Enhancing the Matrix Transpose Operation Using Intel Avx Instruction Set Extension
Ahmed Sherif Zekri · International Journal of Computer Science and Information Technology · 2014
General-purpose microprocessors are augmented with short-vector instruction extensions in order to simultaneously process more than one data element using the same operation.This type of parallelism is known as data-parallel processing.Many scientific, engineering, and signal processing applications can be formulated as matrix operations.Therefore, accelerating these kernel operations on microprocessors, which are the building blocks or large high-performance computing systems, will definitely boost the performance of the aforementioned applications.In this paper, we consider the acceleration of the matrix transpose operation using the 256-bit Intel advanced vector extension (AVX) instructions.We present a novel vector-based matrix transpose algorithm and its optimized implementation using AVX instructions.The experimental results on Intel Core i7 processor demonstrates a 2.83 speedup over the standard sequential implementation, and a maximum of 1.53 speedup over the GCC library implementation.When the transpose is combined with matrix addition to compute the matrix update, B + A T , where A and B are squared matrices, the speedup of our implementation over the sequential algorithm increased to 3.19.