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removed by UEs until the computation is complete The overall program structure can be based on either the Master/Worker pattern or the SPMD pattern The former is particularly appropriate for problems requiring a dynamic schedule In the case in which the computation can terminate before all the tasks are complete, some care must be taken to ensure that the computation ends when it should If we define the termination condition as the condition that when true means the computation is complete either all tasks are complete or some other condition (for example, an acceptable solution has been found by one task) then we want to be sure that (1) the termination condition is eventually met (which, if tasks can be created dynamically, might mean building into it a limit on the total number of tasks created), and (2) when the termination condition is met, the program ends How to ensure the latter is discussed in the Master/Worker and SPMD patterns

def add_person_details(ssn, surname, **kwargs): print("SSN =", ssn) print(" surname =", surname) for key in sorted(kwargs): print(" {0} = {1}"format(key, kwargs[key]))

We look for nouns to help us find entities Nouns are people, places, and things Most entities represent a collection of things, specifically physical things that we work with It is for this reason that nouns are a great identifier of entities Let s say a user tells you that the company has several sites and each site has at least ten employees You can use the nouns to start an entity list; in this case, the nouns are site and employees You have now determined that you will need a Site and an Employee entity in the data model

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This function could be called with just the two positional arguments, or with additional information, for example, add_person_details(83272171, "Luther", forename="Lexis", age=47) This provides us with a lot of exibility And we

can of course accept both a variable number of positional arguments and a variable number of keyword arguments:

Most problems for which this pattern is applicable fall into the following two categories Embarrassingly parallel problems are those in which there are no dependencies among the tasks A wide range of problems fall into this category, ranging from rendering frames in a motion picture to statistical sampling in computational physics Because there are no dependencies to manage, the focus is on scheduling the tasks to maximize efficiency In many cases, it is possible to define schedules that automatically and dynamically balance the load among UEs Replicated data or reduction problems are those in which dependencies can be managed by "separating them from the tasks" as described earlier replicating the data at the beginning of computation and combining results when the termination condition is met (usually "all tasks complete") For these problems, the overall solution consists of three phases, one to replicate the data into local variables, one to solve the now independent tasks (using the same techniques used for embarrassingly parallel problems), and one to recombine the results into a single result Examples We will consider two examples of this pattern The first example, an image construction example, is embarrassingly parallel The second example will build on the molecular dynamics example used in several of the Finding Concurrency patterns

def print_args(*args, **kwargs): for i, arg in enumerate(args): print("positional argument {0} = {1}"format(i, arg)) for key in kwargs: print("keyword argument {0} = {1}"format(key, kwargs[key]))

This function just prints the arguments it is given It can be called with no arguments, or with any number of positional and keyword arguments

Like entities, attributes are described as nouns, but the key difference is that an attribute does not describe more than a single piece of data For example, if a customer describes a vehicle, you will likely want to know more about the information he needs about the vehicle When a customer describes the vehicle identification number (VIN) for a vehicle, there isn t much more detail to be had Vehicle is an entity, and VIN is an attribute When we look for attributes, we also need to look for applied ownership of information Words like own, have, contain, or belong are your biggest clues that you might have a few attributes being described Ownership can describe a relationship when it s ownership between two entities, so make sure you don t turn entities into attributes and vice versa Phrases like Students have a unique student ID number indicate that students own student IDs, and hence a student ID is one attribute of a student You also need to look for phrases like, For customers we track x, y, and z Tracking something about an entity is often a flag that the something is an attribute

In many image construction problems, each pixel in the image is independent of all the other pixels For example, consider the well known Mandelbrot set [Dou86] This famous image is constructed by coloring each pixel according to the behavior of the quadratic recurrence relation Equation 41

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