I’ve got this interesting exercise to share with everyone. See if you can figure this out on your own first… Suppose a homogeneous array with 8 rows and 6 columns is stored in column major order starting at address 20 (base ten). If each entry in the array requires only one memory cell, what is the address of the entry in the third row and fourth column? What if each entry requires two memory cells? So, the problem #1 is following: Identify the address of the entry in the third row and fourth column of the array, in the given problem. And the problem #2 is to: Identify the address of the entry in the third row and fourth column of the array, in the given problem if each entry requires two memory cells. Read More →

Despite its many benefits, some view the OOP paradigm as often being unnecessarily complicated, with a sizeable hardware footprint, and generally requiring more elaborate preparatory planning. While I cannot offer any specific tips or techniques that may be used to minimize or eliminate some of these inherent OOP disadvantages, I strongly believe that at the end of the day, it’s the application architecture and proper planning, that may come to rescue. Let me explain… Read More →

The greater propensity for code reusability is often put forward as a key advantage of OOP over Structured programming. Some however think that this aspect of OOP is overhyped, and that in fact a number of software design problems may arise from over-stressing code reusability. And I am sharing the sentiment. Let’s look at this issue a bit… Read More →

In this article, I’ll compare object-oriented programming (OOP) to structured programming. I will do my best to describe the key concepts of OOP and structured programming languages and described this somewhat complex and technical topic using words and terms that the average individual (someone without professional training in the subject area) can understand so that they may comprehend the issue to some degree. Read More →

Couple things come to mind when thinking about metrics and comparison of algorithms. As we all know, the efficiency of an algorithm can be deduced from the resource usage, thus comparing the scripts quantitatively as oppose to qualitatively, would probably be an easier task. And that’s natural only because quantitatively we can look at numerous data points that can provide insights into quantities of computation resources consumed during execution of an algorithm. In this article, I’ll concentrate mainly on the Quantitative Metrics

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