Abstract: Matrix multiplication has been widely used in various application fields, especially the field of numerical computation. However, double-precision floating-point matrix multiplication suffers from non-optimal performance or efficiency on contemporary computing platforms, including CPU, GPGPU and FPGA. To address this problem, acceleration of double-precision floating-point matrix multiplication with a customized coprocessor is proposed in this paper, which adopts linear array as the basic building block. Firstly, double-buffering technique and optimized memory scheduling are applied to the basic linear array for better computation efficiency. Then, architecture of the matrix multiplication coprocessor and coprocessor-based accelerated computing system are formulated. Furthermore, a performance model tailored for the coprocessor is developed and the design space of coprocessor is explored in detail. Finally, functional correctness of the coprocessor is verified and its hardware implementation cost under mainstream technology node is evaluated. Experimental results show that the proposed coprocessor can achieve the performance of 3 TFLOPS and the efficiency of 99%. Compared with NVIDIA K40 GPGPU for executing double-precision floating-point matrix multiplication, the coprocessor proposed in this paper achieves 1.95× performance with hardware overheads of only 21.05% in area. This work explores the application of customized acceleration in high-performance computing and has certain guidance for improving performance of existing computing systems.