last-modified: Feb 6 11:55, 2002 JST
Applied benchmarks:
Evaluated Java runtime systems:
The computer that run all benchmark programs is as follows:
All results are also available in Mirosoft Excel book format: `JIT-eval-020120.xls'.
SPEC JVM98 shows scores for each benchmark program and the geometric mean of them. Higher number is better.
[953x556]
Runtime system _227_ mtrt _202_ jess _201_ compress _209_ db _222_ mpegaudio _228_ jack _213_ javac Geometric Mean HotSpot Server VM 147 160 11.7 21.7 167 150 15.1 58.2 ORP (O3 JIT) 73.4 68.5 96.3 20.6 161 69.4 19.4 57.9 ORP (O1 JIT) 57.3 64.5 82.7 20.0 143 70.4 14.8 51.1 IBM JDK 1.3 107 78.3 11.7 31.6 179 104 15.0 50.8 Jikes RVM (FastAdaptiveSemispace) 47.0 57.7 74.4 26.5 77.9 63.1 33.3 50.8 HotSpot Client VM 76.0 117 10.8 23.0 114 126 13.7 46.0 Jikes RVM (FastSemispace) 56.0 47.8 75.5 17.5 91.0 46.4 24.9 45.0 Jikes RVM (OptOptSemispace) 32.7 21.7 64.3 15.6 61.9 22.5 11.1 27.2 OpenJIT 33.6 33.0 10.9 10.9 61.0 37.4 7.17 21.6 shuJIT 20.9 29.3 10.3 11.1 56.8 33.1 7.00 19.1 Kaffe 15.2 11.6 73.9 14.4 36.0 9.91 11.7 18.7 TYA 15.7 26.7 9.71 9.40 34.3 29.4 6.84 16.0 sunwjit.so 8.01 12.1 10.6 N/A 43.3 27.2 6.7 14.2 interpreter 11.6 11.9 4.88 6.59 7.62 17.0 8.93 9.09
We specified the `-large' option to run the large problem size version. SciMark 2.0 shows results in MFlops (mega floating-point operations per second) and the composite score, which is the arithmetic mean of them. Higher number is better because results were in MFlops. Therefore, SOR, which shows relatively higher value, affects the composite score. Oppositely, FFT and Monte Carlo are not counted very much.
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Runtime system FFT (1048576) SOR (1000x1000) Monte Carlo Sparse matmult (N=100000, nz=1000000) LU (1000x1000) Composite Score HotSpot Client VM 19.6 249 28.6 88.4 169 111 GCJ (-O2 with .java) 18.9 229 6.56 138 153 109 GCJ (-O2 with .class) 18.7 232 7.28 119 158 107 IBM JDK 1.3.0 19.9 145 20.3 158 167 102 ORP (O3 JIT) 11.3 152 27.1 107 60.5 715 HotSpot Server VM 20.2 157 35.9 20.5 95.6 65.8 Jikes RVM (FastSemispace) 11.2 132 3.35 76.2 54.5 55.4 Jikes RVM (OptOptSemispace) 11.0 128 3.32 72.6 53.2 53.6 ORP (O1 JIT) 10.3 116 22.9 54.7 42.6 49.3 Jikes RVM (FastAdaptiveSemispace) 7.91 130 3.42 72.5 16.2 46.0 GCJ (-O0 with .class) 10.8 74.2 5.27 47.0 44.9 36.4 sunwjit.so 16.2 80.0 4.08 67.2 10.4 35.6 OpenJIT 6.77 70.4 5.34 65.0 13.8 32.3 GCJ (-O0 with .java) 11.0 68.5 5.55 28.6 48.0 32.3 Kaffe 9.83 66.9 3.41 38.8 36.8 31.1 shuJIT 8.91 52.6 5.46 22.4 34.3 24.7 TYA 6.56 27.2 3.83 25.2 29.4 18.4 interpreter 2.69 13.4 3.10 6.55 18.7 8.88
1000 x 1000 500 x 500 ![]()
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[875x573] [905x572]
MFlops second IBM JDK 1.3.0 201.042 3.33 HotSpot Server VM 199.9 3.35 HotSpot Client VM 174.45 3.83 GCJ (-O2 with .java) 162.101 4.13 GCJ (-O2 with .class) 158.414 4.22 OpenJIT 68.977 9.69 GCJ (-O0 with .java) 51.098 13.09 GCJ (-O0 with .class) 45.484 14.70 shuJIT 42.94 15.57 Jikes RVM (OptOptSemispace) 42.509 15.73 ORP (O1 JIT) 39.618 16.88 Jikes RVM (FastSemispace) 39.326 17.00 Jikes RVM (FastAdaptiveSemispace) 39.115 17.01 TYA 38.535 17.35 ORP (O3 JIT) 33.681 19.85 Kaffe 29.966 22.31 interpreter 11.888 56.25 sunwjit.so 8.794 76.03
MFlops second GCJ (-O2 with .java) 175.751 0.48 IBM JDK 1.3.0 170.047 0.49 GCJ (-O2 with .class) 167.667 0.50 HotSpot Client VM 158.176 0.53 HotSpot Server VM 152.702 0.55 OpenJIT 66.906 1.25 Jikes RVM (FastSemispace) 61.282 1.368 Jikes RVM (OptOptSemispace) 59.71 1.40 Jikes RVM (FastAdaptiveSemispace) 54.121 1.549 GCJ (-O0 with .java) 52.925 1.58 ORP (O3 JIT) 48.431 1.73 GCJ (-O0 with .class) 46.626 1.80 ORP (O1 JIT) 41.257 2.03 shuJIT 40.894 2.04 Kaffe 38.21 2.19 TYA 37.934 2.21 interpreter 11.373 7.15 sunwjit.so 8.767 9.56
[878x568]
IBM JDK 1.3.0 7205 GCJ (-O2 with .java) 6823 ORP (O3 JIT) 6736 GCJ (-O2 with .class) 6597 HotSpot Server VM 6462 ORP (O1 JIT) 6228 Jikes RVM (OptOptSemispace) 5925 Jikes RVM (FastSemispace) 5817 Kaffe 4815 HotSpot Client VM 4461 OpenJIT 3442 sunwjit.so 3127 shuJIT 2915 GCJ (-O0 with .java) 2669 TYA 2505 GCJ (-O0 with .class) 2275 Jikes RVM (FastAdaptiveSemispace) 1438 interpreter 563