SUMMARY This paper describes an approach to providing software fault tolerance for future deep‐space robotic National Aeronautics and Space Administration missions, which will require a high degree of autonomy supported by an enhanced on‐board computational capability. We focus on introspection‐based adaptive fault tolerance guided by the specific requirements of applications. Introspection supports monitoring of the program execution with the goal of identifying, locating, and analyzing errors. Fault tolerance assertions for the introspection system can be provided by the user, domain‐specific knowledge, or via the results of static or dynamic program analysis. This work is part of an on‐going project at the Jet Propulsion Laboratory in Pasadena, California. Copyright © 2011 John Wiley & Sons, Ltd.

addresses the enormous complexity of mapping applications to current and future highly parallel platforms - including scalable architectures consisting of tens of thousands of nodes, many-core devices with tens to hundreds of cores, and hierarchical systems providing multi-level parallelism. At systems of these scales, for many important algorithms, performance is dominated by the time required to move data across the levels of the memory hierarchy. As a consequence, locality awareness of algorithms and the efficient management of communication are essential requirements for obtaining scalable parallel performance, and are of particular concern for applications characterized by irregular memory access patterns. We describe the design of a programming system that focuses on productivity of application programmers in expressing locality-aware algorithms for high-end architectures, which are then automatically tuned for performance. The approach combines the successes of two novel concepts for managing locality: high-level specification of user-defined data distributions and model-guided autotuning for data locality. The resulting combined system provides a powerful general mechanism for the specification of data distributions, which can express domain-specific knowledge, and facilitates automatic tuning of a distribution to access patterns in algorithms and its application to different levels of a memory hierarchy. Because there is a clean separation between the specification of a data distribution and the algorithms in which it is used, these can be written separately and composed together to quickly develop new applications that can be tuned in the context of their data set and execution environment. We address key issues for a range of codes that include LU Decomposition, Sparse Matrix-Vector Multiply and Knowledge Discovery. The knowledge discovery algorithms, in particular, stress the proposed language and compiler technology and provide a forcing function for developing tools that address inherent challenges of irregular applications.}

Processor-in-Memory (PIM) architectures avoid the von Neumann bottleneck in conventional machines by integrating high-density DRAM and CMOS logic on the same chip. Parallel systems based on this new technology are expected to provide higher scalability, adaptability, robustness, fault tolerance and lower power consumption than current MPPs or commodity clusters. In this paper we describe the design of Gilgamesh a PIM-based massively parallel architecture, and elements of its execution model. Gilgamesh extends existing PIM capabilities by incorporating advanced mechanisms for virtualizing tasks and data and providing adaptive resource management for load balancing and latency tolerance. The Gilgamesh execution model is based on macroservers a middleware layer which supports object-based runtime management of data and threads allowing explicit and dynamic control of locality and load balancing. The paper concludes with a discussion of related research activities and an outlook to future work.

Opus is a new programming language designed to assist in coordinating the execution of multiple, independent program modules. With the help of Opus, coarse grained task parallelism between data parallel modules can be expressed in a clean and structured way. In this paper we address the problems of how to build a compilation and runtime support system that can efficiently implement the Opus constructs. Our design considers the often-conflicting goals of efficiency and modular construction through software re-use. In particular, we present the system requirements for an efficient Opus implementation, the Opus runtime system, and describe how they work together to provide the underlying services that the Opus compiler needs for a broad class of machines. Copyright © 2000 John Wiley & Sons, Ltd.

The goal of High Performance Fortran (HPF) is to "address the problems of writing data parallel programs where the distribution of data affects performance",

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 complier 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.

Vienna Fortran supports a wide range of data-parallel numerical problems. However, a significant number of scientific and engineering applications are of a multi-disciplinary and heterogeneous nature and thus do not fit well into the data parallel paradigm. In this paper we present new language extensions to fill this gap. Tasks can be spawned as asynchronous activities in a homogeneous or heterogeneous computing environment; they interact by sharing access to Shared Data Abstractions (SDAs). SDAs are an extension of Fortran 90 modules, representing a pool of common data, together with a set of methods for controlled access to these data and a mechanism for providing persistent storage. These extensions support the integration of data and task parallelism and can be used to express task parallel applications in a natural and efficient way.

Parallel processing has been a subject of extensive research for over 20 years, especially in the last 10 years, with many commercial parallel machines becoming available, from small scale parallel machines to massively parallel machines. At one time, it was claimed that parallel machines will become the mainstream computers. However, more recently, some parallel computer vendors have gone out of business and some others are struggling. Some pessimists even claimed that this is a dying field. So, what’s wrong? Five distinguished panelists are invited to share their views on this issue. The panelists are also expected to address what could be done and could be done in order to make parallel computers truly mainstream computers. Panelists

This paper presents a Parameter based Performance Prediction Tool (PPPT) which is part of the Vienna Fortran Compilation System (VFCS), a compiler that automatically translates Fortran programs into message passing programs for massively parallel architectures.

article High performance Fortran without templates: an alternative model for distribution and alignment. Share on Authors: Barbara M. Chapman View Profile , Piyush Mehrotra View Profile , Hans P. Zima View Profile Authors Info & Claims ACM SIGPLAN NoticesVolume 28Issue 7July 1993 pp 92–101https://doi.org/10.1145/173284.155342Online:01 July 1993Publication History 8citation192DownloadsMetricsTotal Citations8Total Downloads192Last 12 Months3Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

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Compilation techniques for the source-to-source translation of programs in an extended FORTRAN 77 to equivalent parallel message-passing programs are discussed. A machine-independent language extension to FORTRAN 77, Data Parallel FORTRAN (DPF), is introduced. It allows the user to write programs for distributed-memory multiprocessing systems (DMMPS) using global addresses, and to specify the distribution of data across the processors of the machine. Message-Passing FORTRAN (MPF), a FORTRAN extension that allows the formulation of explicitly parallel programs that communicate via explicit message passing, is also introduced. Procedures and optimization techniques for both languages are discussed. Additional optimization methods and advanced parallelization techniques, including run-time analysis, are also addressed. An extensive overview of related work is given.>

In order to take on the challenge of fully automatic program parallelizing, one of the last and probably the most decisive missing tool is a performance estimation system. In this paper a new performance prediction tool is introduced, which automatically derives performance estimates for single program multiple data (SPMD) parallel Fortran 77 programs based on distributed memory systems (DMS). The underlying methodology is based on static and dynamic techniques. This paper discusses in particular a high level abstract description of the parallel program, which is utilized to derive performance estimates. The salient features of the overall design of this tool and its components are described.

The authors propose constructs to specify I/O (input/output) operations for distributed data structures in the context of Vienna FORTRAN. These operations can be used by the programmer to provide information which will allow the compiler and runtime environment to optimize the transfer of data to and from secondary storage. Although the language constructs presented have been proposed in the context of Vienna FORTRAN, they can be easily integrated into any other high-performance FORTRAN extension.>