Even though modern programming languages are becoming more important than ever before, programmers have traditionally faced a dilemma: programs written in these languages traditionally have had lower performance than programs written in more conventional, but error-prone languages. In this thesis, I study this problem in the context of one particular modern programming language, Standard ML. Standard ML contains all the language features mentioned previously and more. I use an empirical approach to understand where Standard ML programs though better optimization.

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