Reverse time migration: A seismic processing application on the connection machine

The implementation of a reverse time migration algorithm on the Connection Machine, a massively parallel computer is described. Essential architectural features of this machine as well as programming concepts are presented. The data structures and parallel operations for the implementation of the reverse time migration algorithm are described. The algorithm matches the Connection Machine architecture closely and executes almost at the peak performance of this machine

Parallel processors and nonlinear structural dynamics algorithms and software

The adaptation of a finite element program with explicit time integration to a massively parallel SIMD (single instruction multiple data) computer, the CONNECTION Machine is described. The adaptation required the development of a new algorithm, called the exchange algorithm, in which all nodal variables are allocated to the element with an exchange of nodal forces at each time step. The architectural and C* programming language features of the CONNECTION Machine are also summarized. Various alternate data structures and associated algorithms for nonlinear finite element analysis are discussed and compared. Results are presented which demonstrate that the CONNECTION Machine is capable of outperforming the CRAY XMP/14

An assessment of the connection machine

The CM-2 is an example of a connection machine. The strengths and problems of this implementation are considered as well as important issues in the architecture and programming environment of connection machines in general. These are contrasted to the same issues in Multiple Instruction/Multiple Data (MIMD) microprocessors and multicomputers

CL1 Manual

CL1 is a prototyping language for programming a Connection Machine. It supports a model of the Connection Machine as a collection of tiny conventional machines (process elements), each with its own independent program counter.MIT Artificial Intelligence Laborator

Highly parallel sparse Cholesky factorization

Several fine grained parallel algorithms were developed and compared to compute the Cholesky factorization of a sparse matrix. The experimental implementations are on the Connection Machine, a distributed memory SIMD machine whose programming model conceptually supplies one processor per data element. In contrast to special purpose algorithms in which the matrix structure conforms to the connection structure of the machine, the focus is on matrices with arbitrary sparsity structure. The most promising algorithm is one whose inner loop performs several dense factorizations simultaneously on a 2-D grid of processors. Virtually any massively parallel dense factorization algorithm can be used as the key subroutine. The sparse code attains execution rates comparable to those of the dense subroutine. Although at present architectural limitations prevent the dense factorization from realizing its potential efficiency, it is concluded that a regular data parallel architecture can be used efficiently to solve arbitrarily structured sparse problems. A performance model is also presented and it is used to analyze the algorithms

Data-parallel programming with multiple inheritance on the connection machine

The demand for computers is oriented toward faster computers and newer computers are being built with more than one CPU. These computers require sophisticated software to program them. One such approach to program the multiple CPU machines is through the use of object-oriented programming techniques. An example of such an approach is the use of C* on the Connection Machine. Though C* supports many of the object-oriented concepts, it does not support the concept of software reuse through inheritance. This thesis introduces a new language called C*±+ , an extension of C* language to support inheritance. We also discuss the issues invloved in the implementation of multiple inheritance in programming languages. This thesis describes the differences between C** and C* . It also discusses the various issues involved in the design and implementation of the translator from C** to C* . It also illustrates the advantages of programming in C*++ through an example. Since C*++ is designed to support software reuse which allows the users to create quality software in shorter time, it is anticipated that C*+ will have widespread use in programming the Connection Machine

PTHOMAS: An adaptive information retrieval system on the connection machine.

This paper reports the state of development of PThomas, a network based document retrieval system implemented on a massively parallel fine-grained computer, the Connection Machine. The program is written in C*, an enhancement of the C programming language which exploits the parallelism of the Connection Machine. The system is based on Oddy’s original Thomas program, which was highly parallel in concept, and makes use of the Connection Machine’s single instruction multiple data (SIMD) processing capabilities. After an introduction to systems like Thomas, and their relationship to spreading activation and neural network models, the current state of PThomas is described, including details about the network representation and the parallel operations that are executed during a typical PThomas session

UC: a language for the connection machine

n designing parallel languages, the concern for defining a simple virtual machine must be balanced against the need to efficiently map a program on a specific architecture. UC addresses this problem by separating the programming task from efficiency considerations. UC programs are designed using a small set of constructs that include reduction, parallel assignment, and fixed-point computation. The language also provides a map section that may optionally be used by a programmer to specify data mappings for the program. The authors describe the UC constructs and their implementation on the Connection Machine. They also present measurements of the compiler for simple benchmarks

GP vs GI: if you can't beat them, join them

Genetic Programming (GP) has been criticized for targeting irrelevant problems [12], and is also true of the wider machine learning community [11]. which has become detached from the source of the data it is using to drive the field forward. However, recently GI provides a fresh perspective on automated programming. In contrast to GP, GI begins with existing software, and therefore immediately has the aim of tackling real software. As evolution is the main approach to GI to manipulating programs, this connection with real software should persuade the GP community to confront the issues around what it originally set out to tackle i.e. evolving real software