一种HPF程序的监测与分析工具

该文介绍了一个构筑在HPF(high performance Fortran)编译系统之上的性能监测与性能分析工具.文章概述了HPF编译系统,讨论了性能分析的重要性及主要任务,详细介绍了这一工具所使用的性能分析方法和性能监测与性能数据收集方法,并对此工具的使用效果进行了总结.     

JAPS中并行性分析的设计与实现

并行性分析技术是并行编译器中的关键分析技术，也是这一领域研究的热点问题，其目的是对串性程序进行依赖关系分析，提取可并行成分，并在此基础上对串行程序进行变换和分割。文章主要讨论了在基于JAVA的自动并行编译系统JAPS中，并行性分析模块的设计框架和实现方式。     

基于HPF的地震资料并行处理系统研究与实现

文中首先分析了地震资料并行处理的必要性，在介绍HPF高性能并行语言的特点后，着重讨论了p-HPF编译系统的体系结构与关键技术，然后，在分析地震资料自身特征和处理过程模块化程序设计的特点后，给出了地震资料处理的并行策略和一般SPMD程序的实现框图，最后，用一个简单的例子展示了HPF程序对于不同数据规模的并行效率，并用实际地震资料实现了大规模并行处理。     

树型编译系统设计

从编译系统的用户界面出发,探讨了不同类型用户界面的优缺点。提出了语句模块预处理-编译器的设计思路,阐述了此设计方法基于树型界面实现可视化编译系统的具体实现步骤,重点讨论了树型系统部分的实现方法。     

基于规范划分集的并行循环编译框架

1 引言并行循环描述大多数先进科学应用的核心部分,是颇有价值的并行性来源。高级并行语言往往提供专门指导语句表达并行循环,以便并行编译器利用并行循环的非数据相关特性。在数据并行程序设计语言HPF中规定,可以将关键词INDEPENDENT加在Do循环前面,指出其后的Do循环是一个并行循环。与之相关的编译实现也成为HPF编译器的一个重点。并行循环L可以严格定义如下:假设R(i)表示迭代i所     

编译群体智能系统应用程序:以全分布式智能建筑系统为例

群体智能系统通过邻居个体的信息交互实现群体级别的应用任务,具有良好的鲁棒性和灵活性.与此同时,大多数开发人员难以对分布式、并行的个体交互机制进行描述.一些高级语言允许用户以串行思维方式、从系统全局角度来编程并行的群体智能计算任务,而无需考虑通信协议、数据分布等底层交互细节.但面向用户、全局声明式的群体智能系统应用程序与个体并行执行逻辑存在的巨大语义差距,使得编译过程复杂进而导致应用程序开发效率不高.本文提出了一个编译系统及其支撑工具,支持将高级的群体智能系统应用程序转换为安全、高效的分布式实现.该编译系统通过并行信息识别,计算划分,交互信息生成技术,将面向系统全局、串行编程的群体智能应用程序编译为面向个体独立执行的并行目标代码,从而使用户不必了解个体间的复杂交互机制.设计了一种标准化中间表示,将复杂群体智能计算任务转换为群体智能算子和输入输出变量组合而成的标准化语义模块序列,其以独立于平台的形式表示源程序信息,屏蔽了目标硬件平台的异构性.在一个群体智能系统案例平台中部署和测试了该编译系统,结果表明该系统能够有效将群体智能应用程序编译为平台可执行的目标代码并提升应用程序开发效率,其生成的代码在一系列基准测试中具有比现有编译器更好的性能.     

协作式全局指令调度与寄存器分配

指令级并行是现代高性能代理器的重要特征,对于发挥这类处理器所具有的并行处理能力来说,编译器有至关重要的影响。文中讨论指令级并行编译中的核心问题－全局指令调度与 器分配,并以作者为一种新型的显式并行体系结构微处理器的编译系统为背景,介绍了此类编译器后端设计中面临的指令调度与寄存器分配的时序问题,以及为解决这一问题而提出了的一种协作式全局指令调度与寄存器分配方法。     

JAPS可视化环境的设计与实现

介绍了Ｊａｖａ自动并行化编译系统Ｊａｖａ Ａｕｔｏｍａｔｉｃ Ｐａｒａｌｌｅｌｉｚｉｎｇ Ｓｙｓｔｅｍ（ＪＡＰＳ）的可视化界面中数据转换模块和动态处理模块的设计和实现。其中,数据转移模块根据编译器的输出构造任务依赖图的数据结构并实现其层次化的直观显示。而动态处理模块实现运行过程中对动态信息时获取和处理。     

