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.
OpenACC is a set of C and Fortran extensions, or , designed to simplify off-loading of tasks to accelerators. Version 1.0 of the specification was released in November 2011 @cite_12 . It defines a set of functions and compiler to specify parts of a program whose computation may be offloaded to GPUs, to transfer data between main memory and the GPUs, and to synchronize with the execution of those parts---the .
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In this report, we present X-Kaapi's programming model. A X-Kaapi parallel program is a C or C++ sequential program with code annotation using #pragma compiler directives that allow to create tasks. A specific source to source compiler translates X-Kaapi directives to runtime calls.