Let's take a look at some problems that may be solved using scientific computing. The first problem is to study the behavior of a collision of two black holes, which is very difficult to understand theoretically and practically. Theoretically, this process is extremely complex, and it is almost impossible to perform it in a laboratory and study it live. But this phenomenon can be simulated in a computing laboratory with a proper and efficient implementation of a mathematical formulation of Einstein's general theory of relativity. However, this requires very high computational power, which can be achieved using advanced distributed computing infrastructure.

The second problem is related to engineering and designing. Consider a problem related to automobile testing called crash testing. To reduce the cost of performing a risky actual crash for testing, engineers and designers prefer to perform a computerized simulated crash test. Finally, consider the problem of designing a large house or factory. It is possible to construct a dummy model of the proposed infrastructure. But that requires a reasonable amount of time and is expensive. However, this designing can done using an architectural design tool, and this will save a lot of time and cost. There can be similar examples from bioinformatics and medical science, such as protein structure folding and modeling of infectious diseases. Studying protein structure folding is a very time-consuming process, but it can be efficiently completed using large-scale supercomputers or distributed computing systems. Similarly, modeling an infectious disease will save efforts and cost in the analysis of the effects of various parameters on a vaccination program for that disease.

These three examples are selected as they represent three different classes of problems that can be solved using scientific computing. The first problem is almost impossible. The second problem is possible, but it is risky up to a certain extent and it may result in severe damage. The final problem can be solved without any simulation and it is possible to duplicate it in real-life situations. However, it is costlier and more time-consuming than its simulation.