In mathematics, tessellation can be generalized to higher dimensions and a variety of geometries.Ī periodic tiling has a repeating pattern. EscherĪ tessellation or tiling is the covering of a surface, often a plane, using one or more geometric shapes, called tiles, with no overlaps and no gaps. In addition, the concept of pipelining is used both in arithmetic processors and entire systems, i.e., vector machines to achieve parallelism by overlap of instruction interpretation and arithmetic processing.A wall sculpture in Leeuwarden celebrating the artistic tessellations of M. input-output facilities but carry out calculations with separate instruction and data streams array processors used to augment a host sequential type machine which executes a common instruction stream on many processors and associative processors which again require a host machine and vary from bit to word oriented processors which alternatively select and compute results for many data streams under control of correlation and arithmetic instruction streams. Several approaches to hardware parallelism have been taken including multiprocessors which share common storage and. The use of parallelism to achieve greater processing thruput for computational problems exceeding the capability of present day large scale sequential pipelined data processing systems has been proposed and in some instances hardware employing these concepts has been built. Another benefit from the sav- ing computation is the saved power consumption on the pre- dictor which is also an important factor in nowadays micro- processor. Using O-GEHL predictor as example, the sim- ulation results show that with the storage budget changing from 32kbits to 512Kbits our scheme can save up to 18.2% of computation for a prediction in average as while as only losing up to 1.75% accuracy. This scheme is orthogonal to the other schemes such as ahead pipelining.
In this paper, we propose a Partial-Sum-Global- Update scheme to decrease the number of computation of perceptron predictor with marginal accuracy losing. which most comes from the computation needed by the predicting process. One shortcoming of percep- tron branch predictor is the high prediction latency. State-of-the- art researches have shown that perceptron branch predictor can obtain a higher accuracy than the existing widely used table based branch predictor. With the pipeline deepen and issue width widen, the ac- curacy of branch predictor becomes more and more impor- tant to the performance of a microprocessor. This makes subdivision surfaces an even more attractive tool for free-form surface modeling. Therefore, our method allows many algorithms developed for parametric surfaces to be applied to Catmull-Clark subdivision surfaces. The cost of our evaluation scheme is comparable to that of a bi-cubic spline. We have used our implementation to compute high quality curvature plots of subdivision surfaces. Also, our technique is both efficient and easy to implement.
In particular, on the regular part of the control mesh where Catmull-Clark surfaces are bi-cubic B-splines, the eigenbasis is equal to the power basis. We show that the surface and all its derivatives can be evaluated in terms of a set of eigenbasis functions which depend only on the subdivision scheme and we derive analytical expressions for these basis functions.
In this paper we disprove the belief widespread within the computer graphics community that Catmull-Clark subdivision surfaces cannot be evaluated directly without explicitly subdividing.