SUPRENUM: A trendsetter in modern supercomputer development

Giloi, Wolfgang K.

doi:10.1016/0167-8191(88)90048-8

Cited by 35 publications

(5 citation statements)

References 6 publications

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“…Early work in the field included IVTRAN [43], a language developed for the SIMD machine ILLIAC IV, Connection Machine Fortran [1], Kali [39], and the SUPERB (Suprenum Parallelizer Bonn) system [58]. SUPERB was an interactive restructuring tool, developed at the University of Bonn, which translated Fortran 77 programs into message-passing Fortran for a set of early distributed-memory architectures including the SUPRENUM machine [24]. The system supported general block distributions, which were specified via a simple interactive language.…”

Section: Hpf Predecessorsmentioning

confidence: 99%

From FORTRAN 77 to Locality‐Aware High Productivity Languages for Peta‐Scale Computing

Zima

2007

Scientific Programming

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When the first specification of the FORTRAN language was released in 1956, the goal was to provide an "automatic programming system" that would enhance the economy of programming by replacing assembly language with a notation closer to the domain of scientific programming. A key issue in this context, explicitly recognized by the authors of the language, was the requirement to produce efficient object programs that could compete with their hand-coded counterparts. More than 50 years later, a similar situation exists with respect to finding the right programming paradigm for high performance computing systems. FORTRAN, as the traditional language for scientific programming, has played a major role in the quest for high-productivity programming languages that satisfy very strict performance constraints. This paper focuses on high-level support for locality awareness, one of the most important requirements in this context. The discussion centers on the High Performance Fortran (HPF) family of languages, and their influence on current language developments for peta-scale computing. HPF is a data-parallel language that was designed to provide the user with a high-level interface for programming scientific applications, while delegating to the compiler the task of generating an explicitly parallel message-passing program. We outline developments that led to HPF, explain its major features, identify a set of weaknesses, and discuss subsequent languages that address these problems. The final part of the paper deals with Chapel, a modern object-oriented language developed in the High Productivity Computing Systems (HPCS) program sponsored by DARPA. A salient property of Chapel is its general framework for the support of user-defined distributions, which is related in many ways to ideas first described in Vienna Fortran. This framework is general enough to allow a concise specification of sparse data distributions. The paper concludes with an outlook to future research in this area.

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Section: Hpf Predecessorsmentioning

confidence: 99%

From FORTRAN 77 to Locality‐Aware High Productivity Languages for Peta‐Scale Computing

Zima

2007

Scientific Programming

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“…SUPERB is an interactive restructuring tool that was developed at the University of Bonn by H. Zima and coworkers ([73]). It translates Fortran 77 programs into message-passing Fortran for the Intel iPSC, the GENE-SIS machine, and SUPRENUM ( [31]). SUPERB performs coarse-grain parallelization for a DMMP and is also able to vectorize the resulting code for the individual nodes of the machine.…”

Section: Existing Systemsmentioning

confidence: 99%

Compiling for distributed-memory systems

Zima

Chapman

1993

Proc. IEEE

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Distributed-memop systems are potentiall) scalable to a very large number of processors and promise to be povcvrjiul tools ,for solving large-scale scientijc and engineering problems. Howwer. these machines are currently dificult to program, sirice the user has to distribute the data across the processors atid e.xplicitly formulate the communication required by the program under the selected distribution. During the past years. language extensions oj standard programming languages such as Fortrari were rlei~eloped that allow a concise formulation of data distribution. and new compilation methods were designed and impletnetired that allow the programming of such machines at this relatively high level. In this paper, we describe the current stare of the art in compiling procedural languages (in particular, Fortrun) for [ l i .~t r i b u t e [~-~n e m #~ machines, analyze the limitations of these approuches. tirid outline future research. (SPMD) program for execution on the target DMMP. The compiler analyzes the source code, translating global data references into local and nonlocal data references based on the distributions specified by the user. The nonlocal references are satisfied by inserting appropriate messagepassing statements in the generated code. Finally, the communication is optimized where possible, in particular by combining messages and by sending data at the earliest possible point in time. In algorithms where some data references are made through a level of indirection (such as for unstructured mesh codes and sparse matrix solvers), some of the analysis has to be performed at run-time and the task of the compiler is to generate code to perform this analysis and set up the required communication. This paper is devoted to compilation techniques ,for the source-to-source translation of programs in an ex-264

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“…SUPERB (Suprenum ParallelizER Bonn) is a semi-automatic parallelization system developed at the University of Bonn within the framework of the SUPRENUM project [l]. The target machine is the SUPRENUM supercomputer which is a loosely coupled hierarchical multiprocessor where each nude processor has its own vector unit [2]. SUPERB thus combines MIMD parallelization and vectorization.…”

Section: Introductionmentioning

confidence: 99%

Updating distributed variables in local computations

Gerndt

1990

Concurrency: Pract. Exper.

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This paper describes special aspects of MlMD parallelization in SUPERB. SUPERB is an interactive SIMDMIMD parallelizing system for the SUPRENUM machine. The main topic of this paper is the updating of distributed variables in parallelized applications. The intended applications perform local computations on a large data domain. KEY WORDS Multiprocessors Analysis of algorithms Program transformations

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SUPRENUM: A trendsetter in modern supercomputer development

Cited by 35 publications

References 6 publications

From FORTRAN 77 to Locality‐Aware High Productivity Languages for Peta‐Scale Computing

From FORTRAN 77 to Locality‐Aware High Productivity Languages for Peta‐Scale Computing

Compiling for distributed-memory systems

Updating distributed variables in local computations

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