12/27/2019 0 Comments
Supercomputing Exaflop Target - Assignment Example Data delivery and correction of errors might reduce the speed of the application despite the superb programming. The GPUs has allows enormous calculations of numbers in parallel as they constitute more cores as compared to the CPUs and current is applied to various data-intensive calculations. The GPUs was originally for tasks that are of graphics such as rendering every pixel in an image. This is because the graphics problem resembles the supercomputing problems (Geller, 16). In the modern world of supercomputing, the GPUs relies on the CPUs for other tasks despite being able to provide the highest calculation power. The speed is not a matter of throwing more cores in the given mix given that it is not easy to avail all the power used in the processing. The data ought to be managed for their proper intake and managing of the outcome. For the data to move appropriately between the CPUs and GPUs and attainment of better performance, the problem has to fit in the GPUs itself. Before the benefit is attained the speed of the moving data and that of computing are so mismatched and therefore the GPU has to undertake a number of computations (Geller, 16). The Asian researchers are well positioned to the GPUs more parallel supercomputing that is massive. It is believed that economics that favors such innovations may be brought about by Chinaâ€™s isolation from the western influences. This is because of the vendors from the US who holds different perceptions. Whereas the potential bang for the buck is in Asia, if an application works effectively on this kind of accelerator technology, it might turn out to be very cost effective. Chinaâ€™s recent entry into HPC may be of help in this regard. In contrast, it is argued that programmers in more experienced nations may have to undergo re-education (Geller, 16). Historically Hybrid architectures have had an upper hand over the parallelism. They have used less energy than comparable CPU-only systems. The new Top500 list showed that the architectural battle over energy efficiency is on. The U.S.â€™s CPU-based IBM Blue Gene/Q Prototype supercomputer is the most energy-efficient system with the efficiency of 1,680 Mflops/watt. The K Computer consumes enough energy to power nearly 10,000 homes and costs $10 million a year to operate. These costs would significantly increase in an exaflop world. Major changes in the hardware will require major changes in the algorithms and software. Supercomputing is already widely used in fields as diverse as weather modeling, financial predictions, animation, fluid dynamics, and data searches. Each of these fields embodies several applications. While exaflop computers will spawn now-unimagined uses, any current increases in speed as we race toward that goal will greatly benefit many existing applications (Geller, 16).
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