You are required to read and agree to the below before accessing a full-text version of an article in the IDE article repository.

The full-text document you are about to access is subject to national and international copyright laws. In most cases (but not necessarily all) the consequence is that personal use is allowed given that the copyright owner is duly acknowledged and respected. All other use (typically) require an explicit permission (often in writing) by the copyright owner.

For the reports in this repository we specifically note that

  • the use of articles under IEEE copyright is governed by the IEEE copyright policy (available at
  • the use of articles under ACM copyright is governed by the ACM copyright policy (available at
  • technical reports and other articles issued by M‰lardalen University is free for personal use. For other use, the explicit consent of the authors is required
  • in other cases, please contact the copyright owner for detailed information

By accepting I agree to acknowledge and respect the rights of the copyright owner of the document I am about to access.

If you are in doubt, feel free to contact

Automatic Inference of Task Parallelism in Task-graph-based Actor Models

Publication Type:

Journal article


Journal of IEEE Access




Automatic inference of task level parallelism is fundamental for ensuring many kinds of safety and liveness properties of parallel applications. For example, two tasks running in parallel may be involved in data races when they have conflicting memory accesses, or one is affecting the termination of another by updating shared variables. In this article, we have considered a task-graph-based actor model, used in signal processing applications (e.g., baseband processing in wireless communication, LTE uplink processing) that are deployed on many-core platforms, in which actors, task-graphs and tasks are the active entities running in parallel. Actors invoke task graphs, which in turn invoke tasks, and they communicate through message passing, thus creating different kinds of dependencies and parallelism in the application.We introduce a novel May Happen in Parallel (MHP) analysis for complex parallel applications based on our computational model. The MHP analysis consists of (i) data-flow analysis applicable to parallel control-flow structures inferring MHP facts representing pairs of tasks running in parallel, (ii) identification of all direct and indirect communication by generating a context-free grammar and enumerating valid strings representing parallelism and dependencies among active entities, and (iii) inferring MHP facts when multiple task-graphs communicate. Our analysis is applicable to other computational models (e.g. Cilk or X10) too. We have fully implemented our analysis and evaluated it on signal processing applications consisting of a maximum of 36.57 million lines of code representing 232 different tasks. The analysis approximately 7 minutes to identify all communication information and 10.5 minutes to identify 12052 executable parallel task-pairs (to analyse for concurrency bugs) proving that our analysis is scalable for industrial-sized code-bases.


author = {Abu Naser Masud and Bj{\"o}rn Lisper and Federico Ciccozzi},
title = {Automatic Inference of Task Parallelism in Task-graph-based Actor Models},
isbn = {2169-3536},
volume = {99},
pages = {1--27},
month = {December},
year = {2018},
journal = {Journal of IEEE Access},
url = {}