In the era of parallel computing, there has been a steady growth of cores available on the central processing unit (CPU). However, the “free lunch” is now over and the CPU faces the end of the performance gains easily achieved by Moore's Law. Another widely used processing unit, the graphics processing unit (GPU), has also been affected by Moore's law. Therefore, as the graphics standards required by driving markets have steadily increased, the companies that produce the GPU have been able to consistently deliver a product that meets those standards. Additionally, the GPU has become as efficient, if not more efficient than the CPU, at handling floating point calculations. For this reason, the GPU is no longer limited to graphics-related industries and is used in applications outside of graphics processing. Therefore an analysis of the graphics processing unit will be conducted. The focus will be on the history of the graphics processing unit, the tools that help use the GPU, and the impact it has had on the field of parallel and high-performance computing. Similar to the technologies that the computing industry has become accustomed to, the GPU did not emerge on the market as the sophisticated hardware that it currently is. To get to this point it took many years of development and experimentation that led to the implementation of the GPUs available today. For starters, one of the first companies to develop a dedicated graphics hardware component was IBM. In 1984, they introduced the IBM Professional Graphics controller, which used an integrated Intel 8088 microprocessor to process some of the video-related tasks. Even though it was...half of paper... Thomas S. Evolution of the graphics processing unit. Thesis: University of Nevada, 2004.McClanahan, Chris. History and evolution of the GPU. Georgia Tech College of Computing, 2010.Munshi, Aaftab, Benedict Gaster, and Timothy Mattson. OpenCL Programming Guide. Pearson Education, 2011. Owens, John D, et al. “GPU computing.” Proceedings of the IEEE 96. 2008. 879-899. Sanders, Jason and Edward Kandrot. “CUDA by example.” An introduction to general-purpose GPU programming. Addison Wesley, 2010.Stone, John E, et al. “Accelerating molecular modeling applications with graphics processors.” Journal of Computational Chemistry (2007). Titan, OLCF. Titan, the world's #1 open science supercomputer. 2012. 2014 .The US Titan supercomputer is considered the fastest in the world. November 12, 2012. April 30 2014 .