Parallel Arrays C++ Example

• C++ Parallel Programming
• Parallel Arrays Python
• Java Parallel Arrays
• Sort Parallel Arrays

Anyways, this program below is supposed to put names (last, first) and ages into arrays and then alphabetize them. The problem I have is creating a sort of parallel array to match ages with their respective names after sorting them alphabetically. Windows 10 preactivated torrent.

In computing, a group of parallel arrays (also known as structure of arrays or SoA) is a form of implicit data structure that uses multiple arrays to represent a singular array of records. It keeps a separate, homogeneous data array for each field of the record, each having the same number of elements. Then, objects located at the same index in each array are implicitly the fields of a single record. Pointers from one object to another are replaced by array indices. This contrasts with the normal approach of storing all fields of each record together in memory (also known as array of structures or AoS). For example, one might declare an array of 100 names, each a string, and 100 ages, each an integer, associating each name with the age that has the same index.

Examples[edit]

• Distinguish signal waveforms by memristor conductance modulation. A 1k-cell cross-point array of memristors with TiN/TaO x /HfO y /TiN material stack is fabricated (see Materials and Methods for the fabrication details) as the platform to process multichannel neural signals in parallel.
• It uses Parallel Sorting of array elements. Algorithm of parallelSort 1. The array is divided into sub-arrays and that sub-arrays is again divided into their sub-arrays, until the minimum level of detail in a set of array. Arrays are sorted individually by multiple thread. The parallel sort uses Fork/Join Concept for sorting.

An example in C using parallel arrays:

in Perl (using a hash of arrays to hold references to each array):

Or, in Python:

Pros and cons[edit]

Parallel arrays have a number of practical advantages over the normal approach:

• They can be used in languages which support only arrays of primitive types and not of records (or perhaps don't support records at all).^{[example needed]}
• Parallel arrays are simple to understand, particularly for beginners who may not fully understand records.
• They can save a substantial amount of space in some cases by avoiding alignment issues. For example, some architectures work best if 4-byte integers are always stored beginning at memory locations that are multiple of 4. If the previous field was a single byte, 3 bytes might be wasted. Many modern compilers can automatically avoid such problems, though in the past some programmers would explictly declare fields in order of decreasing alignment restrictions.
• If the number of items is small, array indices can occupy significantly less space than full pointers, particularly on some architectures.
• Sequentially examining a single field of each record in the array is very fast on modern machines, since this amounts to a linear traversal of a single array, exhibiting ideal locality of reference and cache behaviour.
• They may allow efficient processing with SIMD instructions in certain instruction set architectures

Several of these advantage depend strongly on the particular programming language and implementation in use.

However, parallel arrays also have several strong disadvantages, which serves to explain why they are not generally preferred:

• They have significantly worse locality of reference when visiting the records non-sequentially and examining multiple fields of each record, because the various arrays may be stored arbitrarily far apart.
• They obscure the relationship between fields of a single record (e.g. no type information relates the index between them, an index may be used erroneously).
• They have little direct language support (the language and its syntax typically express no relationship between the arrays in the parallel array, and cannot catch errors).
• Since the bundle of fields is not a 'thing', passing it around it tedious and error-prone. For example, rather than calling a function to do something to one record (or structure or object), the function must take the fields as separate arguments. When a new field is added or changed, many parameter lists must change, where passing objects as whole would avoid such changes entirely.
• They are expensive to grow or shrink, since each of several arrays must be reallocated. Multi-level arrays can ameliorate this problem, but impacts performance due to the additional indirection needed to find the desired elements.
• Perhaps worst of all, they greatly raise the possibility of errors. Any insertion, deletion, or move must always be applied consistently to all of the arrays, or the arrays will no longer be synchronized with each other, leading to bizarre outcomes.

