Abstract
This paper proposes extending a multi-core processor with a common matrix unit to maximize on-chip resource utilization and to leverage the advantages of the current multi-core revolution to improve the performance of data-parallel applications. Each core fetches scalar/vector/matrix instructions from its instruction cache. Scalar instructions continue the execution on the scalar datapath; however, vector/matrix instructions are issued by the decode stage to the shared matrix unit through the corresponding FIFO queue. Moreover, scalar results from reduction vector/matrix instructions are sent back from the matrix unit to the scalar core that sent these instructions. Some dense linear algebra kernels (scalar-vector multiplication, scalar times vector plus another, apply Givens rotation, rank-1 update, vector-matrix multiplication, and matrix-matrix multiplication) as well as discrete cosine transform, sum of absolute differences, and affine transformation are used in the performance evaluation. Our results show that the improvement in the utilization of the shared matrix unit with a dual-core ranges from 9% to 26% compared to extending a matrix unit to a single-core. Moreover, the average speedup of the dual-core shared matrix unit over a single-core extended with a matrix unit ranges from 6% to 24% and the maximum speedup ranges from 13% to 46%. (C) 2013 Elsevier Inc. All rights reserved.