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Parallel Processing

Oracle RAC Cluster Tips by Burleson Consulting

This is an excerpt from the bestselling book Oracle Grid & Real Application Clusters.  To get immediate access to the code depot of working RAC scripts, buy it directly from the publisher and save more than 30%.


Parallel processing is a vital part of any high performance computing model. It involves the utilization of large amounts of computing resources to complete a complex task or problem. The resources specific to parallel processing are CPU and memory. Originally confined to use in scientific applications, parallel processing has quickly made inroads into commercial and business applications that need high performance computing facilities like data mining, decision support, and risk management applications.

Parallel execution or processing involves the division of a task into several smaller tasks and making the system work on each of these smaller tasks in parallel. In simple terms, if multiple processors engage a computing task, it is generally executed faster. Parallel processing thereby improves response time and increases throughput by utilizing all of the available computing resources. Parallel execution helps systems scale performance by making optimal use of the hardware resources. In a parallel processing system, multiple processes may reside on a single computer or may be spread across several computers or nodes, as in an Oracle10g RAC cluster.

Some basic requirements for achieving parallel execution and better performance are:

* Computer system/servers with built in multiple processors and better message facilitation among processors.

* Operating system capable of managing the multiple processors.

* Clustered nodes with application software, such as Oracle RAC, that can parallel across the nodes.

There are many distinct benefits and advantages of utilizing parallel execution, such as:

* Better response times:  As the computing tasks are engaged by a group of processors, the tasks are completed in a smaller amount of time.

* Higher Throughput:  Parallel processing results in faster execution of tasks, increasing throughput. A large number of tasks can be performed in a given unit of time.

* Better Price/Performance:  It is usually more expensive to make one very fast single CPU instead of using several slower ones.

Types of Parallelism

There are many types of parallelism. Some of the types are as follows:

* Pipeline Parallelism:  In this type of processing, long sequences of operations, or tasks, are parallel, but there are also overlapping sequential processes during which no parallel tasks are possible. The relational model fits into this model very well. The output of some relational operators becomes the input for other operators; therefore, some waiting time is involved. There is a considerable amount of time saved in the completion of a task through the proper use of pipeline parallelism.

* Independent or Natural Parallelism:   - In this type of parallelism, the tasks do not depend on other tasks. As a result, total execution time is considerably reduced. Sometimes this type is called ?embarrassingly parallel.?

* Inter-query and Intra-query Parallelism:   - Transactions are independent. No transaction requires the output of another transaction to complete. Many CPUs can be kept busy by assigning each task or query to a separate CPU. This type of parallelism, utilizing many separate, independent queries at the same time, is called inter-query parallelism. This is a natural solution for OLTP-type operations. Even some small DSS operations work this way. Hence, the greater the number of CPUs available on a database server, the better the performance will be, in most situations. To speed up the execution of a single large and complex query, this model decomposes it into smaller problems.  It then executes these smaller tasks concurrently by assigning them separate CPUs. This type of parallelism is a natural solution for DSS-type operations where a single transaction analyzes, computes, and updates thousands of database blocks.

 


This is an excerpt from the bestselling book Oracle Grid & Real Application Clusters, Rampant TechPress, by Mike Ault and Madhu Tumma.

You can buy it direct from the publisher for 30%-off and get instant access to the code depot of Oracle tuning scripts.

http://www.rampant-books.com/book_2004_1_10g_grid.htm


 

 
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