The bad locality of reference can be alleviated in some cases: if a structure can be divided into groups of fields that are generally accessed together, an array can be constructed for each group, and its elements are records containing only these subsets of the larger structure's fields. (see data oriented design). This is a valuable way of speeding up access to very large structures with many members, while keeping the portions of the structure tied together. An alternative to tying them together using array indexes is to use references to tie the portions together, but this can be less efficient in time and space.

Another alternative is to use a single array, where each entry is a record structure. Many language provide a way to declare actual records, and arrays of them. In other languages it may be feasible to simulate this by declaring an array of n*m size, where m is the size of all the fields together, packing the fields into what is effectively a record, even though the particular language lacks direct support for records. Some compiler optimizations, particularly for vector processors, are able to perform this transformation automatically when arrays of structures are created in the program.^{[citation needed]}

See also[edit]

References[edit]

• Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein. Introduction to Algorithms, Second Edition. MIT Press and McGraw-Hill, 2001. ISBN0-262-03293-7. Page 209 of section 10.3: Implementing pointers and objects.
• Skeet, Jon (3 June 2014). 'Anti-pattern: parallel collections'. Retrieved 28 October 2014.

Back to: C#.NET Tutorials For Beginners and Professionals

Parallel For in C# with Examples

In this article, I am going to discuss the static Parallel For in C# with some examples. Please read our previous article before proceeding to this article where we discussed the basics of Parallel Programming in C#. As part of this article, we will discuss the need and use of Parallel For loop comparing with the C# for loop. So, let's start the discussion with one of the most frequently asked interview questions.

The main difference between the Parallel For loop and the standard C# for loop are as follows

Note1: When the iterations are independent of each other, means, subsequent iterations do not read the state updates made by previous iterations, then on such cases, we need to use Task Parallel Library (TPL) to run each iteration in parallel on all the available cores.

Note2: Moreover, the iteration should be an expensive iteration otherwise we will get negative performance, that we will also discuss as part of this article.

Syntax:

As you can see in the above example, the static 'For' method of the static 'Parallel' class is defined as public static ParallelLoopResult For(int fromInclusive, int toExclusive, Action body);. Here the first parameter (i.e. int fromInclusive) is the start index. The second parameter (i.e. int toExclusive) is the end index and the third parameter (i.e. Action body) is the delegate which is invoked once per iteration. You can find many overloaded versions of this method in the Parallel class.

Once you run the above code, you will get the following output.

As you can see in the above output, the standard C# for loop iterates sequentially using a single thread as a result, the results are printed sequentially. On the other hand, you can see with the Parallel for loop the results are not printed in sequential order. This is because it uses multiple threads to iterate over the collection. You can see that there are in our example it uses five threads to execute the code. It may vary in your system.

First, we will write the example using C# for loop and will see how much time it will take to complete the execution. Then we will write the same example using Parallel For method and will see how much time it will take to complete the execution.

In the below example, we create a sequential loop. The loop iterates ten times, and the loop control variable increasing from zero to nine. In each iteration, the DoSomeIndependentTask method is called. The DoSomeIndependentTask method performs a calculation that is included to generate a long enough pause to see the performance improvement of the parallel version.

OUTPUT:

As you can see from the above output screen the for loop statement took approximately 3635 milliseconds to complete the execution.

Let's rewrite the same example using the Parallel For method.

OUTPUT:

As shown in the above output image, the Parallel For method took 2357 milliseconds to complete the execution.

Anyways, this program below is supposed to put names (last, first) and ages into arrays and then alphabetize them. The problem I have is creating a sort of parallel array to match ages with their respective names after sorting them alphabetically. Windows 10 preactivated torrent.

In computing, a group of parallel arrays (also known as structure of arrays or SoA) is a form of implicit data structure that uses multiple arrays to represent a singular array of records. It keeps a separate, homogeneous data array for each field of the record, each having the same number of elements. Then, objects located at the same index in each array are implicitly the fields of a single record. Pointers from one object to another are replaced by array indices. This contrasts with the normal approach of storing all fields of each record together in memory (also known as array of structures or AoS). For example, one might declare an array of 100 names, each a string, and 100 ages, each an integer, associating each name with the age that has the same index.

