1 wake up and smell the coffee: performance analysis methodologies for the 21st century kathryn s...

1

Wake Up and Smell the Coffee: Performance Analysis

Methodologies for the 21st Century

Kathryn S McKinleyDepartment of Computer Sciences

University of Texas at Austin

2

Shocking News!

In 2000, Java overtook C and C++ as the most popular programming

language [TIOBE 2000--2008]

3

Systems Researchin Industry and

Academia

ISCA 200620 papers use C and/or C++5 papers are orthogonal to the programming

language2 papers use specialized programming languages2 papers use Java and C from SPEC1 paper uses only Java from SPEC

4

What is Experimental Computer Science?

5

What is Experimental Computer Science?

• An idea

• An implementation in some system

• An evaluation

6

The success of most systems innovationhinges on evaluation methodologies.

1. Benchmarks reflect current and ideally, future reality

2. Experimental design is appropriate

3. Statistical data analysis

7

The success of most systems innovationhinges on experimental methodologies.

1. Benchmarks reflect current and ideally, future reality [DaCapo Benchmarks 2006]

2. Experimental design is appropriate.

3. Statistical Data Analysis [Georges et al.

2006]

8

• We’re not in Kansas anymore!– JIT compilation, GC, dynamic checks,

etc• Methodology has not adapted

– Needs to be updated and institutionalized

QuickTime™ and aTIFF (Uncompressed) decompressor

are needed to see this picture.

“…this sophistication provides a significant challenge tounderstanding complete system performance, not found intraditional languages such as C or C++” [Hauswirth et al OOPSLA ’04]

Experimental Design

9

Experimental Design

• Comprehensive comparison– 3 state-of-the-art JVMs– Best of 5 executions– 19 benchmarks– Platform: 2GHz Pentium-M, 1GB RAM, linux 2.6.15

10

2.3941.2481.246 1.158

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

antlr bloat chart eclipsefop

hsqldb jython luindexlusearchpmd

sunflowxalan

geomean

Relative PerformanceSun JDK 16

IBM J9

BEA JRockit 16

Experimental Design

11

2.3941.2481.246 1.158

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1



sunflowxalan

geomean


IBM J9

BEA JRockit 16

Experimental Design

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1



sunflowxalan

geomean


IBM J9

BEA JRockit 16

12

2.3941.2481.246 1.158

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1



sunflowxalan

geomean


IBM J9

BEA JRockit 16

Experimental Design

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1



sunflowxalan

geomean


IBM J9

BEA JRockit 16

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1



sunflowxalan

geomean


IBM J9

BEA JRockit 16

13

2.3941.2481.246 1.158

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1



sunflowxalan

geomean


IBM J9

BEA JRockit 16

Experimental Design

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1



sunflowxalan

geomean


IBM J9

BEA JRockit 16

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1



sunflowxalan

geomean


IBM J9

BEA JRockit 16

First Iteration

Second Iteration

Third Iteration

14

Experimental Design

Another Experiment• Compare two garbage collectors

– Semispace Full Heap Garbage Collector– Marksweep Full Heap Garbage Collector

15

Experimental Design

Another Experiment• Compare two garbage collectors

– Semispace Full Heap Garbage Collector– Marksweep Full Heap Garbage Collector

• Experimental Design– Same JVM, same compiler settings– Second iteration for both– Best of 5 executions– One benchmark - SPEC 209_db– Platform: 2GHz Pentium-M, 1GB RAM, linux 2.6.15

16

-12050.1

10428.389816.1

9688.019624.979599.989516.329490.579523.979412.749387.97

9443.59337.78

SPEC _209_db Performance

1.1

1.15

1.2

1.25

1.3

1.35

Marksweep Semispace

Normalized Time

Marksweep vs Semispace

17


0.95

1

1.05

1.1

1.15

1.2

Marksweep Semispace

Normalized Time


18


1

1.05

1.1

1.15

1.2

1.25

1.3

20 40 60 80 100 120

Heap Size (MB)

Normalized Time


0.95

1

1.05

1.1

1.15

1.2

Marksweep Semispace

Normalized Time

Semispace Marksweep


-12050.1

10428.389816.1

9688.019624.979599.989516.329490.579523.979412.749387.97

9443.59337.78


1.1

1.15

1.2

1.25

1.3

1.35

Marksweep Semispace

Normalized Time

19

Experimental Design

20

Experimental Design:Best Practices

• Measuring JVM innovations

• Measuring JIT innovations

• Measuring GC innovations

• Measuring Architecture innovations

21

JVM InnovationBest Practices

• Examples:– Thread scheduling– Performance monitoring

• Workload triggers differences– real workloads & perhaps microbenchmarks– e.g., force frequency of thread switching

• Measure & report multiple iterations – start up– steady state (aka server mode)– never configure the VM to use completely

unoptimized code!

