It is an increasingly common belief that the programmability of parallel machines is lacking, and that the high-end computing (HEC) community is suffering as a result of it. The population of users who can effectively program parallel machines comprises only a small fraction of those who can effectively program traditional sequential computers, and this gap seems only to be widening as time passes. The parallel computing community’s inability to tap the skills

Fortran as a simple set of extensions to Fortran 95 [7]. Their principal extension to Fortran was support for shared data known as coarrays. In 2005, the Fortran Standards Committee began exploring the addition of coarrays to Fortran 2008, which is now being finalized. Careful review of drafts of the emerging Fortran 2008 standard led us to identify several shortcomings with the proposed coarray extensions. In this paper, we briefly critique the coarray extensions proposed for Fortran 2008, outline a new vision for coarrays in Fortran language that is far more expressive, and briefly describe our strategy for implementing the language extensions that we propose. I.

Abstract. Co-array notation provides a compact syntax for programming parallel programs. Co-array Fortran (CAF) introduced and implements this notation, and CAF is currently proposed as an extension to the Fortran language standard. We believe that co-array notation requires a revised semantic definition beyond that specified by CAF for both pragmatic reasons within Fortran and to make the notation attractive for incorporation into other programming languages. The revised semantics make the language model easier to understand and reduces the potential for programmer error. Furthermore, these revised semantics allow CAF to be extended to capture collective operations in co-array notation. 1

Large scale parallel simulations are fundamental tools for engineers and scientists. Con-sequently, it is critical to develop both programming models and tools that enhance devel-opment time productivity, enable harnessing of massively-parallel systems, and to guide the diagnosis of poorly scaling programs. This thesis addresses this challenge in two ways. First, we show that Co-array Fortran (CAF), a shared-memory parallel program-ming model, can be used to write scientific codes that exhibit high performance on modern parallel systems. Second, we describe a novel technique for analyzing parallel program performance and identifying scalability bottlenecks, and apply it across multiple program-ming models. Although the message passing parallel programming model provides both portability and high performance, it is cumbersome to program. CAF eases this burden by providing a partitioned global address space, but has before now only been implemented on shared-memory machines. To significantly broaden CAF’s appeal, we show that CAF programs can deliver high-performance on commodity cluster platforms. We designed and imple-

The Message Passing Interface (MPI) is the library-based programming model employed by most scalable parallel applications today; however, it is not easy to use. To simplify program development, Partitioned Global Address Space (PGAS) languages have emerged as promising alternatives to MPI. Co-array Fortran (CAF), Titanium, and Unified Parallel C are explicitly parallel single-program multiple-data languages that provide the abstraction of a global shared memory and enable programmers to use one-sided communication to access remote data. This thesis focuses on evaluating PGAS languages and explores new language features to simplify the development of high performance programs in CAF. To simplify program development, we explore extending CAF with abstractions for group, Cartesian, and graph communication topologies that we call co-spaces. The com-bination of co-spaces, textual barriers, and single values enables effective analysis and optimization of CAF programs. We present an algorithm for synchronization strength re-duction (SSR), which replaces textual barriers with faster point-to-point synchronization. This optimization is both difficult and error-prone for developers to perform manually.

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