If your lab or research group has other machines, use them as well - in the long run, those are the machines to learn how to use effectively. However, assignments will require your codes to run on silo, to avoid issues of trying to test and grade codes running on potentially dozens of different systems.
The course is not the same as numerical analysis, which concentrates on the study of convergence, stability, and error analysis in numerical methods. For that, students should take Math 571-572. Although numerical analysis is an important component of scientific computing, it is only a part of the field. Instead, this course concentrates on
The last item is the most important one. The single most fundamental skill you will need to master is load-store analysis, and that is the pass/fail criterion. Secondary tools and skills include how to recognize in practice when problems in floating point arithmetic occur, how to write code that gives scientifically reproducible results, how to efficiently implement linear algebraic computational operations, and how to time and profile parts of codes.
P573 is not parallel computing. Other courses including CSci B673 concentrates on that aspect of scientific computing.
Some basic Matlab is coverd in the course, a language that provides interactive graphing capabilities and more importantly gives an easy way to recognize and use numerical vector and matrix operations. Matlab is a rapid prototyping tool with easily-accessed graphics. Mostly scripts will be provided for you and basic Matlab will be taught as needed in lectures.
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