Examples[edit]

• Distinguish signal waveforms by memristor conductance modulation. A 1k-cell cross-point array of memristors with TiN/TaO x /HfO y /TiN material stack is fabricated (see Materials and Methods for the fabrication details) as the platform to process multichannel neural signals in parallel.
• It uses Parallel Sorting of array elements. Algorithm of parallelSort 1. The array is divided into sub-arrays and that sub-arrays is again divided into their sub-arrays, until the minimum level of detail in a set of array. Arrays are sorted individually by multiple thread. The parallel sort uses Fork/Join Concept for sorting.

An example in C using parallel arrays:

in Perl (using a hash of arrays to hold references to each array):

Or, in Python:

Pros and cons[edit]

Parallel arrays have a number of practical advantages over the normal approach:

• They can be used in languages which support only arrays of primitive types and not of records (or perhaps don't support records at all).^{[example needed]}
• Parallel arrays are simple to understand, particularly for beginners who may not fully understand records.
• They can save a substantial amount of space in some cases by avoiding alignment issues. For example, some architectures work best if 4-byte integers are always stored beginning at memory locations that are multiple of 4. If the previous field was a single byte, 3 bytes might be wasted. Many modern compilers can automatically avoid such problems, though in the past some programmers would explictly declare fields in order of decreasing alignment restrictions.
• If the number of items is small, array indices can occupy significantly less space than full pointers, particularly on some architectures.
• Sequentially examining a single field of each record in the array is very fast on modern machines, since this amounts to a linear traversal of a single array, exhibiting ideal locality of reference and cache behaviour.
• They may allow efficient processing with SIMD instructions in certain instruction set architectures

Several of these advantage depend strongly on the particular programming language and implementation in use.

However, parallel arrays also have several strong disadvantages, which serves to explain why they are not generally preferred:

• They have significantly worse locality of reference when visiting the records non-sequentially and examining multiple fields of each record, because the various arrays may be stored arbitrarily far apart.
• They obscure the relationship between fields of a single record (e.g. no type information relates the index between them, an index may be used erroneously).
• They have little direct language support (the language and its syntax typically express no relationship between the arrays in the parallel array, and cannot catch errors).
• Since the bundle of fields is not a 'thing', passing it around it tedious and error-prone. For example, rather than calling a function to do something to one record (or structure or object), the function must take the fields as separate arguments. When a new field is added or changed, many parameter lists must change, where passing objects as whole would avoid such changes entirely.
• They are expensive to grow or shrink, since each of several arrays must be reallocated. Multi-level arrays can ameliorate this problem, but impacts performance due to the additional indirection needed to find the desired elements.
• Perhaps worst of all, they greatly raise the possibility of errors. Any insertion, deletion, or move must always be applied consistently to all of the arrays, or the arrays will no longer be synchronized with each other, leading to bizarre outcomes.

The bad locality of reference can be alleviated in some cases: if a structure can be divided into groups of fields that are generally accessed together, an array can be constructed for each group, and its elements are records containing only these subsets of the larger structure's fields. (see data oriented design). This is a valuable way of speeding up access to very large structures with many members, while keeping the portions of the structure tied together. An alternative to tying them together using array indexes is to use references to tie the portions together, but this can be less efficient in time and space.

Another alternative is to use a single array, where each entry is a record structure. Many language provide a way to declare actual records, and arrays of them. In other languages it may be feasible to simulate this by declaring an array of n*m size, where m is the size of all the fields together, packing the fields into what is effectively a record, even though the particular language lacks direct support for records. Some compiler optimizations, particularly for vector processors, are able to perform this transformation automatically when arrays of structures are created in the program.^{[citation needed]}

See also[edit]

References[edit]

• Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein. Introduction to Algorithms, Second Edition. MIT Press and McGraw-Hill, 2001. ISBN0-262-03293-7. Page 209 of section 10.3: Implementing pointers and objects.
• Skeet, Jon (3 June 2014). 'Anti-pattern: parallel collections'. Retrieved 28 October 2014.

