Superword level parallelism aware word length optimization

Many embedded processors do not support floating-point arithmetic in order to comply with strict cost and power consumption constraints. But, they generally provide support for SIMD as a mean to improve performance for little cost overhead. Achieving good performance when targeting such processors requires the use of fixed-point arithmetic and efficient exploitation of SIMD data-path. To reduce time-to-market, automatic SIMDization — such as superword level parallelism (SLP) extraction — and float-to-fixed-point conversion methodologies have been proposed. In this paper we show that applying these transformations independently is not efficient. We propose a SLP-aware word length optimization algorithm to jointly perform float-to-fixed-point conversion and SLP extraction. We implement the proposed approach in a source-to-source compiler framework and evaluate it on several embedded processors. Experimental results illustrate the validity of our approach.