  In recent decades, High Performance Computing (HPC) has undergone significant
enhancements, particularly in the realm of hardware platforms, aimed at
delivering increased processing power while keeping power consumption within
reasonable limits. The Intelligence Processing Unit (IPU) represents an
entirely novel category of massively parallel processors, meticulously designed
to expedite parallel computations through a multitude of processing cores and
on-chip memory components interconnected via high-speed fabrics. While IPUs are
primarily tailored for machine learning applications and come equipped with
several libraries for the seamless implementation of neural networks, they also
retain the capability to execute traditional parallel programs like matrix
multiplication. However, it is essential to acknowledge that there are certain
considerations and limitations when utilizing IPUs for such tasks. This paper
embarks on an extensive analytical examination of matrix multiplications (MM)
executed on an IPU, focusing on aspects such as execution efficiency and memory
usage. Additionally, a comparative analysis is conducted, pitting the IPU
against a GPU. Our findings indicate that IPUs can outperform modern GPUs,
especially in handling the consistently challenging skewed matrix
multiplication operations. For a more comprehensive understanding, we
scrutinize various aspect ratios of matrices for these operations on an IPU and
a Turing-class GPU (RTX 2080TI), revealing that the IPU consistently delivers
more robust performance when dealing with skewed matrices compared to a GPU.