• Use a modest or multiple heap sizes computed as a function of maximum live size of the application

• Use & report multiple architectures

22

0.50

1.00

1.50

2.00

2.50

3.00

3.50

antlr bloat chart eclipse fop hsqldb jython lusearch luindex pmd xalan min max geomean

Performance relative to best

1st JVM A 2nd JVM A 3rd JVM A

1st JVM B 2nd JVM B 3rd JVM B

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

4.50





0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

4.50

5.00

5.50





Best Practices

Pentium M

AMD Athlon

SPARC

23

JIT Innovation Best Practices

Example: new compiler optimization– Code quality: Does it improve the application

code?– Compile time: How much compile time does it

add?– Total time: compiler and application time

together– Problem: adaptive compilation responds to

compilation load– Question: How do we tease all these effects

apart?

24

JIT Innovation Best Practices

Teasing apart compile time and code quality requires multiple experiments• Total time: Mix methodology

– Run adaptive system as intended• Result: mixture of optimized and unoptimized code

– First & second iterations (that include compile time)– Set and/or report the heap size as a function of maximum live

size of the application– Report: average and show statistical error

• Code quality– OK: Run iterations until performance stabilizes on “best”, or– Better: Run several iterations of the benchmark, turn off the

compiler, and measure a run guaranteed to have no compilation

– Best: Replay mix compilation• Compile time

– Requires the compiler to be deterministic– Replay mix compilation

25

Replay CompilationForce the JIT to produce a deterministic result • Make a compilation profiler & replayerProfiler

– Profile first or later iterations with adaptive JIT, pick best or average

– Record profiling information used in compilation decisions, e.g., dynamic profiles of edges, paths, &/or dynamic call graph

– Record compilation decisions, e.g., compile method bar at level two, inline method foo into bar

– Mix of optimized and unoptimized, or all optimized/unoptimized

Replayer– Reads in profile– As the system loads each class, apply profile +/- innovation

• Result– controlled experiments with deterministic compiler behavior– reduces statistical variance in measurements

• Still not a perfect methodology for inlining

26

GC Innovation Best Practices

• Requires more than one experiment...• Use & report a range of fixed heap sizes

– Explore the space time tradeoff– Measure heap size with respect to the maximum live size of the

application– VMs should report total memory not just application memory

• Different GC algorithms vary in the meta-data they require• JIT and VM use memory...

• Measure time with a constant workload– Do not measure through put

• Best: run two experiments– mix with adaptive methodology: what users are likely to see in

practice– replay: hold the compiler activity constant

• Choose a profile with “best” application performance in order to keep from hiding mutator overheads in bad code.

27

Architecture Innovation Best Practices

• Requires more than one experiment...• Use more than one VM• Set a modest heap size and/or report heap size as a function

of maximum live size• Use a mixture of optimized and uncompiled code• Simulator needs the “same” code in many cases to perform

comparisons• Best for microarchitecture only changes:

– Multiple traces from live system with adaptive methodology• start up and steady state with compiler turned off• what users are likely to see in practice

• Wont work if architecture change requires recompilation, e.g., new sampling mechanism– Use replay to make the code as similar as possible

28

statistics

Disraeli

benchmarks

There are lies, damn lies, and“sometimes more than twice as fast”

“our …. is better or almost as good as …. across the board”

“garbage collection degrades performance by 70%”

“speedups of 1.2x to 6.4x on a variety of benchmarks”

“our prototype has usable performance”

“the overhead …. is on average negligible”

Quotes from recent research papers

“…demonstrating high efficiency and scalability”

“our algorithm is highly efficient”

“can reduce garbage collection time by 50% to 75%”

“speedups…. are very significant (up to 54-fold)”

“speed up by 10-25% in many cases…”“…about 2x in two cases…”

“…more than 10x in two small benchmarks”

“…improves throughput by up to 41x”

29

Conclusions

• Methodology includes– Benchmarks– Experimental design– Statistical analysis [OOPSLA 2007]

• Poor Methodology– can focus or misdirect innovation and energy

• We have a unique opportunity– Transactional memory, multicore performance, dynamic

languages, • What we can do

– Enlist VM builders to include replay– Fund and broaden participation in benchmarking

• Research and industrial partnerships• Funding through NSF, ACM, SPEC, industry or ??

– Participate in building community workloads

30

Thank you!

www.dacapobench.org

1 wake up and smell the coffee: performance analysis methodologies for the 21st century kathryn s...

Documents

experimental design

iteration slide

spec slide

evaluation slide

austin slide

inlining slide

heap garbage collector

compilation best