Task parallelism and high-performance languages

The definition of High Performance Fortran (HPF) is a significant event in the maturation of parallel computing: it represents the first parallel language that has gained widespread support from vendors and users. The subject of this paper is to incorporate support for task parallelism. The term task parallelism refers to the explicit creation of multiple threads of control, or tasks, which synchronize and communicate under programmer control. Task and data parallelism are complementary rather than competing programming models. While task parallelism is more general and can be used to implement algorithms that are not amenable to data-parallel solutions, many problems can benefit from a mixed approach, with for example a task-parallel coordination layer integrating multiple data-parallel computations. Other problems admit to both data- and task-parallel solutions, with the better solution depending on machine characteristics, compiler performance, or personal taste. For these reasons, we believe that a general-purpose high-performance language should integrate both task- and data-parallel constructs. The challenge is to do so in a way that provides the expressivity needed for applications, while preserving the flexibility and portability of a high-level language. In this paper, we examine and illustrate the considerations that motivate the use of task parallelism. We also describe one particular approach to task parallelism in Fortran, namely the Fortran M extensions. Finally, we contrast Fortran M with other proposed approaches and discuss the implications of this work for task parallelism and high-performance languages

Towards an Adaptive Skeleton Framework for Performance Portability

The proliferation of widely available, but very different, parallel architectures makes the ability to deliver good parallel performance on a range of architectures, or performance portability, highly desirable. Irregularly-parallel problems, where the number and size of tasks is unpredictable, are particularly challenging and require dynamic coordination. The paper outlines a novel approach to delivering portable parallel performance for irregularly parallel programs. The approach combines declarative parallelism with JIT technology, dynamic scheduling, and dynamic transformation. We present the design of an adaptive skeleton library, with a task graph implementation, JIT trace costing, and adaptive transformations. We outline the architecture of the protoype adaptive skeleton execution framework in Pycket, describing tasks, serialisation, and the current scheduler.We report a preliminary evaluation of the prototype framework using 4 micro-benchmarks and a small case study on two NUMA servers (24 and 96 cores) and a small cluster (17 hosts, 272 cores). Key results include Pycket delivering good sequential performance e.g. almost as fast as C for some benchmarks; good absolute speedups on all architectures (up to 120 on 128 cores for sumEuler); and that the adaptive transformations do improve performance

A domain-specific high-level programming model

International audienceNowadays, computing hardware continues to move toward more parallelism and more heterogeneity, to obtain more computing power. From personal computers to supercomputers, we can find several levels of parallelism expressed by the interconnections of multi-core and many-core accelerators. On the other hand, computing software needs to adapt to this trend, and programmers can use parallel programming models (PPM) to fulfil this difficult task. There are different PPMs available that are based on tasks, directives, or low level languages or library. These offer higher or lower abstraction levels from the architecture by handling their own syntax. However, to offer an efficient PPM with a greater (additional) high-levelabstraction level while saving on performance, one idea is to restrict this to a specific domain and to adapt it to a family of applications. In the present study, we propose a high-level PPM specific to digital signal processing applications. It is based on data-flow graph models of computation, and a dynamic runtime model of execution (StarPU). We show how the user can easily express this digital signal processing application, and can take advantage of task, data and graph parallelism in the implementation, to enhance the performances of targeted heterogeneous clusters composed of CPUs and different accelerators (e.g., GPU, Xeon Phi

Holographic and 3D teleconferencing and visualization: implications for terabit networked applications

Abstract not available