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CAS operations and scalability...


Hello,


When the CAS operation "goes on the bus", use of CAS can impair scalability.
but CAS can be accomplished locally -- that is, with no bus transactions --
and then it can scale.

If we then change the CAS operation that goes on the bus to a normal store
you'll also see a similar slow-down in terms of coherency bus traffic, CAS
isn't appreciably different than a normal store. Also the lock: prefix
caused
the LOCK# signal to be asserted, acquiring exclusive access to the bus.
This doesn't scale of course

As you have noticed i  have wrote parallelhashlist (a parallel hashtable),
you can find parallelhashlist here:

http://pages.videotron.com/aminer/

It's a parallel Hashtable with O(1) best case and O(log(n)) worst case
access that uses lock striping and lightweight MREWs(multiple-readers
-exclusive-writer) , this allows multiple threads to write and read
concurently. also parallelhashlist maintains an independant counter , that
counts the number of entries , for each segment of the hashtable and uses
a lock for each counter, this is also for better scalability. and
parallelhashlist
is scaling very well,  but since it is a parallel hashtable so the
possibility of
contention is low so why doi need the distributed reader-writer lock of
Dmitry Vyukov inside my parallel hashlist ?

Other than that I have done some tests with the lightweight MREW that i am
using inside my parallelhashlist and i have done also some tests with my
lockfree mpmc fifo queue and what i think is that the CAS is generating
a lot of contention this is is why the lightweight MREW and my lockfree_mpmc
are not scaling , but parallelhashlist is scaling very well cause i am using
lock-striping that is lowering contention.

What are doing Dmitry Vyukov in his distributed rwlock is lowering
the contention using the same method as lock striping that i am using inside
parallelhashlist it is why it is scaling, but there is still a possibility
of contention in his distributed rwlock that can cause a problem to the
scalability if there is too many threads and not a sufficient number of
rwlocks in the Dmitry distributed rwlock to be able to lower the contention.

I have tested parallelhashlist(a parallel hashtable that i have implemented)
with four threads on a quad core and it's giving a very well scaling on both
reads and writes.

Also i have done some scalability tests on my parallelsort library and i
have
come
to the conclusion that parallel heapsort is better on scalability than
parallel quicksort
cause the P part (of the Amdahl equation) is bigger in parallel heapsort
than in parallel
quicksort, the parallel heapsort is doing more on the parallel part, it's
why it scales better than parallel quicksort, but parallel quicksort is
still
faster than parallel heapsort and parallel merge sort on my tests on a
quad core processor.

And about lockfree_mpmc( a lockfree fifo queue), i have done some tests
and it's not scaling cause when you are using a single thread some variables
are updated locally on the L1 cache but using multiple threads those
variables are
loaded from the L2 cache and it's more expensive to load them from the L2
cache.and this does generate much more contention

But even though lockfree_mpmc is not scalable, you can increase
the P (parallel) part by doing more of the same: Increase the volume of
data processed by the P part (and therefore the percentage p of time spent
in computing). This is Gustafson's Law and you will get more scalability.

For example i have used the IntToStr() function on each of the four threads
(on
a quad core) on my lockfree_mpmc test programs to convert from and integer
to a string, so i have increased the P (parallel) part and i have got more
scalability,
this is Gustafson's Law, and you have to remember Gustafson's Law ,
this is very important.


You can download my parallel libraries from

http://pages.videotron.com/aminer/




Sincerely,
Amine Moulay Ramdane.


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