By A. Lopatkin
Predstavleny vozmozhnosti sistemy P-CAD 2004 pri proektirovanii pechatnykh plat. Opisany osnovnye priemy proektirovaniya pechatnykh plat: nastrojka skhemnogo redaktora, sozdanie formatok i simvolov komponentov, vvod mnogolistovykh skhem, verifikatsiya i raspechatka skhem, peredacha dannykh iz skhemnogo redaktora v redaktor pechatnykh plat.Rassmatrivayutsya razlichnye instrumenty: ruchnoj i interaktivnoj trassirovki, zadaniya i proverki konstruktorsko-tekhnologicheskikh norm, raspechatki chertezhej pechatnykh plat. Bol'shoe vnimanie udeleno vedeniyu bibliotek i sozdaniyu raznoobraznykh komponentov, v tom chisle mnogosektsionnykh s odnorodnymi i neodnorodnymi sektsiyami, obschimi i sdvoennymi vyvodami, komponentov, imeyuschikh neskol'ko posadochnykh mest. Rassmatrivayutsya metodiki poiska komponentov v bibliotekakh po zadannym kriteriyam, dobavleniya i redaktirovaniya tekstovykh atributov komponentov, sposoby konvertatsii bibliotek, sozdannykh v predyduschikh versiyakh sistemy. Dlya spetsialistov promyshlennykh predpriyatij i studentov tekhnicheskikh vuzov
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Extra resources for P-CAD 2004
5 results and observations of different Hadoop MapReduce jobs in Hadoop Yarn Single node as well as multi-cluster node, and Sect. 6, conclusion and future work. 2 Related Work Maurya et al.  discuss various MapReduce applications such as Pi, Wordcount, Grep, TeraSort, and show experimental results with the ﬁnding that the number of nodes increases as the execution time decreases. Joshi  executed and evaluated different Hadoop workloads using various hardware and software tuning techniques including BIOS, OS, JVM, and Hadoop conﬁguration parameters tuning.
J. L. 30 GHz and 4 GB of RAM. , softwares were installed in each of the nodes. 5 Results and Observations In this section, our observations of experiments performed on the Apache Hadoop Yarn MapReduce framework on single cluster node are presented. After evaluating the performance, jobs are classiﬁed based on various classes such as low CPU intensive job and high CPU intensive job based on CPU utilization (%). Based on classiﬁcation Apache Hadoop Yarn MapReduce jobs are executed on multi-cluster environment and evaluated performance.
A1 ð1; 1 : 8Þ ¼ f31; 22; 37; 57; 46; 34; 27; 20g Consider window size equals to ﬁve. First row of A1 contains eight elements. Slide this window of size ﬁve to entire row to ﬁnd local maxima. From the above Figs. 3, 4, 5 and 6, at coordinates (1, 4) local maxima is found. Coordinates (1, 4) means ﬁrst row and fourth column. Coordinates (1, 4) is temporary considered as center candidate. Coordinates (1, 4) has to pass additional test to conﬁrm his candidature. Extend the concept of local maxima to the different accumulators.
P-CAD 2004 by A. Lopatkin