Java 7 vs Lisp SBCL | Computer Language Benchmarks Game

1 : Are the Java 7 programs faster? At a glance.

Each chart bar shows, for one unidentified benchmark, how much the fastest Java 7 program used compared to the fastest Lisp SBCL program.

These are not the only compilers and interpreters. These are not the only programs that could be written. These are not the only tasks that could be solved. These are just 10 tiny examples.

2 : Are the Java 7 programs faster? Approximately.

Each table row shows, for one named benchmark, how much the fastest Java 7 program used compared to the fastest Lisp SBCL program.

Java 7 used what fraction? used how many times more?
Benchmark	Time	Memory	Code
k-nucleotide †	¹/₅	5×	±
fasta-redux	¹/₅	2×	±
reverse-complement †	¹/₃	2×	2×
mandelbrot	¹/₂	2×	¹/₃
regex-dna	¹/₂	±	¹/₂
binary-trees †	¹/₂	2×	2×
fasta	¹/₂	2×	±
n-body	¹/₂	3×	±
spectral-norm	±	3×	±
fannkuch-redux	±	±	±
pidigits †	2×	10×	2×

Java 7 used what fraction? used how many times more?
Time-used	\|-	\|---	25%	median	75%	---\|	-\|
(Elapsed secs)	¹/₅	¹/₅	¹/₃	¹/₂	±	2×	2×

† possible mismatch - one-core program compared to multi-core program.

± read the measurements and then read the program source code.

3 : Are the Java 7 programs faster? Measurements.

These are not the only tasks that could be solved. These are just 10 tiny examples. These are not the only compilers and interpreters. These are not the only programs that could be written.

For each named benchmark, measurements of the fastest Java 7 program are shown for comparison against measurements of the fastest Lisp SBCL program.

Program Source Code	CPU secs	Elapsed secs	Memory KB	Code B	≈ CPU Load
k-nucleotide
Java 7	44.83	12.93	494,264	1602	84% 81% 85% 99% †
Lisp SBCL	69.14	69.22	105,244	2272	0% 0% 0% 100% †
fasta-redux
Java 7	1.90	1.85	14,500	1443	1% 2% 3% 100%
Lisp SBCL	9.24	9.25	9,356	2005	0% 1% 0% 100%
reverse-complement
Java 7	2.93	1.52	294,784	1661	32% 59% 58% 47% †
Lisp SBCL	4.78	4.79	164,812	896	0% 98% 2% 0% †
mandelbrot
Java 7	27.14	6.90	67,612	796	98% 98% 99% 99%
Lisp SBCL	67.13	16.95	35,300	2459	99% 99% 99% 99%
regex-dna
Java 7	27.09	9.43	708,060	929	65% 83% 78% 63%
Lisp SBCL	46.12	22.59	946,444	1948	35% 42% 43% 86%
binary-trees
Java 7	21.35	9.21	525,420	1007	50% 70% 48% 65% †
Lisp SBCL	20.41	20.45	220,104	649	83% 0% 18% 0% †
fasta
Java 7	5.07	4.99	14,976	1507	2% 0% 100% 1%
Lisp SBCL	8.17	8.18	8,272	1751	0% 0% 0% 100%
n-body
Java 7	24.41	24.39	13,976	1424	0% 0% 0% 100%
Lisp SBCL	37.55	37.56	4,088	1398	0% 0% 0% 100%
spectral-norm
Java 7	16.39	4.21	14,740	950	97% 97% 98% 99%
Lisp SBCL	15.75	4.02	4,488	906	98% 98% 99% 98%
fannkuch-redux
Java 7	67.91	17.17	14,460	1282	98% 99% 100% 100%
Lisp SBCL	57.30	14.77	14,112	1518	98% 99% 97% 94%
pidigits
Java 7	11.75	5.12	46,488	1826	70% 67% 56% 39% †
Lisp SBCL	2.76	2.77	4,884	1073	1% 0% 0% 100% †

† possible mismatch - one-core program compared to multi-core program.

4 : Are there other Java 7 programs for these benchmarks?

Remember - those are just the fastest Java 7 and Lisp SBCL programs measured on this OS/machine. Check if there are other implementations of these benchmark programs for Java 7.

Maybe one of those other Java 7 programs is fastest on a different OS/machine.

5 : Are there other faster programs for these benchmarks?

Remember - those are just the fastest Java 7 and Lisp SBCL programs measured on this OS/machine. Check if there are faster implementations of these benchmark programs for other programming languages.

Maybe one of those other programs is fastest on a different OS/machine.

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) 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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