ErLaDeF - Embedded Realtime Language Development Framework

Director:Philippsen, M.
Period:January 1, 2012 - December 31, 2014
Coworker:Veldema, R.; Blaß, T.; Krainz, J.; Brinkers, D.

ErLaDeF is our test-bed for new programming language and compiler techniques. Our main focus is on building infrastructure for easier (hard + soft) real-time embedded parallel systems programming.
We focus on hard real-time embedded systems as they are about to go massively parallel in the near future.
Real-time and embedded systems also have hard constraints on resource usage. For example, a task should complete in a fixed amount of time, have guaranteed upper-limits on the amount of memory used, etc.
We are developing different ways to manage this concurrency using a combination of strategies: simpler language features, automatic parallelization, libraries of parallel programming patterns, deep compiler analysis, model checking, and making compiler analysis fast enough for interactive use.

Runtime Parallelization of Programs
Our automatic parallelization efforts are currently focused on dynamic parallelization. While a program is running, it is analyzed to find loops where parallelization can help performance. Our current idea is to run long-running loops three times. The first two runs analyze the memory accesses of the loop and can both run in parallel. The first run stores in a shared data structure for every memory address in which loop iteration a write access happens. We do not need any synchronization for this data structure, only the guarantee that one value is written to memory, when two concurrent writes happen. In the second pass we check for every memory access, if it has a dependency to one of the stored write accesses. A write access is part of any data-dependency, so we can find all types of data dependencies. If we do not find any, the loop is actually run in parallel. If we find dependencies, the loop is executed sequentially. We can execute the analyses in parallel to a modified sequential execution of the first loop iterations.
In 2014 we enhanced the analysis so that a loop can start running while the remainder of the loop is analyzed to see if it can be run in parallel. To allow the sequential loop to execute while the tail of the loop is analyzed we needed to instrument the sequential loop slightly. The result is that the loop runs only slightly slower if the loop cannot be parallelized, but if the loop is found to be parallelizable, speedup is near to linear.
Finally, we also created a new language that uses the above library for run-time parallelization. Any loops that the programmer marked as candidates for run-time parallelization are analyzed for constructs that the library cannot yet handle. If the loop is clean, code is generated that uses the library's macros.

Script based language for embedded systems (Pylon)
Pylon is a language that is close to scripting languages (but is statically typed). A large part of the complexity that a programmer would normally take care of when creating an application, is moved to the compiler (i.e., type inference). The programmer does not have to think about types at all. By analyzing the expressions in the program, types are inferred (duck typing). The language is also implicitly parallel, the programmer does not need to have expert knowledge to parallelize an application. The compiler automatically decides what to run in parallel. Finally, the language is kept simple so that learning the language remains easy for novice programmers. For example, we kept the number of keywords small.
Any language constructs that make analyzing the program hard for a compiler has been omitted (pointer arithmetic, inheritance, etc.). Any removed features have been replaced by simpler variants that can be easily analyzed. The current focus of this project lies in supporting the programmer in designing the code. The previous programming language research results have been absorbed in the Pylon project. For example, the prior research results on alternatives to polymorphism and inheritance have been added to the Pylon project. This allows us to report errors at compile time where other languages can only find them at run-time.

Object-oriented languages offer the possibility to dynamically allcoate objects. The memory required for this is allocated at run-tie. However, in contrast to desktop systems, embedded systems typically have very little memory. If the 'new' operator is used often in an embedded system (that are now starting to be programmed with higher-level languages such as Java and C++ that include 'new'), memory can be exhausted at run-time causing an embedded system to crash.
In 2014, we created an analysis to find this problem at compile-time and report this to the developer.
To detect memory exhaustion at compile-time, the analysis determines the live- time of references to objects. If there are no more references to an object, the object can be removed from memory. Normally, such reference counting schemes are performed at run- time, we, however, perform reference counting at compile-time in an interactive fashion. The result is that memory management errors can be found at compile-time instead of at run-time. Additionally, the static reference counting increases a program's performance as the reference counts do not have to be manipulated at run-time. If it is statically determined that an object can be removed, the developer needs to insert a 'delete' statement. With explicit memory management, we are now able to statically determine a program's worst-case memory requirements. The whole analysis outlined above is integrated into Pylon and a predicate propagation framework previously reported on. Note that the analysis is language independent, however, and can be applied to other languages as well (Java, C++, etc.). However, we can in that case no guarantee that reference counts are correctly computed as we require Pylon's analyzability for this.
In 2014, we also have continued to work on interactive program analysis. The framework we developed in 2013 to do program analysis in a lazy manner, suitable for interactive use, was expanded and modified in order to support big code bases, to analyze them and keep the analysis results for later use. This enables us to precisely analyze programs that use libraries, by first analyzing the library, and then using the library's analysis result to get precise analysis results for our program.

The ErLaDeF project is a contribution of the Chair of Computer Science 2 (Programming Systems) to the IZ ESI (Embedded Systems Initiative,

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