A compiler approach to scalable concurrent program design

The programmer's most powerful tool for controlling complexity in program design is abstraction. We seek to use abstraction in the design of concurrent programs, so as to separate design decisions concerned with decomposition, communication, synchronization, mapping, granularity, and load balancing. This paper describes programming and compiler techniques intended to facilitate this design strategy. The programming techniques are based on a core programming notation with two important properties: the ability to separate concurrent programming concerns, and extensibility with reusable programmer-defined abstractions. The compiler techniques are based on a simple transformation system together with a set of compilation transformations and portable run-time support. The transformation system allows programmer-defined abstractions to be defined as source-to-source transformations that convert abstractions into the core notation. The same transformation system is used to apply compilation transformations that incrementally transform the core notation toward an abstract concurrent machine. This machine can be implemented on a variety of concurrent architectures using simple run-time support. The transformation, compilation, and run-time system techniques have been implemented and are incorporated in a public-domain program development toolkit. This toolkit operates on a wide variety of networked workstations, multicomputers, and shared-memory multiprocessors. It includes a program transformer, concurrent compiler, syntax checker, debugger, performance analyzer, and execution animator. A variety of substantial applications have been developed using the toolkit, in areas such as climate modeling and fluid dynamics

Parallel processing and expert systems

Whether it be monitoring the thermal subsystem of Space Station Freedom, or controlling the navigation of the autonomous rover on Mars, NASA missions in the 1990s cannot enjoy an increased level of autonomy without the efficient implementation of expert systems. Merely increasing the computational speed of uniprocessors may not be able to guarantee that real-time demands are met for larger systems. Speedup via parallel processing must be pursued alongside the optimization of sequential implementations. Prototypes of parallel expert systems have been built at universities and industrial laboratories in the U.S. and Japan. The state-of-the-art research in progress related to parallel execution of expert systems is surveyed. The survey discusses multiprocessors for expert systems, parallel languages for symbolic computations, and mapping expert systems to multiprocessors. Results to date indicate that the parallelism achieved for these systems is small. The main reasons are (1) the body of knowledge applicable in any given situation and the amount of computation executed by each rule firing are small, (2) dividing the problem solving process into relatively independent partitions is difficult, and (3) implementation decisions that enable expert systems to be incrementally refined hamper compile-time optimization. In order to obtain greater speedups, data parallelism and application parallelism must be exploited

Hypercube algorithms on mesh connected multicomputers

A new methodology named CALMANT (CC-cube Algorithms on Meshes and Tori) for mapping a type of algorithm that we call CC-cube algorithm onto multicomputers with hypercube, mesh, or torus interconnection topology is proposed. This methodology is suitable when the initial problem can be expressed as a set of processes that communicate through a hypercube topology (a CC-cube algorithm). There are many important algorithms that fit into the CC-cube type. CALMANT is based on three different techniques: (a) the standard embedding to assign the processes of the algorithm to the nodes of the mesh multicomputer; (b) the communication pipelining technique to increase the level of communication parallelism inherent in the CC-cube algorithms; and (c) optimal message-scheduling algorithms proposed in this work in order to avoid conflicts and minimizing in this way the communication time. Although CALMANT is proposed for multicomputers with different interconnection network topologies, the paper only focuses on the particular case of meshes.Peer ReviewedPostprint (published version

Performance Evaluation of Specialized Hardware for Fast Global Operations on Distributed Memory Multicomputers

