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A framework for large-scale parallel processing Paul Krzyzanowski November Introduction Traditional programming tends to be serial in design and execution. We tackle many problems with a sequential, stepwise approach and this is reflected in the corresponding program. With parallel programming, we break up the processing workload into multiple parts, that can be executed concurrently on multiple processors.
Not all problems can be parallelized. The challenge is to identify as many tasks as possible that can run concurrently.
Alternatively, we can identify data groups that can be processed concurrently.
This will allow us to divide the data among multiple concurrent tasks. The most straightforward situation that lends itself to parallel programming is one where there is no dependency among data. Data can be split into chunks and each process can be assigned a chunk to work on.
If we have lots of processors, we can split the data into lots of chunks. It identifies the data, splits it up based on the number of available workers, and assigns a data segment to each worker.
A worker receives the data segment from the master, performs whatever processing is needed on the data, and then sends results to the master.
At the end, the master does whatever final processing e. The name is inspired from map and reduce functions in the LISP programming language. In LISP, the map function takes as parameters a function and a set of values.
That function is then applied to each of the values. Since length returns the length of an item, the result of map is a list containing the length of each item: It combines all the values together using the binary function. The reduce operation can take place only after the map is complete.
MapReduce is not an implementation of these LISP functions; they are merely an inspiration and etymological predecessor. MapReduce MapReduce is a framework for parallel computing.
Programmers get a simple API and do not have to deal with issues of parallelization, remote execution, data distribution, load balancing, or fault tolerance. The framework makes it easy for one to use thousands of processors to process huge amounts of data e.
The Map function reads a stream of data and parses it into intermediate key, value pairs. When that is complete, the Reduce function is called once for each unique key that was generated by Map and is given the key and a list of all values that were generated for that key as a parameter.
The keys are presented in sorted order.
As an example of using MapReduce, consider the task of counting the number of occurrences of each word in a large collection of documents. The user-written Map function reads the document data and parses out the words.
For each word, it writes the key, value pair of word, 1. That is, the word is treated as the key and the associated value of 1 means that we saw the word once.
Since the only values are the count of 1, Reduce is called with a list of a "1" for each occurence of the word that was parsed from the document. The function simply adds them up to generate a total word count for that word. EmitIntermediate w, "1" ; reduce String key, Iterator values: A worker may be assigned a role of either a map worker or a reduce worker.
Split input Figure 1. Split input into shards The first step, and the key to massive parallelization in the next step, is to split the input into multiple pieces. Each piece is called a split, or shard.
For M map workers, we want to have M shards, so that each worker will have something to work on. The number of workers is mostly a function of the amount of machines we have at our disposal. The MapReduce library of the user program performs this split.
The actual form of the split may be specific to the location and form of the data. MapReduce allows the use of custom readers to split a collection of inputs into shards, based on specific format of the files.
Fork processes Figure 2. Remotely execute worker processes The next step is to create the master and the workers.RICE UNIVERSITY A Storage Architecture for Data-Intensive Computing by Jeffrey Shafer A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE.
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Master Thesis Mapreduce master thesis mapreduce In many cases they include a test plan, test case, defect documentation master thesis mapreduce and status report Lawrence Edward Page (born March 26,Master thesis mapreduce. Partial fulfillment of declarative query using science meng. Abstract. MapReduce is an effective tool for processing large amounts of data in parallel using a cluster of processors or computers.
One common data processing task is the join operation, which combines two or more datasets based on values common to each.
Parallel Computing using Open Science Grid Compared to MapReduce Grid. Abstract: Parallel Computing is a type of computation during which many calculations square measure allotted simultaneously, in operation on the principle that enormous problems can typically be divided into smaller problems, which are then solved at the same time .
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