Back to: C#.NET Tutorials For Beginners and Professionals

Parallel For in C# with Examples

In this article, I am going to discuss the static Parallel For in C# with some examples. Please read our previous article before proceeding to this article where we discussed the basics of Parallel Programming in C#. As part of this article, we will discuss the need and use of Parallel For loop comparing with the C# for loop. So, let's start the discussion with one of the most frequently asked interview questions.

The main difference between the Parallel For loop and the standard C# for loop are as follows

Note1: When the iterations are independent of each other, means, subsequent iterations do not read the state updates made by previous iterations, then on such cases, we need to use Task Parallel Library (TPL) to run each iteration in parallel on all the available cores.

Note2: Moreover, the iteration should be an expensive iteration otherwise we will get negative performance, that we will also discuss as part of this article.

Syntax:

As you can see in the above example, the static 'For' method of the static 'Parallel' class is defined as public static ParallelLoopResult For(int fromInclusive, int toExclusive, Action body);. Here the first parameter (i.e. int fromInclusive) is the start index. The second parameter (i.e. int toExclusive) is the end index and the third parameter (i.e. Action body) is the delegate which is invoked once per iteration. You can find many overloaded versions of this method in the Parallel class.

Once you run the above code, you will get the following output.

As you can see in the above output, the standard C# for loop iterates sequentially using a single thread as a result, the results are printed sequentially. On the other hand, you can see with the Parallel for loop the results are not printed in sequential order. This is because it uses multiple threads to iterate over the collection. You can see that there are in our example it uses five threads to execute the code. It may vary in your system.

First, we will write the example using C# for loop and will see how much time it will take to complete the execution. Then we will write the same example using Parallel For method and will see how much time it will take to complete the execution.

In the below example, we create a sequential loop. The loop iterates ten times, and the loop control variable increasing from zero to nine. In each iteration, the DoSomeIndependentTask method is called. The DoSomeIndependentTask method performs a calculation that is included to generate a long enough pause to see the performance improvement of the parallel version.

OUTPUT:

As you can see from the above output screen the for loop statement took approximately 3635 milliseconds to complete the execution.

Let's rewrite the same example using the Parallel For method.

OUTPUT:

As shown in the above output image, the Parallel For method took 2357 milliseconds to complete the execution.

The ParallelOptions class is one of the most useful class when working with multithreading. This class provides options to limit the number of concurrently executing loop methods.

Using the Degree of parallelism we can specify the maximum number of threads to be used to execute the program. Following is the syntax to use ParallelOptions class with Degree of parallelism.

The MaxDegreeOfParallelism property affects the number of concurrent operations run by Parallel method calls that are passed this ParallelOptions instance. A positive property value limits the number of concurrent operations to the set value. If it is -1, there is no limit on the number of concurrently running operations.

Let us see an example for better an understanding of the MaxDegreeOfParallelism.

OUTPUT:

As we set the degree of parallelism to 2. So, a maximum of 2 threads is used to execute the code that we can see from the above output.

C++ Parallel Programming

The below example shows how to break out of a For loop and also how to stop a loop. In this context, 'break' means complete all iterations on all threads that are prior to the current iteration on the current thread, and then exit the loop. 'Stop' means to stop all iterations as soon as convenient.

Parallel Arrays Python

In a Parallel.For or Parallel.ForEach loop, you cannot use the same break or Exit statement that is used in a sequential loop because those language constructs are valid for loops, and a parallel 'loop' is actually a method, not a loop. Instead, you use either the Stop or Break method.

Java Parallel Arrays

In the next article, I am going to discuss theParallel ForEach Method in C#with some examples. Here, In this article, I try to explain the Parallel For in C# with some examples. I hope you understood the need and use of Parallel For method in C#.

Sort Parallel Arrays

Please have a look at our LINQ Tutorials.