The papers present in this text survey both distributed shared memory (DSM) efforts and commercial DSM systems. The book discusses relevant issues that make the concept of DSM one of the most attractive approaches for building large-scale, high-performance multiprocessor systems. The authors provide a general introduction to the DSM field as well as a broad survey of the basic DSM concepts, mechanisms, design issues, and systems. The book concentrates on basic DSM algorithms, their enhancements, and their performance evaluation. In addition, it details implementations that employ DSM solutions at the software and the hardware level. This guide is a research and development reference that provides state-of-the art information that will be useful to architects, designers, and programmers of DSM systems.

Virtual Shared Memory for Distributed Architecture

Programming nonshared memory systems is more difficult than programming shared memory systems, since there is no support for shared data structures. Current programming languages for distributed memory architectures force the user to decompose all data structures into separate pieces, with each owned by one of the processors in the machine, and with all communication explicitly specified by low-level message-passing primitives. This paper presents a new programming environment for distributed memory architectures, providing a global name space and allowing direct access to remote parts of data values. We describe the analysis and program transformations required to implement this environment, and present the efficiency of the resulting code on the NCUBE/7 and IPSC/2 hypercubes. (kr).

In this paper we examine the use of a shared memory programming model to address the problem of portability of application codes between distributed memory and shared memory architectures. We do this with an extension of the Parallel C Preprocessor. The extension, borrowed from Split-C and AC, uses type qualifiers instead of storage class modifiers to declare variables that are shared among processors. The type qualifier declaration supports an abstract shared memory facility on distributed memory machines while making direct use of hardware support on shared memory architectures. Our benchmarking study spans a wide range of shared memory and distributed memory platforms. Benchmarks include Gaussian elimination with back substitution, a two-dimensional fast Fourier transform, and a matrix-matrix multiply. We find that the type-qualifier-based shared memory programming model is capable of efficiently spanning both distributed memory and shared memory architectures. Although the resulting shared memory programming model is portable, it does not remove the need to arrange for overlapped or blocked remote memory references on platforms that require these tuning measures in order to obtain good performance.

Multicore architectures with high core counts have come to dominate the world of high performance computing, from shared memory machines to the largest distributed memory clusters. The multicore route to increased performance has a simpler design and better power efficiency than the traditional approach of increasing processor frequencies. But, standard programming techniques are not well adapted to this change in computer architecture design. In this work, we study the use of dynamic runtime environments executing data driven applications as a solution to programming multicore architectures. The goals of our runtime environments are productivity, scalability and performance. We demonstrate productivity by defining a simple programming interface to express code. Our runtime environments are experimentally shown to be scalable and give competitive performance on large multicore and distributed memory machines. This work is driven by linear algebra algorithms, where state-of-the-art libraries (e.g., LAPACK and ScaLAPACK) using a fork-join or block-synchronous execution style do not use the available resources in the most efficient manner. Research work in linear algebra has reformulated these algorithms as tasks acting on tiles of data, with data dependency relationships between the tasks. This results in a task-based DAG for the reformulated algorithms, which can be executed via asynchronous data-driven execution paths analogous to dataflow execution. We study an API and runtime environment for shared memory architectures that efficiently executes serially presented tile based algorithms. This runtime is used to enable linear algebra applications and is shown to deliver performance competitive with state-of-the-art commercial and research libraries. We develop a runtime environment for distributed memory multicore architectures extended from our shared memory implementation. The runtime takes serially presented algorithms designed for the shared memory environment, and schedules and executes them on distributed memory architectures in a scalable and high performance manner. We design a distributed data coherency protocol and a distributed task scheduling mechanism which avoid global coordination. Experimental results with linear algebra applications show the scalability and performance of our runtime environment.