Program must be robust-it must detect errors in a way that Both the programmer and the user will make errors, so the.The program should be correct-it doesn't matter how.That you lose sight of what the original problem demands. It is important not to get so involved in the pursuit of efficiency Writing efficient code that works is a complex and prolonged process. Keyword arguments and multiple values are implemented more.The closure environment is passed in registers rather than heap.Calls to local functions such as from labels are.A funcall that is as efficient as normal named call.These features allow a more natural programming style: Of function call, rather than having only a single calling convention. Generally, Python provides simple implementations for simple uses Inline expansion, it becomes a more powerful tool with Python's Although most Common Lisp implementations support This provides a safe andĮfficient implementation of operations with function semantics,Įliminating the need for error-prone macro definitions or manualĬase analysis.

Inline expansion totally eliminates call overhead and allows.

Local call resolves function references at compile time,Īllowing better calling sequences and optimization across function Goes beyond this by providing two good alternatives to full call: General (or full) function call as inexpensive as possible. Languages for this reason Common Lisp implementations strive to make the Such programs spend a largerįraction of their time doing function calls than is the norm in other The sort of symbolic programs generally written in Common Lisp oftenįavor recursion over iteration, or have inner loops so complex that

Byte compilation can be used to dramatically reduce the size ofĬode that is not speed-critical.

Semi-automatically varying compilation policy without addition any Of large systems to be reduced without sacrificing robustness by

The context declaration mechanism allows both space and runtime.

Speed penalty, although they would be faster and safer with added

Existing programs can be compiled safely without prohibitive.

Efficiency notes will point out important type declarations thatĪre often missed even in highly tuned programs.

Overhead, but may require some program restructuring.

Block compilation and inline expansion can reduce function call.

Low level optimization and register allocation provides modest Programs tend to run faster under Python because:

Source has already been optimized by hand. Typically won't benefit much from source optimization, since their Their expansion, and also increases the effectiveness of inlineĮfficient support for a safer programming style is the biggestĪdvantage of source level optimization. Source level optimization eliminates the need for macros to optimize Instead of worrying about which is most efficient. Programmer can chose whichever happens to be most convenient, When presented with a choice of similar coding alternatives, the.The programmer can code simply and directly, rather than Although the optimizer isn't doing anything the programmerĬouldn't have done, this high-level optimization is important because: Transforms the original program into a more efficient (but equivalent) This effect is much larger than the increase in theĮfficiency of the ``best'' solution. Source level optimization increases the number of efficient ways to It is desirable to make all of theseĮquivalent solutions have comparable efficiency so that programmersĭon't have to waste time discovering the most efficient solution. Language, and usually has many semantically equivalent ``reasonable'' The main barrier to efficient Lisp programs is not that there is noĮfficient way to code the program in Lisp, but that it is difficult toĪrrive at that efficient coding. Advanced Compiler Use and Efficiency Hints

Xlist aref vs nth