Parallel Execution Profiles

Abstract

Observing the relative behavior of an application's threads is critical to identifying performance bottlenecks and understanding their root causes. We present parallel execution profiles (PEPs), which capture the relative behavior of parallel threads in terms of the user selected code regions they execute. The user annotates the program to identify code regions of interest. The PEP divides the execution time of a multithreaded application into time intervals or a sequence of frames during which the code regions being executed in parallel by application threads remain the same. PEPs can be easily analyzed to compute execution times spent by the application in interesting behavior states. This helps user understand the severity of common performance problems such as excessive waiting on events by threads, threads contending for locks, and the presence of straggler threads.

Tails in the cloud: a survey and taxonomy of straggler management within large-scale cloud data centres

“…Stragglers can occur due to information obfuscation at different levels of the system. Literature [43] [14] [45] [95] has identified that information can be hidden at two different levels: i) OS Level and ii) Application Level. During the execution of resources, the master node (controller) hides information from workers (cluster nodes) at OS level.…”