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Applying an abstract data structure description approach to parallelizing scientific pointer programs
Even though impressive progress has been made in the area of parallelizing scientific programs with arrays, the application of similar techniques to programs with pointer data structures has remained difficult. Unlike arrays which have a small number of well-defined properties that can be utilized by a parallelizing compiler, pointer data structures are used to implement a wide variety of structures that exhibit a much more diverse set of properties. The complexity and diversity of such properties means that, in general, scientific programs with pointer data structures cannot be effectively analyzed by an optimizing and parallelizing compiler.In order to provide a system in which the compiler can fully utilize the properties of different types of pointer data structures, we have developed a mechanism for the Abstract Description of Data Structures (ADDS). With our approach, the programmer can explicitly describe important properties such as dimensionality of the pointer data structure, independence of dimensions, and direction of traversal. These abstract descriptions of pointer data structures are then used by the compiler to guide analysis, optimization, and parallelization.In this paper we summarize the ADDS approach through the use of numerous examples of data structures used in scientific computations, we illustrate how such declarations are natural and non-tedious to specify, and we show how the ADDS declarations can be used to improve compile-time analysis. In order to demonstrate the viability of our approach, we show how such techniques can be used to parallelize an important class of scientific codes which naturally use recursive pointer data structures. In particular, we use our approach to develop the parallelization of an N-body simulation that is based on a relatively complicated pointer data structure, and we report the speedup results for a Sequent multiprocessor
Direct N-body Simulations
Special high-accuracy direct force summation N-body algorithms and their
relevance for the simulation of the dynamical evolution of star clusters and
other gravitating N-body systems in astrophysics are presented, explained and
compared with other methods. Other methods means here approximate physical
models based on the Fokker-Planck equation as well as other, approximate
algorithms to compute the gravitational potential in N-body systems. Questions
regarding the parallel implementation of direct ``brute force'' N-body codes
are discussed. The astrophysical application of the models to the theory of
relaxing rotating and non-rotating collisional star clusters is presented,
briefly mentioning the questions of the validity of the Fokker-Planck
approximation, the existence of gravothermal oscillations and of rotation and
primordial binaries.Comment: 32 pages, 13 figures, in press in Riffert, H., Werner K. (eds),
Computational Astrophysics, The Journal of Computational and Applied
Mathematics (JCAM), Elsevier Press, Amsterdam, 199
Mira: A Framework for Static Performance Analysis
The performance model of an application can pro- vide understanding about its
runtime behavior on particular hardware. Such information can be analyzed by
developers for performance tuning. However, model building and analyzing is
frequently ignored during software development until perfor- mance problems
arise because they require significant expertise and can involve many
time-consuming application runs. In this paper, we propose a fast, accurate,
flexible and user-friendly tool, Mira, for generating performance models by
applying static program analysis, targeting scientific applications running on
supercomputers. We parse both the source code and binary to estimate
performance attributes with better accuracy than considering just source or
just binary code. Because our analysis is static, the target program does not
need to be executed on the target architecture, which enables users to perform
analysis on available machines instead of conducting expensive exper- iments on
potentially expensive resources. Moreover, statically generated models enable
performance prediction on non-existent or unavailable architectures. In
addition to flexibility, because model generation time is significantly reduced
compared to dynamic analysis approaches, our method is suitable for rapid
application performance analysis and improvement. We present several scientific
application validation results to demonstrate the current capabilities of our
approach on small benchmarks and a mini application
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