Dependency on code in Parallel Programming

Serial programs' are designed in a sequential manner such that the outcomes of earlier computations might influence those of subsequent calculations. You cannot assume that the sequence of statements in a parallel program will match the order in which the calculations are carried out; instead, you must be aware of any data dependencies and make sure that the computations are carried out in the proper order. Isolating dependencies inside specific tasks or coordinating the execution of several activities are two methods for managing data reliance in parallel processing.

When a calculation is dependent on the output of another computation, data dependence arises. When there is data dependency in an algorithm or computation, the software must carry out the operations in the proper sequence to get the desired outcome. Any algorithm that is even moderately complicated will have some data reliance.
In serial programming, statements are carried out sequentially and in the order they are written in the code. Writing the code statements in the proper order in a serial application is the only way to manage data dependence. Managing data dependence is more difficult when some parts of the program are run concurrently in parallel programming.

The first step to making sure that parallel processing is done correctly is to identify the areas of your code that entail data dependence. The first two examples below illustrate straightforward calculations that don’t rely on any data. Two examples with a lot of data reliance are given after these to illustrate how the method you select could lessen the data dependency in your application.

WITH NO DEPENDENCY: Consider a programme that adds the equivalent items from the two input arrays, A and B, to create a third array. The programme is still easy to parallelize because there are no relationships between any of the inputs or outputs in this situation, and no operation depends on the outcomes of any previous actions.

To create an output array, a software takes an input array and applies a function to each member. Each output component stands alone from the other outputs. Each output element might be computed by a distinct job since the information needed to calculate a specific output array member depends solely on the corresponding element from the input array.

Consider a programme that takes two input arrays, A and B, and computes a third array by adding the equivalent elements of both input arrays. Since all of the inputs and outputs in this scenario are unrelated to one another and no operation depends on the results of any prior operations, the programme is still simple to parallelize.

A software takes two input arrays and creates an output array by computing the pairwise sum. Each output element might be computed by a distinct job since the information needed to compute a specific output array member depends solely on the matching elements from the input arrays.

With High dependency: Dependency on data is impacted by algorithm selection. Think about a programme that totals an input array. In one implementation, the software builds the total from the first element up, element by element, to get the final value. A pattern of data dependence that is resistant to parallelization is produced by this approach.

A software takes two input arrays and creates an output array by computing the pairwise sum. Each output element might be computed by a distinct job since the information needed to compute a specific output array member depends solely on the matching elements from the input arrays.

With High dependency: Dependency on data is impacted by algorithm selection. Think about a programme that totals an input array. In one implementation, the software builds the total from the first element up, element by element, to get the final value. A pattern of data dependence that is resistant to parallelization is produced by this approach.

When given an array, a software computes the scalar sum of the array’s elements. The method cannot be effectively parallelized in this implementation since each computation depends on the outcome of the previous operation.

With Reduced dependency: A other approach for computing an array sum could combine adjacent pairs of items to create a new array that is roughly half the size of the input array before repeating the process until the output array is reduced to a single element. This alternative approach can benefit from parallel jobs, as opposed to the first array summation algorithm, which requires a distinct task to conduct each pairwise summation at each stage of the reduction. The dependency graph below illustrates this: it includes the same amount of pairwise summations as the first graph, but the pairings are reorganised to enable more summations to be performed concurrently.

When given an array, a software computes the scalar sum of the array’s elements. In this instance, the algorithm adds successive items from the input array to obtain an intermediary result. Up until there is just one entry in the result array, this process is repeated. In this illustration, two parallel workers create the second intermediate array, one worker completes the final add operation, and four parallel workers calculate the first intermediate array. In contrast to the seven stages needed to compute the array sum using the first technique, it only requires three stages to compute the result since much of the work may be done in parallel.