Table of Contents

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  1. Preface
  2. Analyst Service
  3. Catalog Service
  4. Content Management Service
  5. Data Integration Service
  6. Data Integration Service Architecture
  7. Data Integration Service Management
  8. Data Integration Service Grid
  9. Data Integration Service REST API
  10. Data Integration Service Applications
  11. Data Privacy Management Service
  12. Enterprise Data Preparation Service
  13. Interactive Data Preparation Service
  14. Informatica Cluster Service
  15. Mass Ingestion Service
  16. Metadata Access Service
  17. Metadata Manager Service
  18. Model Repository Service
  19. PowerCenter Integration Service
  20. PowerCenter Integration Service Architecture
  21. High Availability for the PowerCenter Integration Service
  22. PowerCenter Repository Service
  23. PowerCenter Repository Management
  24. PowerExchange Listener Service
  25. PowerExchange Logger Service
  26. SAP BW Service
  27. Search Service
  28. System Services
  29. Test Data Manager Service
  30. Test Data Warehouse Service
  31. Web Services Hub
  32. Application Service Upgrade
  33. Appendix A: Application Service Databases
  34. Appendix B: Connecting to Databases from Windows
  35. Appendix C: Connecting to Databases from UNIX or Linux
  36. Appendix D: Updating the DynamicSections Parameter of a DB2 Database

CPU Usage

CPU Usage

The PowerCenter Integration Service process performs read, transformation, and write processing for a pipeline in parallel. It can process multiple partitions of a pipeline within a session, and it can process multiple sessions in parallel.
If you have a symmetric multi-processing (SMP) platform, you can use multiple CPUs to concurrently process session data or partitions of data. This provides increased performance, as true parallelism is achieved. On a single processor platform, these tasks share the CPU, so there is no parallelism.
The PowerCenter Integration Service process can use multiple CPUs to process a session that contains multiple partitions. The number of CPUs used depends on factors such as the number of partitions, the number of threads, the number of available CPUs, and amount or resources required to process the mapping.