How Parallel Processing in the Foreground Can Improve Performance

  • by Sharad Agarwal, Team Lead, Infosys
  • Sameer Sinha, Senior Project Manager, Infosys
  • November 19, 2012
Discover how you can use parallel processing techniques in the foreground to process large amounts of data and improve your SAP CRM system performance.
Key Concept

In SAP CRM, you use foreground work process distribution to make reports and programs faster or to reduce the processing time for complex dialog programs. With it, you can implement parallel processing when you have a set of independent tasks to be executed. Each task starts in parallel, and when all the tasks are finished, you can collect the results (such as a table of information, a data structure, or a data field) from each of the parallel tasks and present them to the user or your organization.

It’s a common scenario: You want to load or process huge volumes of data in SAP CRM or need to improve the performance of a program that is taking hours to run. These situations often occur during initial project setup when data migration from a legacy system to SAP CRM occurs or when increasing volumes of data in SAP CRM cause a program to run for hours when it initially took minutes. This is where parallel processing techniques come in handy for improving system performance. There are two types of parallel processing techniques you can apply:

  • Foreground work process distribution
  • Background work process distribution

In this article, we cover the foreground work process. In ABAP parlance, every report program takes up one dialog work process for its execution and has a sequential execution plan. For example, every statement waits for the completion of the previous one, and therefore the total execution time is the sum of the individual execution times taken by each ABAP statement. If you can find places in the code (i.e., the group of ABAP statements) and create multiple instances of this group, assigning each to dialog work processes running in parallel, then you can reduce processing time by a factor of the number of dialog work processes running in parallel. There are some limitations to this approach that we also cover.

Sharad Agarwal

Sharad Agarwal has a degree in computer science engineering and works as an SAP techno-functional consultant in SAP CRM and SAP IS-Utilities (gas and electricity). He has eight years of work experience and has worked across multiple geographies and industry verticals, providing SAP technical consulting in two end-to-end implementation and three support projects.

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Sameer Sinha

Sameer Sinha has a degree in electronics and communication engineering and has 11 years of experience with SAP. He is an SAP techno-functional consultant in SAP CRM currently serving as a senior project manager and has worked across multiple geographies and industry verticals.

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