Workstation cluster multicomputers are increasingly being applied for solving scientific problems that require massive computing power. Parallel Virtual Machine (PVM) is a popular message-passing model used to program these clusters. One of the major performance limiting factors for cluster multicomputers is their inefficiency in performing parallel program operations involving collective communications. These operations include synchronization, global reduction, broadcast/multicast operations and orderly access to shared global variables. Hall has demonstrated that a .secondary network with wide tree topology and centralized coordination processors (COP) could improve the performance of global operations on a variety of distributed architectures [Hall94a]. My hypothesis was that the efficiency of many PVM applications on workstation clusters could be significantly improved by utilizing a COP system for collective communication operations. To test my hypothesis, I interfaced COP system with PVM. The interface software includes a virtual memory-mapped secondary network interface driver, and a function library which allows to use COP system in place of PVM function calls in application programs. My implementation makes it possible to easily port any existing PVM applications to perform fast global operations using the COP system. To evaluate the performance improvements of using a COP system, I measured cost of various PVM global functions, derived the cost of equivalent COP library global functions, and compared the results. To analyze the cost of global operations on overall execution time of applications, I instrumented a complex molecular dynamics PVM application and performed measurements. The measurements were performed for a sample cluster size of 5 and for message sizes up to 16 kilobytes. The comparison of PVM and COP system global operation performance clearly demonstrates that the COP system can speed up a variety of global operations involving small-to-medium sized messages by factors of 5-25. Analysis of the example application for a sample cluster size of 5 show that speedup provided by my global function libraries and the COP system reduces overall execution time for this and similar applications by above 1.5 times. Additionally, the performance improvement seen by applications increases as the cluster size increases, thus providing a scalable solution for performing global operations

Distributed memory compiler methods for irregular problems: Data copy reuse and runtime partitioning

Outlined here are two methods which we believe will play an important role in any distributed memory compiler able to handle sparse and unstructured problems. We describe how to link runtime partitioners to distributed memory compilers. In our scheme, programmers can implicitly specify how data and loop iterations are to be distributed between processors. This insulates users from having to deal explicitly with potentially complex algorithms that carry out work and data partitioning. We also describe a viable mechanism for tracking and reusing copies of off-processor data. In many programs, several loops access the same off-processor memory locations. As long as it can be verified that the values assigned to off-processor memory locations remain unmodified, we show that we can effectively reuse stored off-processor data. We present experimental data from a 3-D unstructured Euler solver run on iPSC/860 to demonstrate the usefulness of our methods

Analysis of multigrid methods on massively parallel computers: Architectural implications

We study the potential performance of multigrid algorithms running on massively parallel computers with the intent of discovering whether presently envisioned machines will provide an efficient platform for such algorithms. We consider the domain parallel version of the standard V cycle algorithm on model problems, discretized using finite difference techniques in two and three dimensions on block structured grids of size 10(exp 6) and 10(exp 9), respectively. Our models of parallel computation were developed to reflect the computing characteristics of the current generation of massively parallel multicomputers. These models are based on an interconnection network of 256 to 16,384 message passing, 'workstation size' processors executing in an SPMD mode. The first model accomplishes interprocessor communications through a multistage permutation network. The communication cost is a logarithmic function which is similar to the costs in a variety of different topologies. The second model allows single stage communication costs only. Both models were designed with information provided by machine developers and utilize implementation derived parameters. With the medium grain parallelism of the current generation and the high fixed cost of an interprocessor communication, our analysis suggests an efficient implementation requires the machine to support the efficient transmission of long messages, (up to 1000 words) or the high initiation cost of a communication must be significantly reduced through an alternative optimization technique. Furthermore, with variable length message capability, our analysis suggests the low diameter multistage networks provide little or no advantage over a simple single stage communications network

Parallelization of irregularly coupled regular meshes

Regular meshes are frequently used for modeling physical phenomena on both serial and parallel computers. One advantage of regular meshes is that efficient discretization schemes can be implemented in a straight forward manner. However, geometrically-complex objects, such as aircraft, cannot be easily described using a single regular mesh. Multiple interacting regular meshes are frequently used to describe complex geometries. Each mesh models a subregion of the physical domain. The meshes, or subdomains, can be processed in parallel, with periodic updates carried out to move information between the coupled meshes. In many cases, there are a relatively small number (one to a few dozen) subdomains, so that each subdomain may also be partitioned among several processors. We outline a composite run-time/compile-time approach for supporting these problems efficiently on distributed-memory machines. These methods are described in the context of a multiblock fluid dynamics problem developed at LaRC

