A Retargetable MATLAB-to-C Compiler Exploiting Custom Instructions and Data Parallelism
Authors: 
Ioannis Latifis, 
Karthick Parashar, Grigoris Dimitroulakos, Hans Cappelle, Christakis Lezos, 
Konstantinos Masselos, Francky CatthoorAuthors Info & Claims
Ioannis Latifis, Karthick Parashar, Grigoris Dimitroulakos, Hans Cappelle, Christakis Lezos, Konstantinos Masselos, Francky CatthoorAuthors Info & ClaimsArticle No.: 50, Pages 1 - 27
Abstract
This article presents a MATLAB-to-C compiler that exploits custom instructions present in state-of-the-art processor architectures and supports semi-automatic vectorization. A parameterized processor model is used to describe the target instruction set architecture to achieve user-friendly retargetability. Custom instructions are represented via specialized intrinsic functions in the generated code, which can then be used as input to any C/C++ compiler supporting the target processor. In addition, the compiler supports the generation of data parallel/vectorized code through the introduction of data packing/unpacking statements. The compiler has been used for code generation targeting ARM and x86 architectures for several benchmarks. The vectorized code generated by the compiler achieves an average speedup of 4.1× and 2.7× for packed fixed and floating point data, respectively, compared to scalarized code for ARM architecture and an average speedup of 3.1× and 1.5× for packed fixed and floating point data, respectively, for x86 architecture. Implementing data parallel instructions directly in the assembly code would have required a lot of design effort, and it would not been sustainable across evolving platform variants. Thus, the compiler can be employed to efficiently speed up critical sections of the target application. The compiler is therefore potentially employable to raise the design abstraction and reduce development time for both embedded and general-purpose applications.
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Index Terms
- A Retargetable MATLAB-to-C Compiler Exploiting Custom Instructions and Data Parallelism
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Published: 03 October 2020
Online AM: 07 May 2020
Accepted: 01 March 2020
Revised: 01 March 2020
Received: 01 November 2019
Published in TECS Volume 19, Issue 6
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