基于边缘轮廓特征的实时车型分类

采用面向对象技术设计可视化程序设计语言的编译系统,通过对复杂的可视化图片语法进行分析,将各语法现象以语法单元类的形式进行抽象分类,提出了编译器-编译器的设计策略,给出了从可视化图片设计语言到伪指令代码的解决方法.主要研究在面向对象的编辑环境中如何实现满足硬件设备控制器的稳定、高效的编译系统.通过将可视化程序语言分析器(VPLPG)和小型的类C编译(LCC)器配合使用最终实现可视化程序设计语言的编译系统.     

A generic interface for parallel access to large data sets from HPF applications

A critical performance issue for a number of scientific and engineering applications is the efficient transfer of data to secondary storage. Languages such as High Performance Fortran (HPF) have been introduced to allow programming distributed-memory systems at a relatively high level of abstraction. However, the present version of HPF does not provide appropriate constructs for controlling the parallel I/O capabilities of these systems. In this paper, constructs to specify parallel I/O operations on multidimensional arrays in the context of HPF are proposed. The paper also presents implementation concepts that are based on the HPF compiler VFC and the parallel I/O run-time system Panda. Experimental performance results are discussed in the context of financial management and traffic simulation applications.     

p-HPF并行编译系统核外计算的实现及优化策略

文中阐述了ｐ－ＨＰＦ编译系统中对核外计算的支持 以及采取的优化策略,通过对编程模型的扩充和并行Ｉ／Ｏ模型的构造,ｐ－ＨＰＦ编译系统已能对核外数组进行有效的处理。     

p—HPF支持多范例并行计算的并行编译技术

多范例并行是大规模并行应用系统的本质特征,实现p－HPF对多范例并行计算的编译支持不仅可以弥补数据并行示例本身的一些缺点,而且可以提高并行应用系统的效率,文中在论述cluster环境下Global,Local,Serial三种典型并行计算模型的基础上,给出了实现p－HPF对三种模型的典型代表F77＋MPI,ScaLAPACK调用的并行编译技术,包括参数重分布技术、存储转换技术、全局与局部信息交换技术以及局部数组参数的上下界处理技术等,给出了调用实例并分析了实现技术的正确性和有效性。     

Software system of the Earth Simulator

The Earth Simulator (ES) is a large scale, distributed memory, parallel computer system consisting of 640 processor nodes (PN) with shared memory vector multi-processors (64GFLOPS/PN, 5120 APs in total, AP: arithmetic processor). All the nodes are connected via a high speed (16GB/s) single-stage crossbar network called the Interconnection Network (IN).

The operating system for the Earth Simulator is based on SUPER-UX, the UNIX operating system for the SX series scientific supercomputers. In order to realize high-performance parallel processing on the highly parallel machine, the operating system is enhanced for scalability.

The Earth Simulator system is managed as a two-level cluster system called the Super Cluster System. In the Super Cluster System, the Earth Simulator system is divided into 40 clusters (16 PNs/cluster). A single controller called Super Cluster Control Station (SCCS) manages all these clusters. This management system provides Single System Image (SSI) operation, management and job control for the large scale multi-node system.

The Job Scheduler (JS) and NQS running on the SCCS control all jobs of the system. They schedule the resources such as processing nodes and files which have not usually been treated as scheduling resources. This allows efficient scheduling of large scale jobs.

The MPI library (MPI/ES) and the HPF compiler (HPF/ES) are available for distributed parallel programming on the Earth Simulator. MPI/ES conforms to the MPI 2.0 standard and is optimized to exploit the hardware features. HPF/ES conforms to the core part of HPF 2.0 and supports some features of the HPF 2.0 approved extensions and HPF/JA 1.0 extensions. HPF/ES suitably handles the 3-level parallelism of the Earth Simulator system, that is, vectorization, shared-memory parallelization, and distributed-memory parallelization. Moreover, HPF/ES extends the language to easily handle irregular problems.     

