By John Case, Dayanand S. Rajan, Anil M. Shende (auth.), Klaus P. Jantke (eds.)
This quantity comprises the textual content of the 5 invited papers and sixteen chosen contributions awarded on the 3rd foreign Workshop on Analogical and Inductive Inference, AII `92, held in Dagstuhl citadel, Germany, October 5-9, 1992. just like the earlier occasions, AII '92 was once meant to assemble representatives from a number of examine groups, specifically, from theoretical desktop technological know-how, synthetic intelligence, and from cognitive sciences. The papers contained during this quantity represent a cutting-edge record on formal techniques to algorithmic studying, really emphasizing features of analogical reasoning and inductive inference. either those components are presently attracting robust curiosity: analogical reasoning performs a very important function within the booming box of case-based reasoning, and, within the fieldof inductive common sense programming, there have lately been constructed a couple of new suggestions for inductive inference.
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Extra resources for Analogical and Inductive Inference: International Workshop AII '92 Dagstuhl Castle, Germany, October 5–9, 1992 Proceedings
Arenaz, J. Touriño, and R. Doallo Fig. 8. Execution times Fig. 9. Speed-ups on the display buffer, with the corresponding saving of resources. In this case, computational load is measured as the maximum number of array elements that are computed by the processors. 36 and C = 0 for large subarrays of A) when dead code elimination is applied. Unlike our inspector-executor technique (black bars), the array expansion method (gray An Inspector-Executor Algorithm for Irregular Assignment Parallelization 13 Fig.
For the purpose of reducing the cost for massive data clustering, therefore, many approaches to parallel data clustering have been proposed mainly using multiprocessors and/or special hardware [6,7,8,9]. We present an effective implementation of clustering  on a commodity programmable graphics processing unit (GPU), which is used as a powerful SIMD-parallel coprocessor. g. [11,12]). In addition, GPU has already become commonplace even in a low-end PC. Consequently, our scheme J. Cao et al. ): ISPA 2004, LNCS 3358, pp.
Springer-Verlag Berlin Heidelberg 2004 A Parallel Reed-Solomon Decoder on the Imagine Stream Processor 29 pose processor that acts as the host. It contains host interface, stream controller, streaming memory system, microcontroller, 128k stream register file (SRF), eight arithmetic clusters, local register file (LRF) and network interface. Each cluster consists of eight functional units: 3 adders, 2 multipliers, 1 divide/square root unit, 1 communication unit and 1 local scratch-pad memory. The input of each functional unit is provided by LRF in a cluster.