is attempting to generalize the approach towards more generic linear algebra expressions [25], i.e., exactly the subclass of array programs of which this paper is concerned. A lot of effort has been put into compilation of high-level array languages, such as compilation of APL [6,12] and Matlab [19]. Most of these approaches are, in principle, very similar to the work presented here, but the generality of the source language often inhibits a similar effectiveness.…”

Section: Related Workmentioning

confidence: 93%

Abstract expressionism for parallel performance

Bernecky

Scholz

2015

Proceedings of the 2nd ACM SIGPLAN International Workshop on Libraries, Languages, and Compilers for Array Programming

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Programming with abstract, mathematical expressions offers benefits including terser programs, easier communication of algorithms, ability to prove theorems about algorithms, increased parallelism, and improved programming productivity. Common belief is that higher levels of abstraction imply a larger semantic gap between the user and computer and, therefore, typically slower execution, whether sequential or parallel. In recent years, domain-specific languages have been shown to close this gap through sophisticated optimizations benefitting from domain-specific knowledge.In this paper, we demonstrate that the semantic gap can also be closed for non-domain-specific functional array languages, without requiring embedding of language-specific semantic knowledge into the compiler tool chain. We present a simple example of APL-style SaC programs, compiled into C-code that outperform equivalent C programs in both sequential and parallel (OpenMP) environments.We offer insights into abstract expressionist programming, by comparing the characteristics and performance of a numerical relaxation benchmark written in C99, C99 with OpenMP directives, scheduling code, and pragmas, and in SaC, a functional array language. We compare three algorithmic styles: if/then/else, handoptimized loop splitting, and an abstract, functional style whose roots lie in APL.We show that the SaC algorithms match or outperform serial C, and that the hand-optimized and abstract SaC styles generate identical code, and so have identical performance. Furthermore, parallel SaC variants also outperform the best OpenMP C variant by up to a third, with no SaC source code modifications. Preserving an algorithm's abstract expression during optimization opens the door to generation of radically different code for different architectures.

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Section: Related Workmentioning

confidence: 93%

Abstract expressionism for parallel performance

Bernecky

Scholz

2015

Proceedings of the 2nd ACM SIGPLAN International Workshop on Libraries, Languages, and Compilers for Array Programming

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“…For example, MATLAB's array semantics allow an array to be enlarged automatically whenever a write occurs to an out-of-bounds index, and also for certain operations to automatically promote the element type of an array from real to complex numbers. This poses implementation challenges for static MATLAB compilers like FALCON, which have to implement a complete type system with multiple compiler passes and interprocedural flow analyses to check for such drastic changes to arrays [18,25]. In fact, MATLAB's (and APL's) semantics are so flexible that shape inference on arrays is impossible to compute using ordinary dataflow analysis on bounded lattices [13].…”

Section: Dynamic Language Approachesmentioning

confidence: 96%

Array Operators Using Multiple Dispatch

Bezanson¹,

Chen²,

Karpinski³

et al. 2014

Proceedings of ACM SIGPLAN International Workshop on Libraries, Languages, and Compilers for Array Programming

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Arrays are such a rich and fundamental data type that they tend to be built into a language, either in the compiler or in a large lowlevel library. Defining this functionality at the user level instead provides greater flexibility for application domains not envisioned by the language designer. Only a few languages, such as C++ and Haskell, provide the necessary power to define n-dimensional arrays, but these systems rely on compile-time abstraction, sacrificing some flexibility. In contrast, dynamic languages make it straightforward for the user to define any behavior they might want, but at the possible expense of performance.As part of the Julia language project, we have developed an approach that yields a novel trade-off between flexibility and compiletime analysis. The core abstraction we use is multiple dispatch. We have come to believe that while multiple dispatch has not been especially popular in most kinds of programming, technical computing is its killer application. By expressing key functions such as array indexing using multi-method signatures, a surprising range of behaviors can be obtained, in a way that is both relatively easy to write and amenable to compiler analysis. The compact factoring of concerns provided by these methods makes it easier for userdefined types to behave consistently with types in the standard library.

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“…for all v ∈ V do 8: for all d ∈ v.D do 9: d.state ← GETSTATEDEF(v, d) 10: for all u ∈ v.U do 11: u.state ← GETSTATEUSE(v, u) 12: procedure GETSTATEDEF(v, d) 13:…”

Section: Code Generationmentioning

confidence: 99%

“…For example, FALCON [19] is a MATLAB to FORTRAN90 translator with sophisticated type inference algorithms. The McLab group has previously implemented a prototype Fortran 95 generator [12], and has more recently developed the next generation Fortran generator, Mc2FOR [13] in parallel with the MIX10 project. Some of the solutions are shared between the projects, especially the parts which extend the Tamer.…”

Section: Related Workmentioning

confidence: 99%

“…Given the shape information provided by the shape analysis engine [13] built into the McLAB analysis framework [3,4,6], it was not hard to compile MATLAB arrays to X10. However, to generate X10 code whose performance would be competitive to the generated C code (via MATLAB coder) and the generated Fortran code (via the Mc2FOR compiler), was not straightforward, and required deeper understanding of the X10 array system and careful handling of several features of the MATLAB arrays.…”

Section: Compilation To X10 Arraysmentioning

confidence: 99%

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Mix10

Kumar

Hendren

2014

Proceedings of the 2014 ACM International Conference on Object Oriented Programming Systems Languages &Amp; Applications

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MATLAB is a popular dynamic array-based language commonly used by students, scientists and engineers who appreciate the interactive development style, the rich set of array operators, the extensive builtin library, and the fact that they do not have to declare static types. Even though these users like to program in MATLAB, their computations are often very compute-intensive and are better suited for emerging high performance computing systems. This paper reports on MIX10, a source-to-source compiler that automatically translates MATLAB programs to X10, a language designed for "Performance and Productivity at Scale"; thus, helping scientific programmers make better use of high performance computing systems.There is a large semantic gap between the array-based dynamically-typed nature of MATLAB and the object-oriented, statically-typed, and high-level array abstractions of X10. This paper addresses the major challenges that must be overcome to produce sequential X10 code that is competitive with state-of-the-art static compilers for MATLAB which target more conventional imperative languages such as C and Fortran. Given that efficient basis, the paper then provides a translation for the MATLAB parfor construct that leverages the powerful concurrency constructs in X10.The MIX10 compiler has been implemented using the McLab compiler tools, is open source, and is available both for compiler researchers and end-user MATLAB programmers. We have used the implementation to perform many empirical measurements on a set of 17 MATLAB benchmarks. We show that our best MIX10-generated code is significantly faster than the de facto Mathworks' MATLAB system, and that our results are competitive with state-of-the-art static compilers that target C and Fortran. We also show the importance of finding the correct approach to representing arrays in X10, and the necessity of an IntegerOkay analysis that determines which double variables can be safely represented as integers. Finally, we show that our X10-based handling of the MATLAB parfor outperforms the de facto MATLAB implementation.

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Mc2FOR: A tool for automatically translating MATLAB to FORTRAN 95

Cited by 8 publications

References 7 publications

Abstract expressionism for parallel performance

Abstract expressionism for parallel performance

Array Operators Using Multiple Dispatch

Mix10

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