Compiler Techniques for Optimizing Communication and Data Distribution for Distributed-Memory Computers

Advanced Research Projects Agency (ARPA)National Aeronautics and Space AdministrationOpe

The Multicomputer Toolbox - First-Generation Scalable Libraries

First-generation scalable parallel libraries have been achieved, and are maturing, within the Multicomputer Toolbox. The Toolbox includes sparse, dense, iterative linear algebra, a stiff ODE/DAE solver, and an open software technology for additional numerical algorithms, plus an inter-architecture Makefile mechanism for building applications. We have devised C-based strategies for useful classes of distributed data structures, including distributed matrices and vectors. The underlying Zipcodemessage passing system has enabled process-grid abstractions of multicomputers, communication contexts, and process groups, all characteristics needed for building scalable libraries, and scalable application software. We describe the data-distribution-independent approach to building scalable libraries, which is needed so that applications do not unnecessarily have to redistribute data at high expense. We discuss the strategy used for implementing data-distribution mappings. We also describe high-level message-passing constructs used to achieve flexibility in transmission of data structures (Zipcode invoices). We expect Zipcode and MPI message-passing interfaces (which will incorporate many features from Zipcode, mentioned above) to co-exist in the future. We discuss progress thus far in achieving uniform interfaces for different algorithms for the same operation, which are needed to create poly-algorithms. Poly-algorithms are needed to widen the potential for scalability; uniform interfaces make simpler the testing of alternative methods with an application (whether for parallelism or for convergence, or both). We indicate that data-distribution-independent algorithms are sometimes more efficient than fixed-data-distribution counterparts, because redistribution of data can be avoided, and that this question is strongly application dependent

Task-based Runtime Optimizations Towards High Performance Computing Applications

The last decades have witnessed a rapid improvement of computational capabilities in high-performance computing (HPC) platforms thanks to hardware technology scaling. HPC architectures benefit from mainstream advances on the hardware with many-core systems, deep hierarchical memory subsystem, non-uniform memory access, and an ever-increasing gap between computational power and memory bandwidth. This has necessitated continuous adaptations across the software stack to maintain high hardware utilization. In this HPC landscape of potentially million-way parallelism, task-based programming models associated with dynamic runtime systems are becoming more popular, which fosters developers’ productivity at extreme scale by abstracting the underlying hardware complexity. In this context, this dissertation highlights how a software bundle powered by a task-based programming model can address the heterogeneous workloads engendered by HPC applications., i.e., data redistribution, geospatial modeling and 3D unstructured mesh deformation here. Data redistribution aims to reshuffle data to optimize some objective for an algorithm, whose objective can be multi-dimensional, such as improving computational load balance or decreasing communication volume or cost, with the ultimate goal of increasing the efficiency and therefore reducing the time-to-solution for the algorithm. Geostatistical modeling, one of the prime motivating applications for exascale computing, is a technique for predicting desired quantities from geographically distributed data, based on statistical models and optimization of parameters. Meshing the deformable contour of moving 3D bodies is an expensive operation that can cause huge computational challenges in fluid-structure interaction (FSI) applications. Therefore, in this dissertation, Redistribute-PaRSEC, ExaGeoStat-PaRSEC and HiCMA-PaRSEC are proposed to efficiently tackle these HPC applications respectively at extreme scale, and they are evaluated on multiple HPC clusters, including AMD-based, Intel-based, Arm-based CPU systems and IBM-based multi-GPU system. This multidisciplinary work emphasizes the need for runtime systems to go beyond their primary responsibility of task scheduling on massively parallel hardware system for servicing the next-generation scientific applications

Parallel rendering algorithms for distributed-memory multicomputers

Ankara : Department of Computer Engineering and Information Science and the Institute of Engineering and Science of Bilkent University, 1997.Thesis (Ph. D.) -- Bilkent University, 1997.Includes bibliographical references leaves 166-176.Kurç, Tahsin MertefePh.D

Efficient Distribution Analysis via Graph Contraction

Alignment and distribution of data by an optimizing compiler is a dream of both manufacturers and users of parallel computers. The distribution problem has been formulated as an NP-complete graph optimization problem. The graphs arising in applications are large, and the optimization problem does not lend itself to traditional heuristic optimization techniques. In this paper, we improve some earlier results on methods that use graph contraction to reduce the size of a distribution problem. We report on an experiment using seven example programs that show these contraction operations to be effective in practice; we obtain from 60 to 99 percent reductions in problem size, the larger number being more typical, without loss of solution quality

Compiling Fortran 90D/HPF for distributed memory MIMD computers

This paper describes the design of the Fortran90D/HPF compiler, a source-to-source parallel compiler for distributed memory systems being developed at Syracuse University. Fortran 90D/HPF is a data parallel language with special directives to specify data alignment and distributions. A systematic methodology to process distribution directives of Fortran 90D/HPF is presented. Furthermore, techniques for data and computation partitioning, communication detection and generation, and the run-time support for the compiler are discussed. Finally, initial performance results for the compiler are presented. We believe that the methodology to process data distribution, computation partitioning, communication system design and the overall compiler design can be used by the implementors of compilers for HPF

Distributed Calculation of Edge-Disjoint Spanning Trees for Robustifying Distributed Algorithms Against Man-in-the-Middle Attacks

In this paper we provide a distributed methodology to allow a network of agents, tasked to execute a distributed algorithm, to overcome Man-in-the-middle attacks that aim at steering the result of the algorithm towards inconsistent values or dangerous configurations. We want the agents to be able to restore the correct result of the algorithm in spite of the attacks. To this end, we provide a distributed algorithm to let the set of agents, interconnected by an undirected network topology, construct several $edge-disjoint spanning trees$ by assigning a label to their incident edges. The ultimate objective is to use these spanning trees to run multiple instances of the same distributed algorithm in parallel, in order to be able to detect Man-in-the- middle attacks or other faulty or malicious link behavior (e.g., when the instances yield different results) and to restore the correct result (when the majority of instances is unaffected). The proposed algorithm is lightweight and asynchronous, and is based on iterated depth-first visits on the graph. We complement the paper with a thorough analysis of the performance of the proposed algorithms. IEEE Journal Articl

NASA high performance computing and communications program

The National Aeronautics and Space Administration's HPCC program is part of a new Presidential initiative aimed at producing a 1000-fold increase in supercomputing speed and a 100-fold improvement in available communications capability by 1997. As more advanced technologies are developed under the HPCC program, they will be used to solve NASA's 'Grand Challenge' problems, which include improving the design and simulation of advanced aerospace vehicles, allowing people at remote locations to communicate more effectively and share information, increasing scientist's abilities to model the Earth's climate and forecast global environmental trends, and improving the development of advanced spacecraft. NASA's HPCC program is organized into three projects which are unique to the agency's mission: the Computational Aerosciences (CAS) project, the Earth and Space Sciences (ESS) project, and the Remote Exploration and Experimentation (REE) project. An additional project, the Basic Research and Human Resources (BRHR) project exists to promote long term research in computer science and engineering and to increase the pool of trained personnel in a variety of scientific disciplines. This document presents an overview of the objectives and organization of these projects as well as summaries of individual research and development programs within each project