Abstract Titanium is a language and system for high-performance parallel scientific computing. Titaniumuses Java as its base, thereby leveraging the advantages of that language and allowing us to focus

LPARX provides efficient run-time support for dynamic, non-uniform scientific calculations running on MIMD distributed memory architectures. It extends HPF's data decomposition model to provide support for dynamic, block irregular data structures. LPARX represents data decompositions as first-class objects and expresses data dependencies in a manner which is logically independent of data decomposition and problem dimension. LPARX applications are portable across a diversity of MIMD machines. We have implemented a number of applications in LPARX--- including a 3d particle calculation and 2d and 3d adaptive multigrid solvers---which could not have been efficiently implemented in HPF.

Parallel implementations of scientific applications often rely on elaborate dynamic data structures with complicated communication patterns. We describe a set of intuitive geometric programming abstractions that simplify coordination of irregular block-structured scientific calculations without sacrificing performance. We have implemented these abstractions in KeLP, a C++ run-time library. KeLP's abstractions enable the programmer to express complicated communication patterns for dynamic applications, and to tune communication activity with a high-level, abstract interface. We show that KeLP's flexible communication model effectively manages elaborate data motion patterns arising in structured adaptive mesh refinement, and achieves performance comparable to hand-coded message-passing on several structured numerical kernels. to appear in J. Parallel and Distributed Computing 1 Introduction Many scientific numerical methods employ structured irregular representations to improve accura...

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LPARX is a software development tool for implementing dynamic, irregular scientific applications, such as multilevel multilevel finite difference methods and particle methods, on high performance MIMD parallel architectures. It supports coarse grain data parallelism and gives the application complete control over specifying arbitrary block decompositions. LPARX provides structural abstraction, representing data decompositions as first-class objects that can be manipulated and modified at run-time. LPARX, implemented as a C++ class library, is currently running on diverse MIMD platforms, including the Intel Paragon, Cray C-90, IBM SP2, and networks of workstations running under PVM. Software may be developed and debugged on a single processor workstation. 1 Introduction An outstanding problem in scientific computation is how to manage the complexity of converting mathematical descriptions of dynamic, irregular numerical algorithms into high performance applications software. Non-unifo...

LPAR is a portable coarse-grain parallel programming model for non-uniform structured scientific applications running on MIMD message passing architectures. Non-uniform applications, which include N-body methods and adaptive multilevel mesh methods, rely on complex dynamic data structures and are particularly difficult to implement on parallel computers. This paper introduces the LPAR programming abstractions and discusses some important implementation issues. We also present performance results on the Intel iPSC/860 and nCUBE/2 for a vortex dynamics application developed using LPAR. 1 Introduction Recent developments in numerical methods for solving partial differential equations have emphasized elaborate, dynamic, non-uniform data structures. These methods attempt to place computational effort and accuracy in regions of high error or rapidly changing solutions. They are particularly attractive for solving local, non-uniform, time-dependent problems. Typical applications include fast...

this report was partially supported by Grant CCR-90-24549 from the National Science Foundation. This is a report to the National Science Foundation and other agencies; it is not a report by or of the National Science Foundation or any other agency. Participants at the Workshop on Research Directions in Integrating Numerical Analysis, Symbolic Computing, Computational Geometry, and Artificial Intelligence for Computational Science Conference Organizers

In this paper we review the current state of the problem solving environment (PSE) field and make projections for the future. First we describe the computing context, the definition of a PSE and the goals of a PSE. The state-of-the-art is summarized along with sources (books, bibliographics, web sites) of more detailed information. The principal components and paradigms for building PSEs are presented. The discussion of the future is given in three parts: future trends, scenarios for 2010/2025, and research

LPARX is a software development tool for implementing dynamic irregular scientific applications on high performance MIMD parallel computers. LPARX, implemented as a C++ class library, supports coarse grain data parallelism arising in particle methods and adaptive finite difference methods. It provides structural abstraction, which enables data decompositions to exist as first-class objects. LPARX is currently running on diverse MIMD including the Intel Paragon, the Cray C-90, and networks of workstations running under PVM, and software may be developed on a single processor workstation. 1 Introduction An outstanding dilemma in scientific computation is how to manage the expansion of detail that accompanies the programmingprocess from high-level mathematical description to finely tuned implementation. Non-uniform applications, such as particle methods and adaptive grid methods, are particularly challenging owing to the use of dynamic irregular data structures. The difficulty is furthe...

We describe the rationale behind the design of key features of Titanium—an explicitly parallel dialect of Java for high-performance scientific programming—and our experiences in building applications with the language. Specifically, we address Titanium’s partitioned global address space model, single program multiple data parallelism support, multi-dimensional arrays and array-index calculus, memory management, immutable classes (class-like types that are value types rather than reference types), operator overloading, and generic programming. We provide an overview of the Titanium compiler implementation, covering various parallel analyses and optimizations, Titanium runtime technology and the GASNet network communication layer. We summarize results and lessons learned from