Modern platforms used for high-performance computing (HPC) include machines with both general-purpose CPUs, and "accelerators", often in the form of graphical processing units (GPUs). StarPU is a C library to exploit such platforms. It provides users with ways to define "tasks" to be executed on CPUs or GPUs, along with the dependencies among them, and by automatically scheduling them over all the available processing units. In doing so, it also relieves programmers from the need to know the underlying architecture details: it adapts to the available CPUs and GPUs, and automatically transfers data between main memory and GPUs as needed. While StarPU's approach is successful at addressing run-time scheduling issues, being a C library makes for a poor and error-prone programming interface. This paper presents an effort started in 2011 to promote some of the concepts exported by the library as C language constructs, by means of an extension of the GCC compiler suite. Our main contribution is the design and implementation of language extensions that map to StarPU's task programming paradigm. We argue that the proposed extensions make it easier to get started with StarPU,eliminate errors that can occur when using the C library, and help diagnose possible mistakes. We conclude on future work.
The authors present Jade, a high-level parallel programming language for managing course-grain concurrency. Jade simplifies programming by providing the programmer with the abstractions of sequential execution and a shared address space. Jade programmers augment sequential, imperative programs with constructs that declare how parts of the program access data; the Jade implementation dynamically interprets this information to execute the program in parallel. This parallel execution preserves the serial semantics of the original program. Jade has been implemented as an extension to C on shared-memory multiprocessors, a message-passing machine, networks of heterogeneous workstations, and systems with special-purpose functional units. Programs written in Jade run on all of these platforms without modification. > Jade is a portable, implicitly parallel language designed for exploiting task-level concurrency.Jade programmers start with a program written in a standard serial, imperative language, then use Jade constructs to declare how parts of the program access data. The Jade implementation uses this data access information to automatically extract the concurrency and map the application onto the machine at hand. The resulting parallel execution preserves the semantics of the original serial program. We have implemented Jade as an extension to C, and Jade implementations exist for s hared-memory multiprocessors, homogeneous message-passing machines, and heterogeneous networks of workstations. In this atricle we discuss the design goals and decisions that determined the final form of Jade and present an overview of the Jade implementation. We also present our experience using Jade to implement several complete scientific and engineering applications. We use this experience to evaluate how the different Jade language features were used in practice and how well Jade as a whole supports the process of developing parallel applications. We find that the basic idea of preserving the serial semantics simplifies the program development process, and that the concept of using data access specifications to guide the parallelization offers significant advantages over more traditional control-based approaches. We also find that the Jade data model can interact poorly with concurrency patterns that write disjoint pieces of a single aggregate data structure, although this problem arises in only one of the applications.