GPU Accelerated Tensor Computation of Hadamard Product for Machine Learning Applications

The computation on Graphics Processing Unit (GPU) has come out as a new cost-effective parallel computing paradigm for high performance computing that makes possible to process large scale data in parallel. GPU is designed to perform complex mathematical and geometric tasks which are primarily used for 3D graphics related functions. It is also possible to use GPU for non-graphics or general-purpose computation, called General Purpose Computing on GPU (GPGPU), a sub-discipline of High-Performance Computing (HPC). The use of GPU, along with CPU to accelerate more complex scientific, engineering and mathematical tasks is known as GPU Accelerated Computing. In this paper, we propose an efficient tensor computation for Hadamard Product (HP) which is directly applied in machine learning applications especially in…