A coprocessor design for the architectural support of non-numeric operations

Computer Science is concerned with the electronic manipulation of information. Continually increasing amounts of computer time are being expended on information that is not numeric. This is represented in part by modem computing requirements such as the block moves associated with context switching and virtual memory management, peripheral device communication, compilers, editors, word processors, databases, and text retrieval. This dissertation examines the traditional support of non-numeric information from a software, firmware, and hardware perspective and presents a coprocessor design to improve the performance of a set of non-numeric operations. Simple micro-coding of operations can provide a degree of performance improvement through parallel execution of instructions and control store access speeds. New special purpose parallel hardware algorithms can yield complexity improvements. This dissertation presents a parallel hardware regular expression searching algorithm which requires linear time and quadratic space compared to software uniprocessor algorithms which require exponential time and space. A very large scale integration (VLSD implementation of a version of this algorithm was designed, fabricated, and tested. The hardware. searching algorithm is then combined with other special purpose hardware to implement a set of operations. Simulation is then used to quantify the performance improvement of the operations when compared to software solutions. A coprocessor approach allows the optional addition of hardware to accelerate a set of operations. This is appropriate from a complex instruction set computer (CISC) perspective since hardware acceleration is being utilized. It is also appropriate from a reduced instruction set computer (RISC) perspective since the operations are distributed away from the central processing unit (CPU)

Model transformations in Converge

Model transformations are currently the focus of much interest and research due to the OMG’s QVT initiative. Current proposals for model transformation languages can be divided into two main camps: those taking a ‘declarative’ approach, and those opting for an ‘imperative’ approach. In this paper we detail an imperative, meta-circular, object orientated, pattern matching programming language Converge which is enriched with features pioneered by the Icon programming language, amongst them: success/failure, generators and goal-directed evaluation. By presenting these features in a language suitable for representing models, we show that we are able to gain some of the advantages of declarative approaches in an imperative setting

Model transformations in converge.

Model transformations are currently the focus of much interest and research due to the OMG’s QVT initiative. Current proposals for model transformation languages can be divided into two main camps: those taking a ‘declarative’ approach, and those opting for an ‘imperative’ approach. In this paper we detail an imperative, meta-circular, object orientated, pattern matching programming language Converge which is enriched with features pioneered by the Icon programming language, amongst them: success/failure, generators and goal-directed evaluation. By presenting these features in a language suitable for representing models, we show that we are able to gain some of the advantages of declarative approaches in an imperative setting

Using icon-derived technologies to drive model transformations.

Model transformations are currently the object of much interest and research. Current proposals for model transformation languages can be divided into two main camps: those taking a ‘declarative’ approach, and those opting for an ‘imperative’ approach. The Icon programming language is a SNOBOL derivative which contains several unique constructs which make it particularly well suited to the job of analyzing and transforming strings. In this paper we discuss model transformations, analyze the relevant parts of Icon that lend themselves to transforming strings, and then propose how some of Icon’s unique features could be incorporated into a model transformation approach that partially blurs the distinction between ‘declarative’ and ‘imperative’ approaches

Model transformations in converge.

Model transformations are currently the focus of much interest and research due to the OMG’s QVT initiative. Current proposals for model transformation languages can be divided into two main camps: those taking a ‘declarative’ approach, and those opting for an ‘imperative’ approach. In this paper we detail an imperative, meta-circular, object orientated, pattern matching programming language Converge which is enriched with features pioneered by the Icon programming language, amongst them: success/failure, generators and goal-directed evaluation. By presenting these features in a language suitable for representing models, we show that we are able to gain some of the advantages of declarative approaches in an imperative setting