Difference between revisions of "GPU610/SSD"
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{{GPU610/DPS915 Index | 20131}} | {{GPU610/DPS915 Index | 20131}} | ||
− | = | + | = Drive_God_Lin = |
− | + | We are going to be working on a [http://home.web.cern.ch/ CERN] project called Drive_God_Lin (more info soon...) | |
== Team Members == | == Team Members == | ||
# [mailto:sganouts@myseneca.ca?subject=GPU610 Sezar Gantous] | # [mailto:sganouts@myseneca.ca?subject=GPU610 Sezar Gantous] | ||
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=== Assignment 1 === | === Assignment 1 === | ||
Sezar: | Sezar: | ||
+ | I decided to profile CERN project - Drive_God_Lin. | ||
+ | After gprof the project with the test data provided I learned the following: | ||
+ | |||
+ | (summery of gprof) | ||
+ | == | ||
+ | Each sample counts as 0.01 seconds. | ||
+ | % cumulative self self total | ||
+ | time seconds seconds calls ms/call ms/call name | ||
+ | 39.98 26.68 26.68 524 50.92 50.92 ordres_ | ||
+ | 37.42 51.65 24.97 314400 0.08 0.08 zfunr_ | ||
+ | 12.69 60.12 8.47 314400 0.03 0.03 cfft_ | ||
+ | ... | ||
+ | == | ||
+ | As you can see, most the program's time is spent in the ordres() subroutine and zfunr() subroutine; both are localed in the fortran portion of the program (there is a c portion as well). | ||
+ | |||
+ | Furthermore, this program is already parallelized using openmp; which means it may be farther parallelized using cuda technology. | ||
+ | In these two subroutines (specially in orders()) there are a lot of nested loops, if statements, goto's, and even some sore of a search algorithm (in orders() only) that I'm certain it could be notably improved using cuda technology. | ||
+ | |||
---- | ---- |
Revision as of 11:49, 9 February 2013
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Contents
Drive_God_Lin
We are going to be working on a CERN project called Drive_God_Lin (more info soon...)
Team Members
Progress
Assignment 1
Sezar: I decided to profile CERN project - Drive_God_Lin. After gprof the project with the test data provided I learned the following:
(summery of gprof) == Each sample counts as 0.01 seconds.
% cumulative self self total time seconds seconds calls ms/call ms/call name 39.98 26.68 26.68 524 50.92 50.92 ordres_ 37.42 51.65 24.97 314400 0.08 0.08 zfunr_ 12.69 60.12 8.47 314400 0.03 0.03 cfft_
... == As you can see, most the program's time is spent in the ordres() subroutine and zfunr() subroutine; both are localed in the fortran portion of the program (there is a c portion as well).
Furthermore, this program is already parallelized using openmp; which means it may be farther parallelized using cuda technology. In these two subroutines (specially in orders()) there are a lot of nested loops, if statements, goto's, and even some sore of a search algorithm (in orders() only) that I'm certain it could be notably improved using cuda technology.
Dylan:
Stephanie:
For assignment one I profiled a closest pair algorithm. The source code can be found here:
http://rosettacode.org/wiki/Closest-pair_problem/C
I was able to run the code successfully on Matrix and I believe it is a good candidate to parallelize. The closest() function generally eats up the most time and I believe that most of its remedial processes could be done by the GPU.
A useful reference site to help explain closest pair algorithms:
http://www.algorithmist.com/index.php/Closest_Pair