1. Parallel Extensions
Chester Hartin
Code Review 11/30/12

2. History of parallelization
 Definition: a form of computation in which many
calculations are carried out simultaneously,
operating on the principle that large problems
can be divided into smaller ones, which are then
solved concurrently (“in parallel”)
 Developers have tried to improve performance
by parallelizing problems, even before true
multicore systems
 How is this different from multithreading?
Multithreading is a type of parallelism

3. Real-life parallelization
 Consider that we have some eggs to boil (data
to process)
 Before the early 2000s we only had 1 pot (core)
and more eggs than we could boil at once
(meaning we could boil >1 at a time in
parallel, since you can fit >1 egg/pot). After the
early 2000s we had 2 pots, thus could boil twice
as many eggs at once.

4. Pots vs Boil time
Given: We have 10 eggs to boil and each egg requires 8
minutes in order to be ready to eat. Each pot holds up to
5 eggs.
Number of pots Boil Time
1 16 min
2 8 min
3 8 min
4 8 min
Something interesting occurs… Adding more than 2 pots
does nothing to decrease the overall time.

5. Time efficiency
Time to boil 100 eggs

6. What does this mean?
 It really doesn’t matter how many cores we use.
This problem simply will not speed up by adding
cores.
 Our equations are pretty simple:
 Pots Needed = Number of Eggs/Eggs Per Pot
 20 = 100/5
 Time = Ceiling(Pots Needed/Pots) x Boiling Time
 160 = Celing(20/1) x 8
 In Computer Terms:
 ExecutionTime = Ceiling(Amount of
threads/Cores)xThreadExecutionTime

7. Caveat
 In the egg example we assume…
 Thread execution time is constant (never happens)
 Presume each core executes one thread at a time
and does not continue w/ the next thread in the
queue until it’s finished – ie given a quad core
processor, it can execute 4 threads and give us the
same result as the egg boiling w/ 4 pots

8. Short attention span LINQ
 Warning: I only really know basic LINQ (slowly
integrating it into the Real Feeds Project where I
can use it)
 LINQ = Language Integrated Query
 Something something query – gotcha. Looks like
SQL in reverse (we know SQL, right?)
 Layman's terms – LINQ works against collections
of data (any data really that has an enumerator)
to get a all or subset of data

9. Simple LINQ
var ages = new List<int>(){ 25, 21, 18, 65};
var agesInOrder = from age in ages
orderby age ascending
select age;

10. Parallelization and LINQ
 Let’s vary our boil time
Egg Batch Boil Time (min)
1 1 8
2 1 4
3 1 8
4 1 8
5 1 4
6 2 4
7 2 4
8 2 8
9 2 6

11. Ex 2
 Take a collection of egg boil times
 Iterate over the collection and look at 5 items at
a time
 Find the longest cooking time for the egg in the
current patch
 Simulate the boiling time with Thread.Sleep
 Stop looking for eggs when there are <5 eggs in
the current batch

12. Ex 2 (cont)
 First run – ~1600ms
 2nd run - ~1200ms why?
 Put 5 eggs in the pot
 After 4 min, remove 2nd from last egg
 After 8 min, remove remaining egg
 Add next batch that contains 1 egg
 After 4min remove the egg from the pot

13. LINQ to the rescue
 Any for / foreach look can potentially be
converted into LINQ
 Compare Boil() code v1 & v2
 Note :
 Optimized (1600ms vs 1200ms)
 Nicer to read

14. Parallelize It!
 We have 2 options
 Parallel extensions
 Parallel LINQ

15. Parallel Extensions
 Introduced in .Net 4.0
 Has 2 important methods that we’ll focus on
 Parallel.For
 Parallel.For(0, eggs.Length, I => {});
 Parallel.ForEach

16. Parallel LINQ
 Say we have a list of web requests that we need
to do
 Each call takes a certain amount of time & we
want to parallelize it
 In previous examples we’ve relied on an index,
but say if we can’t

17. Example 3