1. IntroductionWhen it comes to computing speed, the GPU offers a huge advantage over the CPU, making it one of the most exciting research areas in modern industrial research and development. GPU is a graphics processing unit that allows you to run high-definition graphics on your personal computer, which are the demand of computing today. Like the central processing unit (CPU), it is a single-chip processor. However, the central processing unit has at most 4 or 8 cores compared to the hundreds of cores of the graphics processing unit. The main reason GPUs were made was for graphics purposes. Because graphics-related calculations place a very heavy load on the central processing unit, the graphics processing unit can help the computer run more efficiently. As stated earlier, graphics processing units came into existence for graphics purposes, but from now on its existence has additional purposes such as computation, precision, and performance. The evolution of the graphics processing unit over the years has been towards better floating point performance. NVIDIA introduced its parallel architecture called “Compute Unified Device Architecture” in 2006-2007 and changed the perspective of GPU computing. CUDA has a significant number of processor cores working together to chew up the data set provided in the application. GPU computing or general-purpose GPU is the use of a GPU (graphics processing unit) to perform general-purpose scientific and engineering calculations. The model for GPU computing involves using a central processing unit and a graphics processing unit together in a heterogeneous co-processing computing model. The compute-intensive part is accelerated by the GPU and the sequential part of the application... middle of paper... unified source code that includes both host and device code, however, the host is simply linear forwarding C code. Device code or GPU code is written using syntax for CUDA to label data parallel functions and their associated data structures. When a GPU device is unavailable, the device's code will continue to run but the central processing unit will be responsible for executing the code, and the execution speed will be very slow compared to the execution speed when a GPU device has executed the part of the code. This is possible with the help of emulation features provided by the CUDA software development kit. Another advantage of CUDA is that you don't have to write the entire program using CUDA technology. You could simply write kernel calls to call CUDA functions for faster processing of large mathematical calculations whenever you need them.