Category Archives: Performance
Persistent Feeders can dramatically decrease the time required to restart a TM1 model. Here is an explanation of what Persistent Feeders are and instructions on how to implement them in your TM1 model.
Overfeeding is where TM1 feeders are targetting more cells than are required. This can lead to excess memory consumption and reduce performance. Here we explain over feeding and how to avoid it.
Dimension order within cubes in TM1 is important not only for performance, but also for usability. When should you set the order for usability and when for performance? And how do you optimise the dimension order for performance? All these questions and more are addressed in this post from ExploringTM1.
This is a fabulous article from our friends at Ironside in the US. The original article is available here. There are various approaches available to enhance the performance of your TM1 application. One such approach is to leverage Stargate Views. Stargate Views are different from traditional view objects and represent cached subsections of cubes that get created through user experience. As users browse cubes via the Cube Viewer or consume cube … Continue Reading
Much Faster Processing with MTQ in TM1 TM1 traditionally uses a single core or thread per task. What this means is that when a user opens a view for a cube (or a TI or Cognos BI opens a view), that view is created by a single core. Recently IBM changed TM1 so that it now can use multi threaded queries and it is almost a linear improvement in performance, roughly … Continue Reading