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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.

Source code optimization can heavily improve software code implementation quality while still being complementary to conventional compilers’ optimizations. Source code analysis tools are very useful in supporting source code optimization. This article discusses MemAssist, a source-level optimization environment for semi-automatic locality optimization of loop-dominated code. MemAssist applies reuse distance analysis and a relevant optimization algorithm to explore the design space. It generates a set of suggestions for locality optimizing loop transformations that reduce data cache miss rate and execution time. MemAssist has been used to optimize a number of applications. Experimental results show that MemAssist leads to cache miss rate reduction at all cache layers, memory accesses reduction by up to 42%, and to a speedup of up to three times. Therefore, MemAssist can be used for efficient early-stage software optimization leading to development effort and time reduction.

Array programming languages, such as MATLAB, are often used for algorithm development by scientists and engineers without taking into consideration implementation related issues and with limited emphasis on relevant optimizations. Application code optimization, especially in terms of data storage and transfer behavior, is still an important issue and heavily affects implementations' quality in terms of performance, power consumption etc. Efficient approaches for the optimization of high level application code are required to derive high quality implementations while still reducing development time and cost. This paper presents MemAssist, a software tool supporting application developers in detecting parts of the application code in MATLAB that do not exploit efficiently the targeted processor architecture and especially the memory hierarchy. Furthermore, the proposed tool guides application developers in applying code transformations in MATLAB for the optimization of the algorithm's temporal data locality. An image processing algorithm has been optimized using MemAssist as a practical usage scenario. Experimental results prove that the use of MemAssist can heavily reduce cache misses (up to 40%) and improve execution time (up to 30% speedup) on two different processor architectures. Thus, MemAssist can be used for optimized application code development that can lead to efficient implementations while still reducing development time and cost.

© 2016 EDAA. This paper discusses a MATLAB to C compiler exploiting custom instructions such as instructions for SIMD processing and instructions for complex arithmetic present in Application Specific Instruction Set Processors (ASIPs). The compiler generates ANSI C code in which the processor's special instructions are represented via specialized intrinsic functions. By doing this the generated code can be used as input to any C/C++ compiler. Thus the proposed compiler allows the description of the specialized instruction set of the target processor in a parameterized way allowing the support of any processor. The proposed compiler has been used for the generation of application code for an ASIP targeting DSP applications. The code generated by the proposed compiler achieves a speed up between 2x-30x on the targeted ASIP for six DSP benchmarks compared to the code generated by Mathworks MATLAB to C compiler. Thus the proposed compiler can be employed to reduce the development time/effort/cost and time to market by raising the abstraction of application design in an embedded systems / system-on-chip development context while still improving implementation efficiency. ispartof: pages:1453-1456 ispartof: PROCEEDINGS OF THE 2016 DESIGN, AUTOMATION & TEST IN EUROPE CONFERENCE & EXHIBITION (DATE) vol:22 issue:2 pages:1453-1456 ispartof: Design, Automation and Test in Europe Conference and Exhibition (DATE) location:GERMANY, Dresden date:14 Mar - 18 Mar 2016 status: published