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 Java 7 measurements

This table shows 4 measurements - CPU Time, Elapsed Time, Memory and Code.

Each row shows those measurements for a particular Java 7 program with a particular command-line input value N.

This table shows the current Java 7 programs.

Program Source Code	N	CPU secs	Elapsed secs	Memory KB	Code B
binary-trees  #2	12	0.14	0.14	?	603
binary-trees  #2	16	0.96	0.98	148,204	603
binary-trees  #2	20	16.37	16.42	499,404	603
binary-trees	12	0.13	0.13	?	1007
binary-trees	16	0.80	0.82	142,924	1007
binary-trees	20	20.06	20.11	545,544	1007
binary-trees-redux  #2	12	0.13	0.14	?	607
binary-trees-redux  #2	16	0.89	0.90	61,512	607
binary-trees-redux  #2	20	101.10	101.17	155,008	607
chameneos-redux  #2	60,000	0.26	0.27	416	1429
chameneos-redux  #2	600,000	1.36	1.37	32,748	1429
chameneos-redux  #2	6,000,000	11.97	11.98	34,168	1429
chameneos-redux  #3	60,000	0.97	0.98	25,664	1267
chameneos-redux  #3	600,000	6.92	6.93	35,800	1267
chameneos-redux  #3	6,000,000	65.13	65.16	35,504	1267
chameneos-redux	60,000	0.26	0.27	416	1770
chameneos-redux	600,000	0.69	0.70	22,916	1770
chameneos-redux	6,000,000	3.34	3.35	23,096	1770
fannkuch-redux  #2	10	0.51	0.51	17,468	514
fannkuch-redux  #2	11	6.15	6.16	17,536	514
fannkuch-redux  #2	12	76.62	76.64	17,560	514
fannkuch-redux	10	0.52	0.52	17,100	1282
fannkuch-redux	11	5.26	5.27	17,100	1282
fannkuch-redux	12	70.18	70.20	17,168	1282
fasta  #4	250,000	0.16	0.17	?	1507
fasta  #4	2,500,000	0.67	0.67	17,544	1507
fasta  #4	25,000,000	5.18	5.19	18,132	1507
fasta  #2	250,000	0.21	0.22	420	1240
fasta  #2	2,500,000	1.02	1.03	18,964	1240
fasta  #2	25,000,000	8.13	8.14	17,696	1240
fasta-redux  #3	250,000	0.12	0.12	?	1443
fasta-redux  #3	2,500,000	0.39	0.39	672	1443
fasta-redux  #3	25,000,000	2.55	2.56	17,264	1443
k-nucleotide  #2	250,000	1.04	1.04	48,820	1602
k-nucleotide  #2	2,500,000	5.80	5.97	97,624	1602
k-nucleotide  #2	25,000,000	50.50	50.55	451,208	1602
k-nucleotide  #3	250,000	1.00	1.01	40,024	1630
k-nucleotide  #3	2,500,000	6.34	6.35	145,424	1630
k-nucleotide  #3	25,000,000	50.82	50.88	448,200	1630
mandelbrot  #3	1,000	0.32	0.33	416	903
mandelbrot  #3	4,000	2.82	2.83	19,404	903
mandelbrot  #3	16,000	41.86	41.88	69,880	903
mandelbrot	1,000	0.31	0.31	416	665
mandelbrot	4,000	2.88	2.88	17,280	665
mandelbrot	16,000	43.26	43.27	17,280	665
mandelbrot  #6	1,000	0.20	0.22	676	802
mandelbrot  #6	4,000	1.84	1.85	19,236	802
mandelbrot  #6	16,000	27.54	27.55	69,684	802
mandelbrot  #2	1,000	0.19	0.20	?	796
mandelbrot  #2	4,000	1.61	1.62	18,896	796
mandelbrot  #2	16,000	23.79	23.80	69,816	796
meteor-contest  #2	2,098	0.39	0.39	23,548	5177
meteor-contest	2,098	0.85	0.86	20,204	4368
n-body  #2	500,000	0.32	0.32	672	1424
n-body  #2	5,000,000	2.34	2.34	17,016	1424
n-body  #2	50,000,000	22.51	22.52	17,016	1424
pidigits  #3	2,000	1.28	1.29	41,480	1826
pidigits  #3	6,000	3.15	3.16	45,472	1826
pidigits  #3	10,000	5.90	5.91	63,344	1826
pidigits	2,000	1.30	1.31	159,000	800
pidigits	6,000	6.70	6.73	356,612	800
pidigits	10,000	18.97	19.02	370,260	800
pidigits  #2	2,000	Failed			938
pidigits  #4	2,000	1.28	1.30	39,676	1808
pidigits  #4	6,000	3.22	3.22	47,480	1808
pidigits  #4	10,000	5.98	5.99	52,096	1808
regex-dna  #6	50,000	0.79	0.80	43,524	1410
regex-dna  #6	500,000	2.82	2.82	80,624	1410
regex-dna  #6	5,000,000	Failed			1410
regex-dna  #2	50,000	0.77	0.79	26,628	1534
regex-dna  #2	500,000	3.82	3.83	102,696	1534
regex-dna  #2	5,000,000	32.79	32.84	746,248	1534
regex-dna  #4	50,000	0.71	0.72	32,196	921
regex-dna  #4	500,000	3.20	3.20	86,496	921
regex-dna  #4	5,000,000	23.44	23.49	822,060	921
regex-dna  #7	50,000	0.53	0.56	20,052	1284
regex-dna  #7	500,000	2.66	2.75	29,324	1284
regex-dna  #7	5,000,000	23.11	23.17	561,732	1284
regex-dna  #5	50,000	0.88	0.89	28,380	929
regex-dna  #5	500,000	3.38	3.39	96,860	929
regex-dna  #5	5,000,000	24.08	24.14	766,760	929
reverse-complement  #7	250,000	0.32	0.46	21,036	1640
reverse-complement  #7	2,500,000	0.44	1.08	43,592	1640
reverse-complement  #7	25,000,000	1.84	8.88	266,488	1640
reverse-complement  #6	250,000	0.14	0.14	?	745
reverse-complement  #6	2,500,000	0.31	0.32	784	745
reverse-complement  #6	25,000,000	1.79	1.83	514,504	745
reverse-complement  #4	250,000	0.16	0.16	?	592
reverse-complement  #4	2,500,000	0.40	0.40	788	592
reverse-complement  #4	25,000,000	2.49	2.52	473,352	592
reverse-complement  #3	250,000	0.20	0.22	420	1661
reverse-complement  #3	2,500,000	0.43	0.44	50,412	1661
reverse-complement  #3	25,000,000	2.64	2.66	298,040	1661
spectral-norm	500	0.26	0.27	416	514
spectral-norm	3,000	5.14	5.15	17,804	514
spectral-norm	5,500	17.00	17.00	18,320	514
spectral-norm  #2	500	0.28	0.28	416	950
spectral-norm  #2	3,000	5.30	5.31	17,560	950
spectral-norm  #2	5,500	17.53	17.54	17,560	950
thread-ring  #3	500,000	2.65	2.67	30,540	530
thread-ring  #3	5,000,000	23.07	23.10	38,588	530
thread-ring  #3	50,000,000	221.09	221.26	290,804	530

