Parallelhashlist was updated to version 1.31 ...

[aminer]

Hello,


In my previous version of ParallelHashList even if i have used
lock striping , synchronizing access to the counter that computes
the number of entries in the hashtable was reintroducing the scalability
problem of exclusive locking, this counter was  called a hot field because
every mutative operation needs to access it. In version 1.31, parallelhashlist
maintains an independant counter , that counts the number
of entries , for each segment of the hashtable and it uses a lock
for each counter and  this is better for scability.  also i have changed
parallelhashlist to use only 100 lightweight  MREWs (multiple-readers -exclusive-writer)
this will lower the memory consumption  and  this allows multiple threads
to write and read concurently. I am using lock striping with
100 lightweight MREWs (multiple-readers -exclusive-writer) and
this allows up to 100 parallel writes, but this upper bound on
parallel writes will not affect parallel reads, so this will give a good
performance and a good scalability .


Description:

A parallel HashList with O(1) best case and O(log(n)) worst case
access that uses lock striping and 100 lightweight MREWs
(multiple-readers -exclusive-writer) , this allows multiple threads
to write and read concurently. I am using lock striping with
100 lightweight MREWs (multiple-readers -exclusive-writer) and
this allows up to 100 parallel writes, but this upper bound on
parallel writes will not affect parallel reads, 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 also for better scalability.

You can download parallelhashlist version 1.31 from:

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


Language: FPC Pascal v2.2.0+ / Delphi 5+: http://www.freepascal.org/

Operating Systems: Win , Linux and Mac (x86).


and please take a look at the benchmarks here:

http://pages.videotron.com/aminer/parallelhashlist/queue.htm

Note: When i have done those benchmarks , there was not enough/much items
organized as a self-balancing tree in the individual chains of the
hashtable, so , almost all the items was found and inserted in O(1) , so
the parallel part in the Amdahl equation was not much bigger compared
to to the serial part. But you will notice in pratice that as soon as you will have
more items on the chains of the Hashtable, organized as self-balancing tree,
with a worst case log(n)  , the parallel part will become bigger in the Amdahl
equation and you will have better performance and scalability than the numbers
in the graph of the benchmarks
...


Thank you.

Amine Moulay Ramdane.

[aminer]

Hello

You can use the parallel Iterate() method of parallelhashlist
when also you want to do a sort by keys, but don't forget
to use my other parallelsort library to sort in parallel your array
that you have to fill from inside the the TIterateFunc callback..

You can download my parallelsort library from:

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


Amine Moulay Ramdane.

[aminer]

Hello,


All the source code of my following freewares are available on the zipfiles..


Parallel compression library

Parallel sort library

Parallhashlist

Threadpool engine

Lockfree MPMC and SPMC

etc.



You can download the source code of all those freewares from:

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



Thank you.


Amine Moulay Ramdane.

[aminer]

I wrote about parallelhashlist this:


"Note: When i have done those benchmarks , there was not enough/much items organized as a self-balancing tree in the individual chains of the hashtable, so , almost all the items was found and inserted in O(1) , so the parallel part in the Amdahl equation was not much bigger compared to to the serial part. But you will notice in pratice that as soon as you will have more items on the chains of the Hash table , organized as self-balancing tree, with a worst case log(n)  , the parallel part will become bigger in the Amdahl equation and you will have better performance and scalability than the numbers in the graph of the benchmarks ... "


Cause you can increase p 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.



Amine Moulay Ramdane.