1. Parallel Programming Fall 2013 Tuesday/Thursday 11:00 am - 12:15 pm Instructors
Dr. Barry Wilkinson
University of North Carolina Charlotte
Dr. Clayton Ferner
University of North Carolina Wilmington
© B. Wilkinson/Clayton Ferner. Modification date: August 21, 2013
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2. Outline
These slides constitute the course outline and include the course syllabus and course policies.

3. Parallel Computing
The use of multiple processors or computers to solve problems at a greater computational speed than using a single computer/processor.
Basic idea is simple – using N computers/ processors collectively on a problem should lead to a faster solution.
Can tackle problems that could not be solved in a reasonable time otherwise.
Also solve problems with higher precision and/or with more memory requirements.

4. Computer platforms for parallel computing
Multiple interconnected computers
- Cluster Computing, group of interconnected computers typically using Ethernet switch and physically all in one room.
2. A single computer system with multiple internal processors or cores
- Usually shares a common main memory
Computer system with attached graphic processing unit (GPU)
- GPUs have large number of execution cores, now used for high performance computing as well as original graphics application
Combination of above most likely.

5. UNCC cluster Has all three types of platform.
cci-grid01.uncc.edu – ccci-grid9.uncc.edu
Login
cci-grid06
cci-grid07
cci-grid08
cci-grid01
cci-grid02
cci-grid03
cci-grid04
cci-grid09
C2050 GPU
(448 core)
C2050 GPU
(448 core)
K20 GPU
(2496 core)
coit-grid09:
Dual AMD Opteron GHz 16-core each, with 16 GB main memory
RAID1 disks,
redundant power supplies, VM, web server, user gateway login, SSH, NAT, DHCP, DNS, and LDAP, etc.
coit-grid01-4: Each two Xeon 3.4 Ghz processors, 8GB main memory
coit-grid06:
GPU server, with NVIDIA 2050 GPU
coit-grid08:
GPU server, E GHz 6-core Xeon processor with NVIDIA K20 GPU, 32 GB main memory
coit-grid07:
GPU server, X GHz quad-core Xeon processor with NVIDIA 2050 GPU, 12GB main memory
switch
coit-grid05: Four quad-core 2.93Ghz Xeon processors 64GB main memory
1.2 TB disk (RAID 5)
cci-grid05
All user’s home directories on cci-grid05 (NFS)
Has all three types of platform.

6. Course Prerequisites
UNC-C Catalog prerequisites: ITCS 2214 (Data Structures) and ITCS 3181/3182 (Computer Architecture).
ITCS 3181/2 not strictly necessary so long as you know what a computer consists of (processors, memory and I/O) and how it operates.
What is needed -- basic skills in C and Java.

7. Course Text
Parallel Programming: Techniques and Application Using Networked Workstations and Parallel Computers, 2nd edition, by B. Wilkinson and M. Allen, Prentice Hall Inc., 2005, ISBN
On-line notes are provided that are derived from the course text – with some updated materials from 2005

8. Home Page http://cs.uncc.edu/~abw/ITCS4145F13/ Password: rp19zb39
For course notes, assignments, announcements, etc.:
Password protected:
Username: pablo
Password: rp19zb39

9. Course Contents
Parallel computers: architectural types, shared memory, message passing, interconnection networks, potential for increased speed.
Parallel patterns for structured parallel programming, motives, advantages, introduction to various patterns, tools.
Seeds pattern programming framework to create distributed computations. (Assignment 1)
Using compiler directives for pattern programming, Paraguin compiler. (Assignment 2)

10. Course Contents (Continued)
Message passing: MPI message passing APIs, send, receive, collective operations. Running MPI programs on a cluster, implement patterns with MPI. (Assignment 3)
More computation patterns and their application, divide and conquer, pipeline, iterative synchronous all-to-all, stencil.

