cache performance in java virtual machines: a … performance in java virtual machines: a study of...

Cache Performance in Java Virtual Machines: A Study of Constituent Phases

Anand S. Rajan Shiwen Hu and Juan RubioARM Inc. Laboratory for Computer Architecture

The University of Texas at Austin

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Cache Performance in JVMs: A Study of Constituent Phases

Motivation

� The execution of a Java program consists of distinct JVM phases� Class loading � Garbage collection � Execution

� Efficient execution of Java programs necessitates a comparative study of requirements and characteristics of JVM phases

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Outline

� Experimental methodology

� Instruction cache performance

� Data cache performance

� Impact on cache performance� Varying cache sizes� Varying application data sets

� Conclusion

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Methodology� L1 cache behavior of three JVM phases

� Class loading, garbage collection, and execution� Two execution modes: interpreted and JIT

� Experimental workloads: SPECjvm 98 benchmarks� Both s1 and s100 data sets are used

� LaTTe JVM:� An open-source, state-of-the-art JVM� Highly optimized JIT compiler� Fast mark-and-sweep garbage collector

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Methodology (Cont.)

256KB, 32 byte blocks,4-way set associative,

write through with write-no-allocate

256KB, 32 byte blocks,2-way set associative3

64KB, 32 byte blocks,4-way set associative,



16KB, 32 byte blocks, 4-way set associative,



L1 data cacheL1 instruction cacheConfiguration

� Cache simulator:� Based on Cachesim5 from Sun’s Shade V6 tool suite� A JVM phase aware cache simulator

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Observations for Both Caches

� Class loading is negligible � In terms of cache misses� Holds for both execution modes

� Garbage collection is relatively more active in the JIT mode than the interpreted mode� Larger working set� Reduced total instruction counts and memory

references

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Instruction Cache Performance

� Better instruction cache locality in garbage collection than execution phase

� Better instruction miss rate under JIT mode than under interpreted mode� Execution phase: high method reuse� Garbage collection: low miss rate

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Data Cache Performance

� Higher overall data cache miss rate under JIT mode� Better data locality under interpreted mode

� Data accesses reduce drastically under JIT mode� Bytecodes are read only once for compilation� Stack accesses are optimized into register-register

operations

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Data Cache Reads� Higher read miss rate under JIT mode

� Interpreted mode: 0.94% (mpegaudio) to 5.1% (jess)� JIT mode: 5.75% (mpegaudio) to 19% (db)

� High read miss rate during garbage collection under both execution modes� Up to 19.4% (interpreted) and 18.8% (JIT)� Due to large working set and pointer chasings

� Read misses dominated by execution phase under both execution modes� Execution phase contributes > 90% of read misses

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Data Cache Writes

� High write miss rates in garbage collection� For both execution modes� 50% to 74%

� Garbage collection contributes 44%-78% of write misses� For both execution modes� Exceptions: compress and mpegaudio

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Data Cache Performance with Increased Sizes (Execution Phase)

� Write misses are harder to be removed by larger caches� Most write misses are compulsory misses� Holds for both phases

Data cache read misses (%)

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compress jess db mpegaudio mtrt jack

16K 64K 256K Data cache write misses (%)

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16K 64K 256K

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Data Cache Performance with Increased Sizes (Garbage Collection Phase)

� Larger caches more effective for the execution phase than the garbage collector� Working set of the garbage collector is much larger than 256KB

� Diminishing reduction on data cache missesData cache read misses (%)

0

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16

20


16K 64K 256K Data cache write misses (%)

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16K 64K 256K

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Impact of Larger Data Sets

� JVM phases perform differently as data set increases� Little change: class loading, JIT compilation� Big change: interpretation, garbage collection

� Garbage collection under both execution modes� Performance deteriorates for both data cache reads

and writes� Performances of instruction cache accesses varies

little

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Impact of Larger Data Sets (Cont.)

