Over the last twenty years, the open source community has provided more and more software on which the world's High Performance Computing (HPC) systems depend for performance and productivity. The community has invested millions of dollars and years of effort to build key components. But although the investments in these separate software elements have been tremendously valuable, a great deal of productivity has also been lost because of the lack of planning, coordination, and key integration of technologies necessary to make them work together smoothly and efficiently, both within individual PetaScale systems and between different systems. It seems clear that this completely uncoordinated development model will not provide the software needed to support the unprecedented parallelism required for peta/exascale computation on millions of cores, or the flexibility required to exploit new hardware models and features, such as transactional memory, speculative execution, and GPUs. This report describes the work of the community to prepare for the challenges of exascale computing, ultimately combing their efforts in a coordinated International Exascale Software Project.

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Exploiting the full performance potential of distributed memory machines requires a careful distribution of data across the processors. Vienna Fortran is a language extension of Fortran which provides the user with a wide range of facilities for such mapping of data structures. In contrast to current programming practice, programs in Vienna Fortran are written using global data references. Thus, the user has the advantages of a shared memory programming paradigm while explicitly controlling the data distribution. In this paper, we present the language features of Vienna Fortran for FoRTRAN 77, together with examples illustrating the use of these features

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This report captures and expands the outcomes of this workshop. In the context of extreme heterogeneity, it defines basic research needs and opportunities in computer science research to develop smart and trainable operating and runtime systems, programming environments, and predictive tools that will make future systems easier to adapt to scientists' computing needs and easier for facilities to deploy securely.

Vienna Fortran, High Performance Fortran (HPF), and other data parallel languages have been introduced to allow the programming of massively parallel distributed-memory machines (DMMP) at a relatively high level of abstraction, based on the SPMD paradigm. Their main features include directives to express the distribution of data and computations across the processors of a machine. In this paper, we use Vienna-Fortran as a general framework for dealing with sparse data structures. We describe new methods for the representation and distribution of such data on DMMPs, and propose simple language features that permit the user to characterize a matrix as "sparse" and specify the associated representation. Together with the data distribution for the matrix, this enables the compiler and runtime system to translate sequential sparse code into explicitly parallel message-passing code. We develop new compilation and runtime techniques, which focus on achieving storage economy and reducing communication overhead in the target program. The overall result is a powerful mechanism for dealing efficiently with sparse matrices in data parallel languages and their compilers for DMMPs.Index Terms-Data-parallel language and compiler, sparse computation, distributed-memory machines, runtime support.

SUMMARYOpenMP has gained wide popularity as an API for parallel programming on shared memory and distributed shared memory platforms. Despite its broad availability, there remains a need for a portable, robust, open source, optimizing OpenMP compiler for C/C++/Fortran 90, especially for teaching and research, for example into its use on new target architectures, such as SMPs with chip multi-threading, as well as learning how to translate for clusters of SMPs. In this paper, we present our efforts to design and implement such an OpenMP compiler on top of Open64, an open source compiler framework, by extending its existing analysis and optimization and adopting a source-to-source translator approach where a native back end is not available. The compilation strategy we have adopted and the corresponding runtime support are described. The OpenMP validation suite is used to determine the correctness of the translation. The compiler's behavior is evaluated using benchmark tests from the EPCC microbenchmarks and the NAS parallel benchmark.

Abstract. A recent trend in mainstream computer nodes is the combined use of general-purpose multicore processors and specialized accelerators such as GPUs and DSPs in order to achieve better performance and to reduce power consumption. To support this trend, the OpenMP Language Committee has approved a set of extensions to OpenMP (referred to as the OpenMP accelerator model). The initial version is the subject of Technical Report 1 (TR1) while OpenMP 4.0 Release Candidate 2 (RC2) further refines the extensions. In this paper, we examine the newly released accelerator directives and create an initial reference implementation, referred to as HOMP (Heterogeneous OpenMP). Focused on targeting NVIDIA GPUs, our work is based on an existing OpenMP implementation in the ROSE sourceto-source compiler infrastructure. HOMP includes extensions to parse the new constructs and to represent them in the AST and other compiler translation details. Further we provide initial runtime support. For our evaluation, we have adapted a few existing OpenMP codes to use the accelerator model directives and present preliminary performance results. Finally, we critique the accelerator model in terms of its impact on developers and compiler writers and suggest possible improvements.