Speedup and Quality Up with Ada Tasking
Jan Verschelde
Abstract:
Writing parallel versions for shared memory multicore computers with
Ada tasks requires minimal modifications of the original source code.
For pleasingly parallel computations we experienced almost optimal speedups.
If we can afford to spend the same amount of time as one core,
then we can ask how much better (e.g.: how much more accurate)
we can solve a problem with p cores. This leads to the notion to quality up.
Similar to speedup factors, we can compute quality up factors.
In this talk we report on our coding efforts to write multicore versions
of the path trackers in PHCpack, a free and open source software package
to solve polynomial systems. We started investigating the use of
multithreading to compensate for the overhead of double double and
quad double arithmetic.
FOSDEM 2014, ULB, Brussels, Belgium
slides of the talk