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||<tablestyle="width: 1052px; height: 95px;">'''Name''' ||'''Language''' ||'''Preproc.: dur./threshold/Sum |X(u)|''' ||'''Preproc.: dist(x,y)''' ||'''Dataset''' ||'''Hardware''' || ||Adrian,Kevin,Christoph ||C++ ||132s /966/1.4M ||539s ||Saarland ||Intel Xeon X5560 @ 2.8GHz || ||(*1) Eugen ||C++ ||13s /435/2.3M ||18s ||Saarland ||Intel i5 750 @ 2.7GHz || ||(*2) Eugen ||C++ ||9s /754/1.5M ||63s ||Saarland ||Intel i5 750 @ 2.7GHz || |
||<tablewidth="1052px" tableheight="95px">'''Name''' ||'''Language''' ||'''Preproc.: dur./threshold/Sum |X(u)|''' ||'''Preproc.: dist(x,y)''' || '''Query-Time / avg. |X(source)| * |Y(target)|''' || '''Dataset''' ||'''Hardware''' || ||Adrian,Kevin,Christoph ||C++ ||132s / 966 / 1.4M ||539s || ? ||Saarland ||Intel Xeon X5560 @ 2.8GHz || ||Adrian,Kevin,Christoph ||C++ ||5039s / 1123 / 89M ||2744s || ? ||!BaWü ||Intel Xeon X5560 @ 2.8GHz || ||(*1) Eugen ||C++ ||12s / 435 / 2.3M ||18s || 0.032ms / 174 ||Saarland ||Intel i5 750 @ 2.7GHz || ||(*2) Eugen ||C++ ||9s / 754 / 1.5M ||63s || 0.026ms / 112 ||Saarland ||Intel i5 750 @ 2.7GHz || || Mirko,Dirk,Kyanoush ||C++ ||9s / 2000 / 1.6M || 312s || 0.0046ms / 41 ||Saarland ||Intel !Core2Duo 2.4GHz || || Markus,Oleksii,Stefan ||Java ||480s / 500 / 1.3M || 240s || 0.045ms / ? ||Saarland ||Intel i5 M430 @ 2.27GHz || |
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1.6%(*1) and 0.87%(*2) local searches out of 100000 samples with an decrease in average search time of 61% | 1.6%(*1) and 0.87%(*2) local searches out of 100k samples |
Your experimental results for exercise sheet 6
Please sumarize your results from exercise sheet 6 in a row in the table below. In the first column, write your name (abbreviate if you are in a group, to keep it short). In the second column, put the programming language you used (C++ or Java). In the third column put the first preprocessing time (in seconds), the threshold (= number of transit nodes), the value Sum_u |X(u)|. In the fourth column put the preprocessing time which is needed to calculate dist(x,y) for all transit nodes x,y. In the fifth column put the name of the dataset you computed these average times on (preferably Ba-Wü, but if that didn't work Saarland). In the last column put the used hardware.
Name |
Language |
Preproc.: dur./threshold/Sum |X(u)| |
Preproc.: dist(x,y) |
Query-Time / avg. |X(source)| * |Y(target)| |
Dataset |
Hardware |
Adrian,Kevin,Christoph |
C++ |
132s / 966 / 1.4M |
539s |
? |
Saarland |
Intel Xeon X5560 @ 2.8GHz |
Adrian,Kevin,Christoph |
C++ |
5039s / 1123 / 89M |
2744s |
? |
BaWü |
Intel Xeon X5560 @ 2.8GHz |
(*1) Eugen |
C++ |
12s / 435 / 2.3M |
18s |
0.032ms / 174 |
Saarland |
Intel i5 750 @ 2.7GHz |
(*2) Eugen |
C++ |
9s / 754 / 1.5M |
63s |
0.026ms / 112 |
Saarland |
Intel i5 750 @ 2.7GHz |
Mirko,Dirk,Kyanoush |
C++ |
9s / 2000 / 1.6M |
312s |
0.0046ms / 41 |
Saarland |
Intel Core2Duo 2.4GHz |
Markus,Oleksii,Stefan |
Java |
480s / 500 / 1.3M |
240s |
0.045ms / ? |
Saarland |
Intel i5 M430 @ 2.27GHz |
1.6%(*1) and 0.87%(*2) local searches out of 100k samples