Higher-order functions are closely related to first-class functions in that higher-order functions and first-class functions both allow functions as arguments and results of other functions. The distinction between the two is subtle: Higher-order functions enable partial application or curryinga technique that applies a function to its arguments one at a time, with each application returning a new function that accepts the next argument. This lets a programmer succinctly express, for example, the successor function as the addition operator partially applied to the natural number one.
The need for communications between tasks depends upon your problem: You DON'T need communications: Some types of problems can be decomposed and executed in parallel with virtually no need for tasks to share data. These types of problems are often called embarrassingly parallel - little or no communications are required.
For example, imagine an image processing operation where every pixel in a black and white image needs to have its color reversed. The image data can easily be distributed to multiple tasks that then act independently of each other to do their portion of the work.
You DO need communications: Most parallel applications are not quite so simple, and do require tasks to share data with each other. For example, a 2-D heat diffusion problem requires a task to know the temperatures calculated by the tasks that have neighboring data.
Changes to neighboring data has a direct effect on that task's data.
There are a number of important factors to consider when designing your program's inter-task communications: Communication overhead Inter-task communication virtually always implies overhead.
Machine cycles and resources that could be used for computation are instead used to package and transmit data. Communications frequently require some type of synchronization between tasks, which can result in tasks spending time "waiting" instead of doing work.
Competing communication traffic can saturate the available network bandwidth, further aggravating performance problems. Bandwidth latency is the time it takes to send a minimal 0 byte message from point A to point B. Commonly expressed as microseconds.
Sending many small messages can cause latency to dominate communication overheads. Often it is more efficient to package small messages into a larger message, thus increasing the effective communications bandwidth.
Visibility of communications With the Message Passing Model, communications are explicit and generally quite visible and under the control of the programmer. With the Data Parallel Model, communications often occur transparently to the programmer, particularly on distributed memory architectures.
The programmer may not even be able to know exactly how inter-task communications are being accomplished. This can be explicitly structured in code by the programmer, or it may happen at a lower level unknown to the programmer.
Synchronous communications are often referred to as blocking communications since other work must wait until the communications have completed. Asynchronous communications allow tasks to transfer data independently from one another.
For example, task 1 can prepare and send a message to task 2, and then immediately begin doing other work. When task 2 actually receives the data doesn't matter. Asynchronous communications are often referred to as non-blocking communications since other work can be done while the communications are taking place.
Interleaving computation with communication is the single greatest benefit for using asynchronous communications.
Scope of communications Knowing which tasks must communicate with each other is critical during the design stage of a parallel code.
Both of the two scopings described below can be implemented synchronously or asynchronously. Collective - involves data sharing between more than two tasks, which are often specified as being members in a common group, or collective. Some common variations there are more: Efficiency of communications Oftentimes, the programmer has choices that can affect communications performance.
Only a few are mentioned here. Which implementation for a given model should be used? Using the Message Passing Model as an example, one MPI implementation may be faster on a given hardware platform than another.
What type of communication operations should be used? As mentioned previously, asynchronous communication operations can improve overall program performance. Network fabric - different platforms use different networks. Some networks perform better than others. Choosing a platform with a faster network may be an option.
Overhead and Complexity Finally, realize that this is only a partial list of things to consider!!! Designing Parallel Programs Synchronization Managing the sequence of work and the tasks performing it is a critical design consideration for most parallel programs.Fibonacci Series or Sequence in C++ Today in C++ Tutorial we will learn How to write C++ program to find Fibonacci Series.
The Fibonacci Sequence can be written as 0,1,1,2,3,5,8, What is Fibonacci Series or Sequence. Write a c program to subtract two numbers without using subtraction operator.
Write a c program to find largest among three numbers using binary minus operator. The solution below will answer following questions: 1. Write a program to generate the Fibonacci series in C. 2. Fibonacci series in C using for loop.
Jun 10, · Top 30 Programming interview questions Programming questions are an integral part of any Java or C++ programmer or software analyst interview. No matter on which language you have expertise it’s expected that you are familiar with fundamental of programming and can solve problems without taking help of API.
Jul 30, · I want to get first 10 Fibonacci numbers, but something is wrong in this program, need help. Write a program in C++ programming language to print fibonacci number using for loop and using recursion with sample input and output.
C++ Program to Display Fibonacci Series using Loop and Recursion.