11. Course Contents (Continued)
Programming with shared memory programming: Specifying parallelism, sharing data, critical sections, threads, OpenMP. Running threaded/ OpenMP programs on multi-core system, hybrid MPI/OpenMP programs. (Assignment 4)
CPU-GPU systems: data parallel pattern, GPU architecture, programming in CUDA, issues for achieving high performance. (Assignment 5)

12. Course Contents (Continued)
Algorithms and applications: Selection from:
Sorting algorithms
Searching algorithms
Numerical algorithms
Image processing algorithms

13. Course Grade Sites with +/- grading Sites without +/- grading
Undergraduate Grade Scale A A B B B C C C D D D F
Graduate
Grade Scale A A B B B C C C U
Undergraduate
Grade Scale A B C D F
Graduate
Grade Scale A B C U

14. Computation of Grades Class quizzes (2) 25% Assignments (5) 50%
Final exam 25% The assessment and percentages may be modified.
Read small print

15. Graduate students
There will be additional work in the assignments for graduate students, which will be extra credit for undergraduates.

16. Student Learning Outcomes (SLOs)
The learning outcomes for the UNC-W undergraduate parallel computing course are:
Students develop solutions and implement the solutions for scientific problems using multiple processors in a cluster.
Students acquire knowledge in writing programs which require processes to synchronize and exchange data using Message Passing Interface (API).
Students acquire knowledge in writing programs which require processes to spawn multiple treads that share memory on multiple core processors.
Students learn to profile parallel programs by placing event probes and to log them for analysis and tuning of the program performance.
The Estimate Curriculum Category Content (Semester hours) is:
Area
Core
Advanced
Algorithms
1.0
Software design
0.5
Data structures
0.25
Concepts of programming languages
Computer organization and architecture

17. Code of Student Academic Integrity
All submitted assignments must be your own work. Copied work or work done by more than one person (unless specifically instructed) will not be accepted - at the very minimum, zero credit - and may be subject to disciplinary action.
The Code of Student Academic Integrity applies to both assignments and quizzes/tests.

18. Due dates for assignments
Will be posted on home page with assignment.
Assignments can be turned in late but with a penalty.
Late Penalty
< 24 hours 10%
> 24 hours and < 48 hours 20%
> 48 hours and < 72 hours 30%
> 72 hours 100%

19. Attendance
Attendance is expected. Attendance will be recorded. If you miss classes, it can have a deleterious effect on your grade:
After 4 missed classes without good reason:
2 marks off overall accumulated marks (out of 100) for each additional class missed.
If you must miss class, see the instructors beforehand to avoid losing marks.

20. Mid-Term Unsatisfactory Grades
UNC-Charlotte requires midterm unsatisfactory grades to be submitted by the end of the 8th week of class.
Mid term grades
Undergraduates: Satisfactory or D or F
Graduates: Satisfactory or C or U
will be based upon all work graded by week 7, which
probably will be two assignments and one class test.

21. Ask questions during class and participate in class discussion.
Expectations
Read all materials provided.
Read slides before class. Take additional notes during class
Ask questions during class and participate in class discussion.
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22. NCREN facility (North Carolina Research and Education Network)‏
Each site provides microphones for individuals to speak.
Please feel fee to speak at suitable times but remember NCREN needs recognize site and speaker, so first announce your institution and name.
Most microphones are turned on all the time. Any sound will be heard across all sites, so avoid unnecessary sounds.
Fans from a laptop can be especially disturbing, so move laptop away from a microphone. Turn cell phones off.
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23. NCREN Video Class Recording
The lectures are available on-line.
Details to be provided.
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24. Instructors details
UNC-Charlotte Barry Wilkinson Department of Computer Science University of North Carolina, Charlotte Home page: Office Hours Tuesday/Thursday: 2:00 pm to 3:30 pm 
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25. Instructors details
UNC-Wilmington Clayton Ferner Department of Computer Science University of North Carolina Wilmington Home page: Office Hours Tuesday/Thursday 12:30 pm to 2:00 pm
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26. Questions