The efficient computation of ownership sets in HPF

Ownership sets are fundamental to the partitioning of program computations across processors by the owner-computes rule. These sets arise due to the mapping of arrays onto processors. In this paper, we focus on how ownership sets can be efficiently determined in the context of the HPF language and show how the structure of these sets can be symbolically characterized in the presence of arbitrary array alignment and array distribution directives. Our starting point is a system of equalities and inequalities due to Ancourt et al. (1995) that captures the array mapping problem in HPF. We arrive at a refined system that enables us to efficiently solve for the ownership set using the Fourier-Motzkin Elimination technique and that requires the course vector as the only auxiliary vector. The formulation makes it possible to enumerate the elements of the ownership set exactly once, a feature that is very beneficial when such sets are applied to handle DO loops qualified by HPF's INDEPENDENT directive. We develop important and general properties pertaining to HPF alignments and distributions and show how they can be used to eliminate redundant communication due to array replication. Polynomial-time schemes that determine whether the ownership set of a particular processor, with respect to some array, is the empty set or whether the ownership set of every processor, with respect to some array, is the empty set, are presented. We show how distribution directives with unspecified processor meshes can be efficiently handled at compile time. We also show how to avoid the generation of communication code when pairs of array references are ultimately mapped onto the same processors. Experimental data demonstrating the improved code performance that the latter optimization enables is presented and discussed     

Cluster环境下p—HPF编译器支持的并行计算范式

p-HPF是研制的一个符合HPF（high performance Fortran)规范的并行编译系统,以HPF为核心实现多范式并行计算是开发大型并行应用系统的基础。首先论述了Cluster环境下的并行运行范式,包括farm parallel范式、流水线并行、流循环并行、基于数据并行和组合数据并行等,抽象分析了它们的性能,接着给出了利用p-HPF的外部过程机制、任务并行机制以以FORALL,INDEPENDENT DO等典型并行语句实现几种典型并行范式的方法,给出了实例程序,对实例进行了实际运行并对运行结果进行了分析。     

A Library-Based Approach to Task Parallelism in a Data-Parallel Language

Pure data-parallel languages such as High Performance Fortran version 1 (HPF) do not allow efficient expression of mixed task/data-parallel computations or the coupling of separately compiled data-parallel modules. In this paper, we show how these common parallel program structures can be represented, with only minor extensions to the HPF model, by using a coordination library based on the Message Passing Interface (MPI). This library allows data-parallel tasks to exchange distributed data structures using calls to simple communication functions. We present microbenchmark results that characterize the performance of this library and that quantify the impact of optimizations that allow reuse of communication schedules in common situations. In addition, results from two-dimensional FFT, convolution, and multiblock programs demonstrate that the HPF/MPI library can provide performance superior to that of pure HPF. We conclude that this synergistic combination of two parallel programming standards represents a useful approach to task parallelism in a data-parallel framework, increasing the range of problems addressable in HPF without requiring complex compiler technology.     

Hesitant Pythagorean fuzzy Maclaurin symmetric mean operators and its applications to multiattribute decision-making process

Hesitant Pythagorean fuzzy (HPF) sets can easily express the uncertain information while Maclaurin symmetric mean (MSM) operator, can capture the interrelationship among the multiattributes, and are suitable for aggregating the information into a single number. By taking the advantages of both, in this paper, we extend the traditional MSM to HPF environment. For this, we develop the HPFMSM operator for aggregating the HPF information. The desirable characteristics, such as idempotency, monotonicity, and boundedness, are studied. Then, we discussed some special cases with respect to the parameter value of the HPFMSM operators and showed that it generalizes the various existing operators. Furthermore, we studied the weighted HPFMSM operator to aggregate the HPF information with different preferences to the input arguments. On the basis of these operators, we solved the multiattribute decision-making problems with HPF information. The practicality and effectiveness of the developed approach are demonstrated through a numerical example.     

Hyper-Radial Visualization (HRV) method with range-based preferences for multi-objective decision making

A visualization-based methodology is developed in which a Hyperspace Pareto Frontier (HPF) can be represented for design concept selection. The new approach is termed the Hyper-Radial Visualization (HRV) method. The HRV method enables designers to investigate trade-off decisions between Pareto solutions by their relative position in an HRV-based visualization. Three a posteri range-based preference incorporation approaches are proposed in this paper that can be combined with HRV-based visualizations to enable designers to quickly identify better regions in high dimensional performance space for Multi-objective Optimization Problems (MOPs). The paper first explains the details of the HRV method, which can generate a meaningful representation of an HPF. Second, three color-coding preference schemes are proposed in this work to enable intuitive trade-off studies using the HRV-based HPF visualizations. Finally, several MOPs are used to investigate the performance of the HRV-based preference approaches that have been proposed. The viability and desirability of using the HRV for decision making support is also explored.