Differences between revisions 11 and 84 (spanning 73 versions)

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

Intel i5-2520M @ 2.50GHz / 8GB

Python

Matia

3,239ms

1,261ms

48,521

11,217ms

265ms

1,206

7,343ms

17ms

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

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

Intel i5 @ 3.50 GHz - 16 GB RAM

Python

Frank

2,879ms

1,096ms

48,521

10,862ms

280ms

1,206

6,781ms

18ms

i5-4690 @ 3.50GHz / 8GB

Python

Elke

5,816ms

1,040ms

48,521

11,752ms

99ms

1,206

9,662ms

3ms

Intel i5 @ 2.20GHz / 8GB

Python

Elias + Max

9,072ms

3,320ms

50,918

18,569ms

408ms

2,595

14,731ms

111ms

1,172

Intel i7 @ 2.90GHz / 8GB

Python

/'fræŋk/

425ms

222ms

48,521

738ms

53ms

1,206

550ms

14ms

Intel i7 @ 2.50GHz / 16GB

Python (pypy)

HuiHui

42,823ms

15,120ms

48,521

86,142ms

5,068ms

5,335

68,242ms

2,381ms

5,799

Intel Core i5 @ 1.7 GHz - 8GB RAM

Python

Erik

9,275ms

3,653ms

48,521

19,176ms

448ms

1,206

14,924ms

5.7ms

Intel® Core™ i5-2520M CPU @ 2.50GHz / 4GB

Python

17,908ms

2,924ms

48,521

33,701ms

957ms

1,206

27,116ms

101ms

Intel Core i5-2435M CPU @ 2.40GHz / 8GB

Python

Daniel

9,888ms

3,644ms

48,521

26,517ms

504ms

1,206

19,804ms

64ms

Intel Pentium T4500 @ 2.3GHz / 4GB

Python

Sam

8,273ms

2,101ms

50,875

18,099ms

5,490ms

52,763

15.414ms

978ms

11,757

Intel Core i5-2520M CPU @ 2.50GHz / 8GB

Python

David

4,396ms

1,389ms

48,521

13,319ms

156ms

1,206

9,072ms

13ms

Intel Core i5 @ 2.2 GHz - 8GB RAM

Python

Zhiwei

831ms

289390ms

96900ms

335391ms

94525ms

21251ms

11153

2.6 GHz Intel Core i7

C++

Robin

767ms

253ms

48,489

1,700ms

33ms

1,179

1,251ms

2ms

Intel i7 @ 2.00GHz / 16GB

C++

-  ⇤ ← Revision 11 as of 2015-11-19 18:37:13 → 
  Size: 1549
  Editor: adpult
  Comment:
+   ← Revision 84 as of 2015-11-24 05:18:51 → ⇥
  Size: 5524
  Editor: 095-097-250-107
  Comment: +1
-Deletions are marked like this.
+Additions are marked like this.
 Line 3:
-Add your row to the table below, following the examples already there. Please write down the ratios for both time and number of PED computations (comp) as floats with '''exactly two digits of precision'''.
+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.
 Line 6:
-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). 
The first row shows you the queries to measure for.
-Line 9:
+Line 7:
-||<tablewidth="100%" tableheight="auto" style="text-align:center" |2> '''Name''' |||||| '''The H''' |||||| '''Ter,inator''' |||||| '''Figct Cl'''  ||<style="text-align:center" |2>'''Processor / RAM''' ||||<style="text-align:center" |2>'''Language''' ||
||<style="text-align:center">   '''baseline''' ||<style="text-align:center"> '''q-Gram index''' ||<style="text-align:center"> '''#computations''' ||<style="text-align:center">   '''baseline''' ||<style="text-align:center"> '''q-Gram index''' ||<style="text-align:center"> '''#computations''' ||<style="text-align:center">   '''baseline''' ||<style="text-align:center"> '''q-Gram index''' ||<style="text-align:center"> '''#computations''' ||
||Elmar      ||    3.09          ||   3.44          ||    81.36              ||   136.75             ||    1905.78          ||   21196.12         ||   136.75             ||    1905.78          ||   21196.12         || Intel i5-2520M @ 2.50GHz / 8GB || Python ||
+'''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      ||<:>    408ms     ||<:>   2,595        ||<:>    14,731ms      ||<:>    111ms       ||<:>   1,172           || Intel i7 @ 2.90GHz / 8GB || Python ||
||/'fræŋk/ ||<:>    425ms        ||<:>   222ms        ||<:>    48,521            ||<:>   738ms      ||<:>    53ms     ||<:>   1,206        ||<:>    550ms      ||<:>    14ms       ||<:>   5           || Intel i7 @ 2.50GHz / 16GB || Python (pypy) ||
||!HuiHui ||<:>    42,823ms        ||<:>   15,120ms        ||<:>    48,521            ||<:>   86,142ms      ||<:>    5,068ms     ||<:>   5,335        ||<:>    68,242ms      ||<:>    2,381ms       ||<:>   5,799          || Intel Core i5 @ 1.7 GHz - 8GB RAM || Python ||
||Erik      ||<:>    9,275ms        ||<:>   3,653ms        ||<:>    48,521            ||<:>   19,176ms      ||<:>    448ms     ||<:>   1,206        ||<:>    14,924ms      ||<:>    5.7ms       ||<:>   4           || Intel® Core™ i5-2520M CPU @ 2.50GHz  / 4GB || Python ||
|| Il ||<:>    17,908ms       ||<:>   2,924ms        ||<:>    48,521            ||<:>   33,701ms      ||<:>    957ms     ||<:>   1,206       ||<:>    27,116ms    ||<:>    101ms     ||<:>   5           || Intel Core i5-2435M CPU @ 2.40GHz / 8GB || Python ||
||Daniel    ||<:>  9,888ms  ||<:>  3,644ms  ||<:>  48,521  ||<:>  26,517ms  ||<:>  504ms  ||<:>  1,206  ||<:>  19,804ms  ||<:>  64ms  ||<:>  5  || Intel Pentium T4500 @ 2.3GHz / 4GB || Python ||
||Sam    ||<:>  8,273ms  ||<:>  2,101ms  ||<:>  50,875  ||<:>  18,099ms  ||<:>  5,490ms  ||<:>  52,763  ||<:>  15.414ms  ||<:>  978ms  ||<:>  11,757  || Intel Core i5-2520M CPU @ 2.50GHz / 8GB || Python ||
||David ||<:>  4,396ms  ||<:>  1,389ms  ||<:>  48,521  ||<:>  13,319ms  ||<:>  156ms  ||<:>  1,206  ||<:>  9,072ms  ||<:>  13ms  ||<:>  5  || Intel Core i5 @ 2.2 GHz - 8GB RAM || Python ||
||Zhiwei||<:> 831ms||<:> 289390ms||<:> 5||<:> 96900ms||<:> 335391ms||<:> 16||<:> 94525ms||<:> 21251ms||<:> 11153||2.6 GHz Intel Core i7||C++||
||Robin      ||<:>    767ms        ||<:>   253ms        ||<:>    48,489            ||<:>   1,700ms      ||<:>    33ms     ||<:>   1,179        ||<:>    1,251ms      ||<:>    2ms       ||<:>   5           || Intel i7 @ 2.00GHz / 16GB || C++ ||