 Java 7 : ubiquitous jit server virtual machine 

java version "1.7.0_11"
Java(TM) SE Runtime Environment (build 1.7.0_11-b21)
Java HotSpot(TM) 64-Bit Server VM (build 23.6-b04, mixed mode)

Home Page: Java SE at a Glance

Download: Java SE Downloads

Let's see how much, or how little, the time taken to invoke the JVM might contribute to the usual Java program times shown in the benchmarks game. Here are some additional (Intel® Q6600® quad-core) elapsed time measurements, taken after the Java programs started and before they exited.

In the first case (Cold), we simply started and measured the program 66 times; and then discarded the first measurement leaving 65 data points.

   public static void main(String[] args){
      for (int i=0; i<1; ++i){ 
         System.gc(); 
         long t1 = System.nanoTime();
         nbody.program_main(args);
         long t2 = System.nanoTime();
         System.err.println( String.format( "%.6f", (t2 - t1) * 1e-9 ) );         
      }
   }

In the second case (Warmed), we started the program once and repeated measurements again and again and again 66 times without restarting the JVM; and then discarded the first measurement leaving 65 data points.

   public static void main(String[] args){
      for (int i=0; i<66; ++i){ 
         System.gc(); 
         long t1 = System.nanoTime();
         nbody.program_main(args);
         long t2 = System.nanoTime();
         System.err.println( String.format( "%.6f", (t2 - t1) * 1e-9 ) );         
      }
   }

Compare these additional measurements against the usual Java program measurements shown in the benchmarks game --

"1.7.0_06" Java HotSpot(TM) 64-Bit Server VM
System.nanoTime()	1) Cold		2) Warmed
	mean	σ	mean	σ	usual
meteor contest	0.0118s	0.0007	0.0016s	0.0002	0.22s
fasta-redux	2.45s	0.00	2.32s	0.00	2.51s
spectral-norm	4.44s	0.02	4.20s	0.16	4.51s
pidigits	4.69s	0.09	4.44s	0.05	4.61s
fasta	5.07s	0.46	4.84s	0.02	5.13s
chameneos-redux	5.84s	0.46	5.70s	0.48	5.65s
mandelbrot	7.93s	0.23	7.99s	0.01	7.02s
k-nucleotide	8.09s	0.28	--	--	8.05s
regex-dna	8.65s	0.27	--	--	8.61s
binary-trees	10.54s	0.28	7.66s	0.16	9.08s
fannkuch-redux	16.89s	1.32	17.26s	0.10	17.38s
nbody	22.43s	0.00	22.41s	0.00	22.50s
binary-trees-redux	34.15s	0.39	33.93s	0.31	33.38s

The largest and most obvious effects of bytecode loading and dynamic optimization can be seen with the meteor-contest program which only runs for a fraction of a second.

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