Titlebook: VLSI Algorithms and Architectures; Aegean Workshop on C Filia Makedon,Kurt Mehlhorn,P. Spirakis Conference proceedings 1986 Springer-Verlag

HEIR

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尖牙

Two processor scheduling is in ,,ystems with arbitrary precedence constraints using a polynomial number of processors and running in time polylog in the size of the input. Whereas previous parallel solutions for the problem made extensive use of randomization, our algorithm is completely deterministic and based on an interesting it

疏忽

Parallel ear decomposition search (EDS) and st-numbering in graphs,algorithm is based on depth-first search (DFS). A known conjecture states that DFS, which is a key technique in designing serial algorithms, is not amenable to poly-log time parallelism using "around linearly" (or even polynomially) many processors. The first contribution of this paper is a general

伪造

A unifying framework for systolic designs,fewer processing elements than previously considered ones. The synthesis method consists of two parts: 1) deriving from the high-level problem specification a form more suitable to VLSI implementation; 2) mapping the new specification into physical hardware. The method also provides a unifying frame

向下五度才偏

Optimal tradeoffs for addition on systolic arrays,y, including:.For all combinations of the above constraints, we obtain optimal tradeoffs among the resources of area, pipeline delay, and worst-case time. It turns out that there is a subtle interplay among the constraints and some of our results seem counter intuitive. For instance, we show that al

CLIFF

ingenue

哥哥喷涌而出

,Diogenes, circa 1986 Ψαχνω για ενα τιμιo ανϑρoπo,graph and is . then, the book embedding is converted to an efficient fault-tolerant layout of the array. We survey here work on both stages of the methodology, highlighting recent progress and pointing out remaining challenges.

occult

Nonsequential computation and laws of nature,ted for. This attitude has persisted in common models for parallel computations. Wrongly, as we shall argue, since the laws of physics intrude forcefully when we want to obtain realistic estimates of the performance of parallel or distributed algorithms. First, we shall explain why it is reasonable

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