PATESIA - Parallelization techniques for embedded systems in automation

Director:Philippsen, M.
Period:June 1, 2009 - December 31, 2014
Coworker:Kempf, S.; Veldema, R.; Blaß, T.

This project was launched in 2009 to address the refactorization and parallelization of applications used in the field of industry automation. These programs are executed on specially designed embedded systems. This hardware forms an industry standard and is used worldwide. As multicore-architectures are increasingly used in embedded systems, existing sequential software must be parallelized for these new architectures in order to improve performance. As these programs are typically used in the industrial domain for the control of processes and factory automation they have a long life cycle. Because of this, the programs often are not being maintained by their original developers any more. Besides that, a lot of effort was spent to guarantee that the programs work reliably. For these reasons the software is only extended in a very reluctant way.
Therefore, a migration of these legacy applications to new hardware and a parallelization cannot be done manually, as it is too error prone. Thus, we need tools that perform these tasks automatically or aid the developer with the migration and parallelization.

Research on parallelization techniques
We developed a special compiler for the parallelization of existing automation programs. First, we examined automation applications with respect to automatic parallelizability. We found that it is hard to perform an efficient automatic parallelization with existing techniques. Therefore this part of the project focuses on two steps to handle this situation. As first step, we developed a data dependence analysis that identifies potential critical sections in a parallel program, presents them to the programmer and adds their protection to the code. We ware able to show that our approach to identify critical sections finds atomic blocks that closely match the atomic blocks that an expert would add to the code. Besides that, we showed in 2014 that the impacts on execution times are negligible if our technique finds atomic blocks that are larger than necessary or that are not necessary at all.
As second step we have refined and enhanced existing techniques (software transactional memory (STM) and lock inference) to implement atomic blocks. In our approach, an atomic block uses STM as long as lock inference would lead to coarse-grained synchronization. The atomic block switches from STM to lock inference as soon as a fine-grained synchronization is possible. With this technique, an atomic block always uses fine-grained synchronization while the runtime overhead of STM is minimized at the same time. We showed that (compared to a pure STM or lock inference implementation) our technique speeds up execution times by a factor between 1.1 and 6.3. Although fine-grained synchronization in general leads to better performance than a coarse-grained solution, there are cases where a coarse-grained implementation shows equal performance. We therefore presented a runtime mechanism for an STM that also works together with our combined technique. This runtime mechanism starts with a small number of locks, i.e., a coarse-grained locking, where accesses to different shared variables are protected by the same lock. If this coarse-grained locking leads to too many non-conflicting accesses waiting for the same lock, our approach gradually increases the number of locks. This makes the locking more fine-grained so that non-conflicting accesses can be executed concurrently. Our runtime mechanism that dynamically tunes the locking-granularity makes the programs run up to 3.0 times faster than a fixed coarsegrained synchronization.
We completed this project part in 2014.

Research on migration techniques
Our research on the migration of legacy applications originally consisted of having a tool that automatically replaces suboptimal code constructs with better code. The code sequences that had to be replaced as well as the replacement codes were specified by developers by means of a newly developed pattern description language. However, we found this approach to be too difficult for novice developers.
This led us to the development of a new tool that automatically learns and generalizes patterns from source code archives, recognizes them in other projects, and presents recommendations to developers. The foundation of our tool lies in the comparison of two versions of the same program. It extracts the changes that were made between two source codes, derives generalized patterns of suboptimal and better code from these changes, and saves the patterns in a database. Our tool then uses these patterns to suggest similar changes for the source code of different programs.
In 2014 we developed a new symbolic code execution engine to minimize the number of wrong recommendations. Depending on the number and the generality of the patterns in the database, it is possible that without the new engine our tool generates some unfitting recommendations. To discard the unfitting ones, we compare the summary of the semantics/behavior of the recommendation with summary of the semantics/behavior of the database pattern. If both differ too severely, our tool drops the recommendation from the results. The distinctive features of our approach are its applicability to isolated code fragments and its automatic configuration that does not require any human interaction.
In 2015 we improved the detection of similar code changes. In a first step, we evaluated whether clustering algorithms are suitable to replace the currently used naive approach to identify similar code changes. To this end we developed heuristics and metrics to enable an automatic grouping of similar code changes. Using these we compared the results of different clustering algorithms. In this way we could clearly improve the results compared to the previously used approach. The aim of the second improvement is to automatically refine the resulting groups of similar code changes. For this purpose we evaluated several machine learning algorithms for outlier detection to remove those code changes that have been spuriously assigned to a group.
The latest results of our tool SIFE are found here (last update: 2014-05-09).

Parts of the project are funded by the "ESI-Anwendungszentrum"

ESI Anwendungszentrum
watermark seal