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| Add your row to the table below, following the examples already there. Please write down the ratios for both time and comparisons (comp) as floats with '''exactly two digits of precision'''. In the first column, write your first name, or name1+name2 if you work in a group. Speedup is always measured as time/comparisons_for_baseline / time_for_your_version or comparisons_for_baseline / comparisons_for_your_version (<1 = slower, >1 faster). |
Add your row to the table below, following the examples already there. ''Time B'' is the time for the baseline. ''Time Q'' is the time using the q-gram index. ''#PEDs Q'' is the number of PED computations done with the q-gram based algorithm. |
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| ||'''Name''' || '''The H''' time ||'''The H''' comp.|| '''Ter,inator''' time ||'''Ter,inator''' comp.|| '''Figct Cl''' time ||'''Figct Cl''' comp.|| ||Elmar || 3.09 || 3.44 || 81.36 || 136.75 || 1905.78 || 21196.12 || |
In the first column, write your first name, or name1+name2 if you work in a group. '''Note''': The original dataset had encoding issues. Please measure your times using the corrected [[http://ad-teaching.informatik.uni-freiburg.de/InformationRetrievalWS1516/movie-titles-ascii.txt|dataset]] we uploaded {{{ #!html <span style="color:red">(Sunday, November 22nd, 17:35h).</span> }}} ||<tablewidth="100%" tableheight="auto" style="text-align:center" |2> '''Name''' |||||| '''Query: The H''' |||||| '''Query: Terinator''' |||||| '''Query: Figct Cl''' ||<style="text-align:center" |2>'''Processor / RAM''' ||||<style="text-align:center" |2>'''Language''' || ||<:> '''Time B''' ||<style="text-align:center"> '''Time Q''' ||<:> '''#PEDs Q''' ||<:> '''Time B''' ||<:> '''Time Q''' ||<:> '''#PEDs Q''' ||<:> '''Time B''' ||<:> '''Time Q''' ||<:> '''#PEDs Q''' || ||Elmar ||<:> 3,912ms ||<:> 1,218ms ||<:> 48,521 ||<:> 12,254ms ||<:> 107ms ||<:> 1,206 ||<:> 8,060ms ||<:> 3ms ||<:> 5 || Intel i5-2520M @ 2.50GHz / 8GB || Python || ||Matia ||<:> 3,239ms ||<:> 1,261ms ||<:> 48,521 ||<:> 11,217ms ||<:> 265ms ||<:> 1,206 ||<:> 7,343ms ||<:> 17ms ||<:> 5 || Intel i5 @ 2.60GHz / 8GB || Python || ||Marco ||<:> 265ms ||<:> 57ms ||<:> 31,466 ||<:> 501ms ||<:> 81ms ||<:> 31,518 ||<:> 264ms ||<:> 15ms ||<:> 5301 || Intel i5 @ 2.60GHz / 8GB || Java || ||Raghu ||<:> 45,120ms ||<:> 14,155ms ||<:> 48,521 ||<:> 154,070ms ||<:> 2,558ms ||<:> 1,206 ||<:> 101,053ms ||<:> 125ms ||<:> 5 || AMD A6 @ 1.80 GHz / 4GB || Python || ||Jay ||<:> 4,168ms ||<:> 919ms ||<:> 34,797 ||<:> 13,249ms ||<:> 3,379ms ||<:> 41,125 ||<:> 8,816ms ||<:> 639ms ||<:> 11,783 || i7-4510U @ 2.00GHz / 8GB || Python || ||Evgeny + Numair||<:> 4,875ms ||<:> 1,834ms ||<:> 49,229 ||<:> 10,630ms ||<:> 271ms ||<:> 1,240 ||<:> 8,298ms ||<:> 19ms ||<:> 8 || Intel i5 @ 3.50 GHz - 16 GB RAM || Python || ||Frank ||<:> 2,879ms ||<:> 1,096ms ||<:> 48,521 ||<:> 10,862ms ||<:> 280ms ||<:> 1,206 ||<:> 6,781ms ||<:> 18ms ||<:> 5 || i5-4690 @ 3.50GHz / 8GB || Python || ||Elke ||<:> 5,816ms ||<:> 1,040ms ||<:> 48,521 ||<:> 11,752ms ||<:> 99ms ||<:> 1,206 ||<:> 9,662ms ||<:> 3ms ||<:> 5 || Intel i5 @ 2.20GHz / 8GB || Python || ||Elias + Max ||<:> 9,072ms ||<:> 3,320ms ||<:> 50,918 ||<:> 18,569ms ||<:> 6,808ms ||<:> 52,763 ||<:> 14,731ms ||<:> 1,078ms ||<:> 11,849 || Intel i7 @ 2.90GHz / 8GB || Python || |
Results for Exercise Sheet 5 (q-Gram Index)
Add your row to the table below, following the examples already there. Time B is the time for the baseline. Time Q is the time using the q-gram index. #PEDs Q is the number of PED computations done with the q-gram based algorithm.
In the first column, write your first name, or name1+name2 if you work in a group.
Note: The original dataset had encoding issues. Please measure your times using the corrected dataset we uploaded (Sunday, November 22nd, 17:35h).
Name |
Query: The H |
Query: Terinator |
Query: Figct Cl |
Processor / RAM |
Language |
|||||||
Time B |
Time Q |
#PEDs Q |
Time B |
Time Q |
#PEDs Q |
Time B |
Time Q |
#PEDs Q |
||||
Elmar |
3,912ms |
1,218ms |
48,521 |
12,254ms |
107ms |
1,206 |
8,060ms |
3ms |
5 |
Intel i5-2520M @ 2.50GHz / 8GB |
Python |
|
Matia |
3,239ms |
1,261ms |
48,521 |
11,217ms |
265ms |
1,206 |
7,343ms |
17ms |
5 |
Intel i5 @ 2.60GHz / 8GB |
Python |
|
Marco |
265ms |
57ms |
31,466 |
501ms |
81ms |
31,518 |
264ms |
15ms |
5301 |
Intel i5 @ 2.60GHz / 8GB |
Java |
|
Raghu |
45,120ms |
14,155ms |
48,521 |
154,070ms |
2,558ms |
1,206 |
101,053ms |
125ms |
5 |
AMD A6 @ 1.80 GHz / 4GB |
Python |
|
Jay |
4,168ms |
919ms |
34,797 |
13,249ms |
3,379ms |
41,125 |
8,816ms |
639ms |
11,783 |
i7-4510U @ 2.00GHz / 8GB |
Python |
|
Evgeny + Numair |
4,875ms |
1,834ms |
49,229 |
10,630ms |
271ms |
1,240 |
8,298ms |
19ms |
8 |
Intel i5 @ 3.50 GHz - 16 GB RAM |
Python |
|
Frank |
2,879ms |
1,096ms |
48,521 |
10,862ms |
280ms |
1,206 |
6,781ms |
18ms |
5 |
i5-4690 @ 3.50GHz / 8GB |
Python |
|
Elke |
5,816ms |
1,040ms |
48,521 |
11,752ms |
99ms |
1,206 |
9,662ms |
3ms |
5 |
Intel i5 @ 2.20GHz / 8GB |
Python |
|
Elias + Max |
9,072ms |
3,320ms |
50,918 |
18,569ms |
6,808ms |
52,763 |
14,731ms |
1,078ms |
11,849 |
Intel i7 @ 2.90GHz / 8GB |
Python |
|