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A <em>parameter </em>is a variable used to pass information to a method.
Answer:
The solution code is written in Python 3.
- def convertDate(date_string):
-
- date_list = date_string.split("/")
-
- for i in range(0, len(date_list)):
- date_list[i] = int(date_list[i])
-
- return date_list
-
-
- print(convertDate('06/11/1930'))
Explanation:
Firstly, create a function convertDate() with one parameter, <em>date_string</em>. (Line 1).
Next, use the Python string <em>split()</em> method to split the date string into a list of date components (month, day & year) and assign it to variable <em>date_list</em>. (Line 3) In this case, we use "/" as the separator.
However, all the separated date components in the <em>date_list</em> are still a string. We can use for-loop to traverse through each of the element within the list and convert each of them to integer using Python<em> int() </em>function. (Line 5 - 6)
At last return the final date_list as the output (Line 8)
We can test our function as in Line 11. We shall see the output is as follow:
[6, 11, 1930]
The discipline of building hardware architectures, operating systems, and specialized algorithms for running a program on a cluster of processors is known as <u>parallel computing.</u>
<u></u>
<h3>What is Parallel Computing?</h3>
Parallel computing refers to the process of breaking down larger problems into smaller, independent, often similar parts that can be executed simultaneously by multiple processors communicating via shared memory, the results of which are combined upon completion as part of an overall algorithm. The primary goal of parallel computing is to increase available computation power for faster application processing and problem solving.
<h3>Types of parallel computing</h3>
There are generally four types of parallel computing, available from both proprietary and open source parallel computing vendors:
- Bit-level parallelism: increases processor word size, which reduces the quantity of instructions the processor must execute in order to perform an operation on variables greater than the length of the word.
- Instruction-level parallelism: the hardware approach works upon dynamic parallelism, in which the processor decides at run-time which instructions to execute in parallel; the software approach works upon static parallelism, in which the compiler decides which instructions to execute in parallel.
- Task parallelism: a form of parallelization of computer code across multiple processors that runs several different tasks at the same time on the same data.
- Superword-level parallelism: a vectorization technique that can exploit parallelism of inline code.
Learn more about parallel computing
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