Highly parallel computation

Highly parallel computing architectures are the only means to achieve the computation rates demanded by advanced scientific problems. A decade of research has demonstrated the feasibility of such machines and current research focuses on which architectures designated as multiple instruction multiple datastream (MIMD) and single instruction multiple datastream (SIMD) have produced the best results to date; neither shows a decisive advantage for most near-homogeneous scientific problems. For scientific problems with many dissimilar parts, more speculative architectures such as neural networks or data flow may be needed

An empirical evaluation of techniques for parallel simulation of message passing networks

209 p.[EN]In the field of computer design, simulation is an essential tool to validate and evaluate architectural proposals. Conventional simulation techniques, designed for their use in sequential computers, are too slow if the system to simulate is large or complex. The aim of this work is to search for techniques to accelerate simulations exploiting the parallelism available in current, commercial multicomputers, and to use these techniques to study a model of a message router. This router has been designed to constitute the communication infrastructure of a (hypothetical) massively parallel computer. Three parallel simulation techniques have been considered: synchronous, asynchronous-conservative and asynchronous-optimistic. These algorithms have been implemented in three multicomputers: a transputer-based Supernode, an Intel Paragon and a network of workstations. The influence that factors such as the characteristics of the simulated models, the organization of the simulators and the characteristics of the target multicomputers have in the performance of the simulations has been measured and characterized. It is concluded that optimistic parallel simulation techniques are not suitable for the considered kind of models, although they may provide good performance in other environments. A network of workstations is not the right platform for our experiments, because the communication demands of the parallel simulators surpass the abilities of local area networks—the granularity is too fine. Synchronous and conservative parallel simulation techniques perform very well in the Supernode and in the Paragon, specially if the model to simulate is complex or large—precisely the worst case for traditional, sequential simulators. This way, studies previously considered as unrealizable, due to their exceedingly high computational cost, can be performed in reasonable times. Additionally, the spectrum of possibilities of using multicomputers can be broadened to execute more than numeric applications.[ES]En el ámbito del diseño de computadores, la simulación es una herramienta imprescindible para la validación y evaluación de cualquier propuesta arquitectónica. Las ténicas convencionales de simulación, diseñadas para su utilización en computadores secuenciales, son demasiado lentas si el sistema a simular es grande o complejo. El objetivo de esta tesis es buscar técnicas para acelerar estas simulaciones, aprovechando el paralelismo disponible en multicomputadores comerciales, y usar esas técnicas para el estudio de un modelo de encaminador de mensajes. Este encaminador está diseñado para formar infraestructura de comunicaciones de un hipotético computador masivamente paralelo. En este trabajo se consideran tres técnicas de simulación paralela: síncrona, asíncrona-conservadora y asíncrona-optimista. Estos algoritmos se han implementado en tres multicomputadores: un Supernode basado en Transputers, un Intel Paragon y una red de estaciones de trabajo. Se caracteriza la influencia que tienen en las prestaciones de los simuladores aspectos tales como los parámetros del modelo simulado, la organización del simulador y las características del multicomputador utilizado. Se concluye que las técnicas de simulación paralela optimista no resultan adecuadas para trabajar con el modelo considerado, aunque pueden ofrecer un buen rendimiento en otros entornos. La red de estaciones de trabajo no resulta una plataforma apropiada para estas simulaciones, ya que una red local no reúne condiciones para la ejecución de aplicaciones paralelas de grano fino. Las técnicas de simulación paralela síncrona y conservadora dan muy buenos resultados en el Supernode y en el Paragon, especialmente si el modelo a simular es complejo o grande—precisamente el peor caso para los algoritmos secuenciales. De esta forma, estudios previamente considerados inviables, por ser demasiado costosos computacionalmente, pueden realizarse en tiempos razonables. Además, se amplía el espectro de posibilidades de los multicomputadores, utilizándolos para algo más que aplicaciones numéricas.Este trabajo ha sido parcialmente subvencionado por la Comisión Interministerial de Ciencia y Tecnología, bajo contrato TIC95-037