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Hopper disassembler python scripts code#

Most would agree that "runnning" and "executing" are the same concept, and that "time" is expressed in a unit of time (like milliseconds). This is not what you would intuitively use as a definition. The definition of "running time" in 'Introduction to Algorithms' by C,L,R,S is actually not a time, but a number of steps. What is the real meaning or definition of running time and execution time? Are they the same of different?

Does running time depend on the computer or not?.

Does running time describe the number of steps executed or not?.

What is the real meaning or definition of running time and execution time? Are they the same of different?.

I thought the execution time and the running time are same! Also the running time depends on the computer (my assumption) but the author from the second book says that we cannot consider the Execution time to analyze algorithms as it totally depends on the computer. This kind of comparison is independent of machine time, programming style, etc.Īs you can see from CLRS the author describes the running time as the number of steps executed whereas in the second book the author says its not a good measure to use Number of step executed to analyze the algorithms. Of the input size n (i.e., f(n)) and compare these different functions corresponding to running Ideal solution? Let us assume that we express the running time of a given algorithm as a function With the programming language as well as the style of the individual programmer. Number of statements executed? Not a good measure, since the number of statements varies To compare algorithms, let us define a few objective measures:Įxecution times? Not a good measure as execution times are specific to a particular computer. Running time but also in terms of other factors (e.g., memory, developer effort, etc.) The goal of the analysis of algorithms is to compare algorithms (or solutions) mainly in terms of Then I referred a book called Data Structures and Algorithms made easy by Narasimha Karumanchi. The running time of an algorithm on a particular input is the number of primitiveĪlso the author uses the running time to analyze algorithms. In which the author describes the Running time of an algorithm as

I'm currently reading this book called CLRS 2.2 page 25.

Hopper disassembler python scripts code#

In practice, you should not expect such optimizations, as the Stream implementation code is too complex for the optimizer. This behavioral compatibility implies that even if the processing gets optimized away, a peek(System.out::println) would keep printing all elements as if they were processed. However, this requires inlining of all involved methods to be sure that this conditions always applies and there are no side effect which must be retained. But the responsibility lies at the JVM’s optimizer which might do the same for loops. This does not mean that such optimizations based on logic or algebra are impossible. This is a short-circuiting terminal operation. Go to the Stream API documentation and check whether the particular terminal operation’s documentation contains the sentence This is always the case, so when you don’t chain a terminal operation, no intermediate operation will ever process any element.įinding out whether a terminal operation is short-circuiting, is rather easy. The term “lazy” only applies to intermediate operations and means that they only perform work when being requested by the terminal operation. Having a short-circuiting operation in the pipeline is a necessary, but not sufficient, condition for the processing of an infinite stream to terminate normally in finite time.

A terminal operation is short-circuiting if, when presented with infinite input, it may terminate in finite time. An intermediate operation is short-circuiting if, when presented with infinite input, it may produce a finite stream as a result. The actual term you’re asking for is short-circuitingįurther, some operations are deemed short-circuiting operations.