The document outlines Amber Hanna's project to build a Raspberry Pi computer cluster. It describes the goals of improving data analysis, the hardware used including 9 Raspberry Pis, and the software installed including Raspbian OS and MPI. Testing involved using the cluster to compute integrals and averages of increasing size, showing it was faster than a single Pi for large problems but had limitations like requiring the number of computations to be divisible by the number of Pis. The project provides an overview of building a Raspberry Pi cluster from start to testing.

1. BUILDING A RASPBERRY PI CLUSTER
Amber Hanna
Advisor: Dr. Gary Hughes

2. TODAY’S OUTLINE
• Project Goals
• What is a Raspberry Pi?
• Part I: Hardware
• Part II: Software
• Part III: Internet Connection
• Part IV: Integrating the Pi’s
• Part V: Testing the Cluster
• Limitations
• Future Steps

3. PROJECT GOALS
• Build a functional computer cluster from scratch
• Improve data analysis for the statistics department
• Learn a new subject

4. WHAT IS A RASPBERRY PI?
• Credit-card sized computer
• Multi-functional
• Ports
• Quad USB
• Ethernet
• Audio
• HDMI
• Micro SD
• Micro USB
• 1 GB RAM
Provided by Google Images

5. PART I: HARDWARE
Materials Needed
• 9 Raspberry Pi’s (Model 2B)
• 9 Micro SD cards
• 2 Ethernet switches
• Display
• Mouse
• Keyboard
• 5 Charger Ports
• 9 Charging micro USB cords
• Outlet Strip

7. PART II: SOFTWARE
• Blank canvas
• Install NOOBS
• Raspbian Operating System
• Linux based
• Command Line
• Graphical User Interface
• Python pre-installed

8. PART III: INTERNET CONNECTION
• Connecting to Cal Poly’s network
• Secure Mustang Wireless
• Not as easy as we thought!
• Thank you, Tom Randall
• 129.65.215.xxx IP addresses
• Ranging from .130 - .138

9. PART IV: INTEGRATING THE PI’S
• Download Message Passing Interface (MPI)
• Download Mpi4py interpreter
• Used Python
• Cluster-specific syntax
• Read and write the image to all SD cards
• Insert into all Pi’s

10. PART V: TESTING THE CLUSTER
The cluster is built! Now what!?
Provided by Google Images

11. TESTING THE CLUSTER I: INTEGRALS
Provided by Google Images

12. TESTING THE CLUSTER I: INTEGRALS
MASTER NODE
Number of
Trapezoids (n)
Real User System
10,000 0.894s
0.886s
0.830s
0.760s
0.070s
0.120s
100,000 1.576s
1.586s
1.410s
1.430s
0.160s
0.150s
300,000 3.135s
3.101s
2.990s
2.960s
0.140s
0.130s
400,000 3.791s
3.895s
3.710s
3.800s
0.070s
0.090s
500,000 4.545s
4.626s
4.490s
4.470s
0.050s
0.110s
1,000,000 8.667s
8.254s
8.400s
8.170s
0.110s
0.090s
10,000,000 1m17.052s
1m17.719s
1m16.060s
1m18.090s
0.650s
0.610s
50,000,000 6m22.925s 6m20.05s 2.780s
Segmentation Fault
DISTRIBUTED PROCESSING
Number of
Trapezoids (n)
Real User System
10,000 2.848s
2.733s
9.350s
8.970s
0.960s
1.170s
100,000 3.031s
3.063s
10.130s
10.170s
1.200s
0.990s
300,000 3.330s
3.498s
11.560s
11.910s
1.230s
1.200s
400,000 3.522s
3.995s
12.330s
14.120s
0.990s
1.030s
500,000 3.832s
4.067s
13.460s
13.480s
1.040s
1.270s
1,000,000 4.931s
5.135s
17.620s
18.460s
1.220s
1.260s
10,000,000 25.211s
23.888s
1m36.780s
1m32.540s
2.070s
1.760s
50,000,000 1m45.877s
1m56.439s
6m54.180s
6m45.030s
5.420s
7.100s
100,000,000 Memory Crashed

13. TESTING THE CLUSTER I: INTEGRALS
Real Time for Computing Integrals Explained by the Number of Trapezoids (n)

14. TESTING THE CLUSTER I: INTEGRALS
Real Time for Computing Integrals Explained by the Number of Trapezoids (n) – Zoom View

15. TESTING THE CLUSTER II: AVERAGES

16. TESTING THE CLUSTER II: AVERAGES
MASTER NODE
Number Count
(n)
Real User System
90 0.808s
0.810s
0.710s
0.660s
0.090s
0.140s
900 0.831s
0.838s
0.720s
0.760s
0.110s
0.070s
9,000 0.899s
0.921s
0.800s
0.780s
0.090s
0.130s
90,000 1.683s
1.743s
1.570s
1.560s
0.110s
0.140s
900,000 9.281s
9.345s
9.000s
9.000s
0.270s
0.350s
9,000,000 1m26.276s
1m25.132s
1m24.250s
1m26.190s
2.020s
1.940s
DISTRIBUTED PROCESSING
Number Count (n) Real User System
90 2.900s
2.890s
9.920s
10.160s
1.130s
1.070s
900 2.719s
2.805s
9.300s
9.540s
1.070s
1.210s
9,000 3.550s
3.062s
12.570s
10.770s
1.090s
0.990s
90,000 5.950s
5.408s
21.680s
20.020s
1.600s
1.060s
900,000 30.195s
29.361s
1m55.930s
1m53.040s
3.690s
3.430s
9,000,000 Crashed

17. TESTING THE CLUSTER II: AVERAGES
Real Time for Computing the Average Explained by the Number Count – Zoom View

18. LIMITATIONS
• General Problems:
• Must need to know Linux commands
• Must need to know python (and well!)
• Clustering Problems:
• N must be divisible by # of clusters
• Accuracy will be compromised
• Creates an in-balance
• Can be fixed with tweaking
• Inefficient Results

19. FUTURE STEPS
You can help!

20. THANK YOU ALL!