第7章高水準プログラミング言語

While runtime program management using virtual machines provides large flexibility gains, many industries that rely on large enterprise-level software to support demanding mission-critical business applications typically rely on more traditional runtime environments and older, proven HLLs. This is particularly true in the financial industry today. A good example is the use of Python as a core language (https://openstax.org/r/76PythonCorLang) for J.P. Morgan’s Athena program and Bank of America’s Quartz program. Why do you think this is the case?

While debuggers are present in most virtual machines and integrated program development environments (IDEs) and provided by programming language interpreters, they also come as standalone tools. Symbolic debuggers understand high-level syntax. In short, a debugger finds errors in programs. It can also set parameters for stopping an execution should it reach a certain point in the source code or if a constant is read.

The goal of code optimization is to rework parts of your code for efficiency. Some of the problem areas of optimization are redundant computations, as well as inefficient use of the hardware registers, multiple functional units (input, output), memory, and cache.

We can interpret the results of optimization to mean fast and/or decreasing memory requirements. Optimization is best carried out at multiple levels, one of which is at the basic block level. A basic block is a minimal length sequence of instructions that will always execute in its entirety if it executes at all, equivalent to a code block. Code improvement at the level of basic blocks is known as local optimization, consisting of eliminating redundant operations (unnecessary loads, common subexpression calculations), providing effective instruction scheduling, and providing register allocation.

At higher levels, compilers review and analyze all subroutines for further improvements. The code improvements at multiple layers that include loop performance improvement, register allocation, and instruction scheduling is called global optimization.

The medical industry views code optimization as a must-have. Most medical operations make use of patient portals for communication between patients and the medical practice. This requires both speed and platform portability such as Windows and Android. Because of this, Java is the language of choice (https://openstax.org/r/76LangofChoice) for these applications. Why is it important for programmers to understand code optimization techniques? Should they only rely on the optimizing compiler provided for the HLL they use? Why or why not? Will you become a better programmer by understanding the way that lower-level code works in order to have a high-level view of optimization?

As with code optimization, code improvement also aims to increase execution speed. Code improvement focuses on eliminating basic blocks redundancy, loop improvements, and instruction scheduling. The biggest area of improvement focuses on the review and enhancement of the behavior of loops. Reordering loops can be difficult but rewarding as all data dependencies must be respected (loop-carried dependencies).

The conclusion that one can form based on code optimization and code improvement is that the “need for speed” is paramount in today’s global software and internet environments.

Did you know that compilers can often improve the performance of your code? This process is called optimization. Optimizers can make your code run faster by using techniques such as removing unnecessary calculations, improving memory usage, and reorganizing code for better efficiency.

By understanding optimization techniques, you can write smoother code and use fewer resources. As a side note, mainstream programming language compilers provided by Microsoft and Oracle are designed to optimize the execution of programs. Microsoft’s .Net framework uses virtual machines that manage the CLR of various programming languages. The intermediate byte code generated by compilers that target these virtual machines is optimized using some of the techniques described in this chapter.