(Check my blog @ http://www.marioalmeida.eu/ ) In this presentation I present the performance metrics and results of running the parsec benchmark with the raytrace application on Upc's boada server

1. Instrumenting a
benchmark
application
Tools and Measurements Techniques
Project by Mário Almeida (EMDC)
Barcelona, 25 April 2012

2. Index (1/2)
Tools and configuration
● Parsec
○ Overview
○ Benchmark programs
● Extrae
● Paraver
● Configuration
1

3. Index (2/2)
Measurements
● Raytrace
○ Overview
○ Code
○ Inputs
○ Traces
○ Load Balancing
○ Cache misses and instructions
○ Execution time
○ Configuration comparisons
○ Extrae overhead
Conclusions 2

4. Tools and configuration

5. Parsec
Overview
● Benchmark with the following characteristics:
○ Multithreaded
○ Emerging workloads
○ Diverse
○ Not HPC-focused
○ Research
3

6. Parsec
Benchmark programs
● blackscholes
● bodytrack
● canneal
● dedup
● facesim
● ferret
● fluidanimate
● freqmine
● raytrace
● ... 4

7. Extrae
● Instrumentation package to trace programs
and run with shared memory model and
message passing programming.
5

8. Paraver
● Detailed quantitative analysis of a program
performance.
● Concurrent comparative analysis of several
traces.
● Support for mixed message passing and
shared memory.
● Building of derived metrics.
6

9. Configuration (1/4)
Boada server:
● Dual CPU Six Core with Hyperthreading.
● Kills applications after a few minutes.
● 24 GB of RAM.
Boada server:
● Used cpulimit to limit the cpu usage up to four cores.
7

10. Configuration (2/4)
Installed and/or configured:
● Parsec 2.1 with raytrace package only.
● Extrae 2.2.1.
● Paraver 4.3.0 (in my laptop).
● CpuLimit
● Minor configurations on .bashrc.
● Multiple scripts to clean, build and run.
8

11. Configuration (3/4)
9

12. Configuration (4/4)
10

13. Measurements

14. Raytrace
Overview
● Physical simulation for visualization
● Computer animation
● Input is a complex object of many triangles.
11

15. Raytrace
Code
For every pixel in the image
calculate trajectory of ray striking pixel
find closest intersection point of ray with scene
geometry
calculate contribution of all lights at intersection point
recursively trace specularly reflected ray
end for
12

16. Raytrace
Inputs
● simsmall - 1 million polygons (480x270)
● simmedium - 1 million poly (960x540)
● simlarge - 1 million poly (1920x1080)
● native - 10 million poly (1920x1080)
13

17. Raytrace
Trace (1/2)
Only 10% of the execution time is parallel!
Not created Running
14

18. Raytrace
Trace (2/2)
Render time is proportional to the # of frames!
Init and adding object Build Context Render
15

19. Raytrace
Load balancing (1/2)
Not created Create Threads Task
Barrier Wait for all threads 16

20. Raytrace
Load balancing (2/2)
Good load balancing between the slave
threads.
17

21. Raytrace
Cache and instructions
High number of cache misses Very low number of cache misses
There were no significative
diferences of IPC between
threads.
18

22. Raytrace
Execution time (1/3)
These are average times from
multiple executions of the parallel
code only and without extrae
overhead.
There was a high average
deviation of 0.3 seconds in the
experiments.
Bigger inputs were more accurate.
19

23. Raytrace
Execution time (2/3)
There was a smaller average
deviation of 0.03 seconds.
With 64 threads it runs almost
three times faster!
20

24. Raytrace
Execution time (3/3)
There was a even smaller average
deviation of 0.02 seconds.
With 64 threads it runs almost
three times faster!
21

25. Raytrace
Configuration comparison
In the case of the limited
configuration, although
perfomance doesn't seem
to degrade, the execution
time seems to stabilize for
more than 8 threads.
22

26. Raytrace
Extrae overhead
23

27. Conclusions

28. Conclusions (1/3)
● The system seemed to perform worse for a
number of threads multiple of the total
number of physical cores.
● The program has a good load balancing.
● Fine-granular parallelism.
24

29. Conclusions (2/3)
● Although it wasn't possible to verify,
increasing the input should cause higher
cache misses, because of the big working
sets that won't fit on the memory.
● Memory bandwidth should be the main issue
for good speedups.
● Boada killed almost all the native input
executions. 25

30. Conclusions (3/3)
● Paraver simplifies the process of analyzing
an application performance.
● Better knowledge of the systems
architecture would be needed in order
further analyse the performance of the
application.
26

31. Questions