hkaiser changed the topic of #ste||ar to: STE||AR: Systems Technology, Emergent Parallelism, and Algorithm Research | stellar-group.org | HPX: A cure for performance impaired parallel applications | github.com/STEllAR-GROUP/hpx | This channel is logged: irclog.cct.lsu.edu
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<Pritesh_V> Hi Everyone
<hkaiser> hey Pritesh_V
<deepak[m]> "Sieve of Eratosthenes" (most efficient way to find all primes smaller than n when n is smaller than 10 million)
<deepak[m]> --> is this algorithm a good fit for heavy computational use of CPU. And can it be improved by hpx for performance. I was just trying to implement it.
<hkaiser> deepak[m]: the question is whether the algorithm you chose can be efficiently parallelized
<hkaiser> otherwise using HPX will not give you too much of a benefit
<deepak[m]> hkaiser: oh I see
<Pritesh_V> "SuperDC" (efficient way for computing eigenvalue for some rank structured matrices) I think think we can solve this problem in parallel. here's the link of paper: https://arxiv.org/pdf/2108.04209.pdf
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<gonidelis[m]> Pritesh_V amazing idea!
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<gonidelis[m]> dimpapag: welcome!
<Pritesh_V> Thanks gonidelis[m] shoult I start writing proposal on this?
<gonidelis[m]> Pritesh_V: feel free, yeaj
<gonidelis[m]> yeah*
<dimpapag> I have already built HPX locally and played around a bit with pybind11
<gonidelis[m]> awesome! which one interests u more?
<gonidelis[m]> dimpapag: for optimizing the parallel algorithms our STL algos implementations are here
<gonidelis[m]> under algorithms are the C++17 versions and under container_algorithms are the ranges (c++20) versions
<dimpapag> Since I have a background in Python, I started looking on the Pythonize project
<gonidelis[m]> cool!
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