2. Course Details
Delivery
◦ Lectures/discussions: English
◦ Assessments: English
◦ Ask questions in class if you don’t understand
◦ Email me after class if you do not want to ask in
class
◦ DO NOT LEAVE QUESTIONS TILL THE DAY BEFORE THE
EXAM!!!
Assessments (this may change)
◦ Homework (~1 per week): 10%
◦ Midterm: 20%
◦ 1 project + final exam OR 2 projects: 35%+35%
3. Course Details
Textbook
◦ Principles of Parallel Programming, Lin & Snyder
Other sources of information:
◦ COMP 322, Rice University
◦ CS 194, UC Berkeley
◦ Cilk lectures, MIT
Many sources of information on the
internet for writing parallelized code
4. Teaching Materials & Assignments
Everything is on Jusur
◦ Lectures
◦ Homeworks
Submit homework through Jusur
Homework is given out on Saturday
Homework due following Saturday
You lose 10% for each day late
No homework this week!
5. Outline
This lecture:
◦ Why study parallel computing?
◦ Topics covered on this course
Next lecture:
◦ Discuss an example problem
6. Why study parallel computing?
First, WHAT is parallel computing?
◦ Using multiple processors (in parallel) to solve a
problem faster than a single processor
Why is this important?
◦ Science/research is usually has two parts.
Theory, and experimentation.
◦ Some experiments just take too long on a single
processor (days, months, or even years)
◦ We do not want to wait for so long
◦ Need to execute experiments faster
7. Why study parallel computing
BUT, parallel computing very
specialized
◦ Few computers in the world with many procs.
◦ Most software not (very) parallelized
◦ Typically parallel programming is hard
◦ Result: parallel computing taught at Masters
level
Why study it during undergraduate?
◦ The entire computing industry has shifted to
parallel computing. Intel, AMD, IBM, Sun, …
8. Why study parallel computing?
Today:
◦ All computers are multi-core, even laptops
◦ Mobile phones will also be multi-core
◦ Number of cores keeps going up
◦ Intel/AMD:
~2004: 2 cores per processor
~2006: 4 cores per processor
~2009: 6 cores per processor
If you want your software to use all
those cores, you need to parallelize it.
BUT, why did this happen?
9. Why did this happen?
We need to look at history of
processor architectures
All processors made of transistors
◦ Moore’s Law: number of transistors per chip
doubles every 18-24 months
◦ Fabrication process (manufacture of chips)
improvements made transistors smaller
◦ Allows more transistors to be placed in the
same space (transistor density increasing).
11. Why did this happen?
What did engineers do with so many
transistors?
◦ Added advanced hardware that made your code
faster automatically
MMX, SSE, superscalar, out-of-order execution
Smaller transistors change state faster
◦ Smaller transistors enables higher speeds
Old view:
◦ “Want more performance? Get new processor.”
◦ New processor more advanced, and higher speed.
◦ Makes your software run faster.
◦ No effort from programmer for this extra speed.
Don’t have to change the software.
12. Why did this happen?
But now, there are problems
◦ Engineers have run out of ideas for advanced
hardware.
◦ Cannot use extra transistors to automatically
improve performance of code
OK, but we can still increase the
speed, right?
13. Why did this happen?
But now, there are problems
◦ Engineers have run out of ideas for advanced
hardware.
◦ Cannot use extra transistors to automatically
improve performance of code
OK, but we can still increase the
speed, right? WRONG!
14. Why did this happen?
But now, there are problems
◦ Higher speed processors consume more power
Big problem for large servers: need their own
power plant
◦ Higher speed processors generate more heat
Dissipating (removing) the heat is requiring
more and more sophisticated equipment, heat
sinks cannot do it anymore
◦ Result: not possible to keep increasing speed
Let’s look at some heat sinks
15. Intel 386 (25 MHz) Heatsink
The 386 had no heatsink!
It did not generate much heat
Because it has very slow speed
20. Why study parallel computing?
Old view:
◦ “Want more performance? Get new processor.”
◦ New processor will have higher speed, more
advanced. Makes your software run faster.
◦ No effort from programmer for this extra speed.
New view:
◦ Processors will not be more advanced
◦ Processors will not have higher speed
◦ Industry/academia: Use extra transistors for
multiple processors (cores) on the same chip
◦ This is called a multi-core processor
E.g., Core 2 Duo, Core 2 Quad, Athlon X2, X4
21. Quotes
◦ “We are dedicating all of our future product
development to multicore designs. … This is a
sea change in computing”
Paul Otellini, President, Intel (2005)
◦ Number of cores will ~double every 2 years
22. Why study parallel computing?
What are the benefits of multi-core?
◦ Continue to increase theoretical performance:
Quad-core processor, with each core at 2GHz
is like 4x2GHz = 8GHz processor
◦ Decrease speed to reduce temperature, power
16-core at 0.5GHz = 16*0.5 = 8GHz
8GHz, but at lower temperature, lower power
Multi-core is attractive, because it
removes existing problems
No limit (yet) to number of cores
23. Affects on Programming
Before:
◦ Write sequential (non-parallel) program.
◦ It becomes faster with newer processor
Higher speed, more advanced
Now:
◦ New processor has more cores, but each is slower
◦ Sequential programs will run slower on new proc
They can only use one core
◦ What will run faster?
Parallel program that can use all the cores!!!
24. Why study parallel computing?
You need knowledge of parallelism
◦ Future processors will have many cores
◦ Each core will become slower (speed)
◦ Your software will only achieve high
performance if it is parallelized
Parallel programming is not easy
◦ Many factors affect performance
◦ Not easy to find source of bad performance
◦ Usually requires deeper understanding of
processor architectures
◦ This is why there is a whole course for it
25. Course Topics
Foundations of parallel algorithms
◦ How do we make a parallel algorithm?
◦ How do we measure its performance?
Foundations of parallel programming
◦ Parallel processor architectures
◦ Threads/tasks, synchronization, performance
◦ What are the trade-offs, and overheads?
Experiment with real hardware
◦ 8-way distributed supercomputer
◦ 24-core shared memory supercomputer
If we have time:
◦ GPGPUs / CUDA
26. Skills You Need
Basic understanding of processor
architectures
◦ Pipelines, registers, caches, memory
Programming in C and/or Java
27. Summary
Processor technology cannot continue
as before. Changed to multi-cores.
Multi-cores require programs to be
parallelized for high performance
This course will cover core theory
and practice of parallel computing
