By Sanguthevar Rajasekaran

The facility of parallel computing to approach huge info units and deal with time-consuming operations has ended in extraordinary advances in organic and clinical computing, modeling, and simulations. Exploring those contemporary advancements, the guide of Parallel Computing: types, Algorithms, and purposes presents complete insurance on all points of this box.

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Extra resources for Handbook of Parallel Computing: Models, Algorithms and Applications (Chapman & Hall CRC Computer & Information Science Series)

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Not only science and technology but also everyday life provide many instances demonstrating time-varying complexity. Thus, for example, 1. An illness may get better or worse with time, making it more or less amenable to treatment. 2. Biological and software viruses spread with time making them more difficult to cope with. 3. Spam accumulates with time making it more challenging to identify the legitimate email “needles” in the “haystack” of junk messages. 4. , a spaceship racing toward Mars). 5.

3 Number of Operations Required to t Complete Stage i When C(t ) = 22 Stage i ti C(ti ) S(i) 0 1 0 C(0) 22 2 0+2 C(2) 22 3 2 + 16 C(18) 22 2 18 Since S(i) > i−1 j=1 S(j), the total number of operations required by i stages is less than 2S(i), that is, O(S(i)). Here we observe again that while C(t ) = 2C(t − 1), the number of operations required by S(i), for i > 2, increases significantly faster than double those required by all previous stages combined. t 3. For t ≥ 0, C(t ) = 22 . 3 illustrates ti , C(ti ), and S(i), for 1 ≤ i ≤ 3.

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