� Execution phase under interpreted mode:� Cache performance varies little

� Execution phase under JIT mode:� Performance improves for both instruction cache

accesses and data cache writes � Performance of data cache reads deteriorates

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Conclusion

� L1 cache performance of Java programs under� Three phases� Two execution engines� Three cache configurations� Two application data sets

� L1 instruction cache performance determined by the execution phase

� Garbage collection is more significant in the JIT mode than in the interpreted mode

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Conclusion (Cont.)� Higher data cache miss rates of the garbage collector

than the execution engine

� Higher data cache miss rates in the JIT mode than in the interpreted mode

� A larger cache is more effective on eliminating� Read misses than write misses� misses from execution phase than from garbage collector

� Impact of changing data set varies depending on the JVM phase and cache access type

THANK YOU

Appendix

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Instruction Cache Performance

1.144.000.2795.981.31

0.722.860.8997.140.72Jack (int)(jit)

0.7545.520.5154.451.21

0.4631.400.4268.510.47Mtrt (int)(jit)

0.180.480.3199.510.18

0.600.0030.4599.990.60Mpegaudio (int)(jit)

0.112.210.0397.700.12

0.160.620.1099.350.16Db (int)(jit)

1.2614.280.6885.681.48

1.333.511.0396.481.35Jess (int)(jit)

0.071.030.1698.850.07

1.300.0020.1599.461.30Compress (int)(jit)

% Abs. miss%Total miss% Abs. miss%Total miss% Abs. miss

OverallGarbage Collection PhaseExecution PhaseBenchmark

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Decomposition of Data Cache Misses

18.8739.1460.820.007

3.0915.7284.260.004Jack (int)(jit)

21.4771.8128.160.005

4.0968.8631.140.003Mtrt (int)(jit)

11.060.3499.630.005

1.080.0599.940.004Mpegaudio (int)(jit)

19.5213.6586.330.004

4.205.5094.480.002Db (int)(jit)

24.0741.2058.770.004

6.1118.9481.040.003Jess (int)(jit)

3.600.9898.990.003

2.980.1599.840.001Compress(int)(jit)

Overall D-Cache Miss %Garbage Collection %Execution %Class Loading %Benchmark

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Performance of Data Cache Reads

9.299.917.8690.039.48

2.722.098.2497.892.68Jack (int)(jit)

15.8466.7917.0433.1813.83

4.2366.444.3133.554.09Mtrt (int)(jit)

5.750.216.6699.755.75


18.824.0418.7695.9418.83

3.720.0219.3998.063.67Db (int)(jit)

11.1917.6812.7482.3010.92

5.084.7314.7495.264.92Jess (int)(jit)

8.790.3612.9999.628.78

2.030.0516.1499.942.03Compress (int)(jit)



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Performance of Data Cache Writes

48.3556.4258.9943.5639.21

4.1244.9657.7054.902.34Jack (int)(jit)

38.8278.1238.6921.8539.31

3.6677.474.2722.512.44Mtrt (int)(jit)

31.950.4450.0499.5431.91


59.6159.6159.5940.3412.54

5.9213.6360.5986.365.18Db (int)(jit)

66.4854.2663.8845.7269.86

9.5344.1765.3255.815.68Jess (int)(jit)

19.922.2067.3797.7719.61

6.190.2674.5699.736.18Compress (int)(jit)



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Cache Performance with Different Data Sets (JIT Execution)

39.2154.599.4810.011.311.78

2.342.412.682.640.720.7Jack (int)(jit)

39.3155.4113.839.041.211.78

2.443.774.093.180.420.59Mtrt (int)(jit)

31.9147.485.759.010.181.7

1.511.660.942.170.61.02Mpegaudio (int)(jit)

12.5454.7718.838.870.121.79

5.185.173.672.050.160.45Db (int)(jit)

69.8661.210.929.461.481.95

5.688.184.927.751.351.43Jess (int)(jit)

19.6125.898.788.030.070.58

6.180.752.030.241.30.01Compress (int) (jit)

s100s1s100s1s100s1

D-cache WriteD-cache ReadI-cacheMiss Rates (%)

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Cache Performance with Different Data Sets (Garbage Collection)

58.9957.217.866.180.270.29

57.760.358.246.370.890.69Jack (int)(jit)

38.6962.6217.0410.520.510.17

4.2765.684.3113.290.470.29Mtrt (int)(jit)

50.0450.076.666.640.310.31

49.8949.893.884.170.450.51Mpegaudio (int)(jit)

59.5947.9918.765.490.030.28

60.5947.1519.390.960.10.05Db (int)(jit)

63.8850.9912.744.920.680.38

65.3259.814.744.041.030.73Jess (int)(jit)

67.3751.4912.996.730.160.25

74.5652.1716.143.30.150.04Compress (int) (jit)

s100s1s100s1s100s1

D-cache WriteD-cache ReadI-cacheMiss Rates (%)

cache performance in java virtual machines: a … performance in java virtual machines: a study of...

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