ExaHyPE-Engine issueshttps://gitlab.lrz.de/exahype/ExaHyPE-Engine/-/issues2019-09-21T15:09:46+02:00https://gitlab.lrz.de/exahype/ExaHyPE-Engine/-/issues/296GradQ format2019-09-21T15:09:46+02:00Jean-Matthieu GallardGradQ formatThe format of gradQ (used by NCP and ViscousFlux) is not consistent
* Linear => like F for the flux PDE so gradQ[nDim][nVar]
* Nonlinear => all in one dimension gradQ[nDim\*nVar]
* FV => all in one dimension but with param gradQ[nDim\*...The format of gradQ (used by NCP and ViscousFlux) is not consistent
* Linear => like F for the flux PDE so gradQ[nDim][nVar]
* Nonlinear => all in one dimension gradQ[nDim\*nVar]
* FV => all in one dimension but with param gradQ[nDim\*nData]
Changing this will change the user API and break some application, however a simple adapter could allow them to run until a more in depth correction is done.
Do we need the parameter in FV?
This would also be a good time to maybe change BgradQ similarly if possible (I don't know it it would make sense) so that linear and nonlinear have the same API, even if it introduce a slight inefficiency in nonlinear/FV
@reinarz @domcha @ga24dib @leo.rannabauerJean-Matthieu GallardJean-Matthieu Gallardhttps://gitlab.lrz.de/exahype/ExaHyPE-Engine/-/issues/275User-friendly API, use AbstractSolver as Adapter2019-07-23T10:40:16+02:00Dominic Etienne CharrierUser-friendly API, use AbstractSolver as AdapterIn order to make the code more easy to use, we could reestablish the MySolver_Variables
data access classes.
We could implement the user hooks that take a raw pointer in the AbstractSolver,
and would then call the user solver's implemen...In order to make the code more easy to use, we could reestablish the MySolver_Variables
data access classes.
We could implement the user hooks that take a raw pointer in the AbstractSolver,
and would then call the user solver's implementation which takes the
data access object as argument.
**Example:**
```
void MyAbstractSolver::adjustPointSolution(double* const Q,...) {
Variables vars(Q);
static_cast<MyUserSolver*>(this)->adjustPointSolution(vars,...);
}
```
We could then have additional data access classes for the full solution array, e.g. ``Solution`` and ``ReadOnlySolution``.
Potentially, we could then introduce dimension-aware iterators with appropriate typecast to
a ``Variables`` or ``ReadOnlyVariables`` object.
This would allow writing code like the following.
**Example:**
```
void MyAbstractSolver::refinementCriterion(ReadOnlySolution solution,...) {
double rhoMax = 0;
for ( ReadOnlyVariables& Q : solution ) {
rhoMax = std::max( Q.rho(), rhoMax );
}
}
```
Remarks
-------
``(ReadOnly)Variables`` and ``Variables`` must be split or declared in such a way that
it is clear that there is not necessarily a contiguous array storing
and variables and parameters. We should get rid of the index operator and
have separate functions ``double`` parameter(int i), ``double& variable(int i)`` instead.https://gitlab.lrz.de/exahype/ExaHyPE-Engine/-/issues/175Symbolic flux calculations reduce speed significantly2019-03-21T10:56:35+01:00Ghost UserSymbolic flux calculations reduce speed significantlyI just completed some Likwid performance measurements for the generic and optimised kernels.
For the optimised kernels, I tested a variant using "symbolic variables" in the
flux and eigenvalue computation and another variant using
classi...I just completed some Likwid performance measurements for the generic and optimised kernels.
For the optimised kernels, I tested a variant using "symbolic variables" in the
flux and eigenvalue computation and another variant using
classic array indexing (optimised-nonsymbolic).
The files are suffixed by a ".likwid.csv".
I further attached measured Peano adapter times
The files are suffixed by a ".csv".
Setup
--------
* Compressible Euler equations (Euler_Flow)
* pure ADER-DG scheme (no limiter)
* polynomial orders p=3,5,7,9;
regular 27^3 grid (3D)
* TBB threads=1,12,24.
* Intel icpc17 (USE_IPO=on).
* nonfused (3 algorithmic phases) vs. fused (a single pipelined algorithmic phase) ADER-DG implementation
* no predictor reruns did occur for the fused implementation
Preliminary Results
----------------------------
* Optimised kernels are faster than the generic ones (I kind of expected this ðŸ˜‰)
* Raw array access (optimised-nonsymbolic) is significantly faster than using the "symbolic variables"(optimised).
* Fused scheme pays off (as long as number of reruns is low; very interesting for linear PDEs (no reruns here))
Files
-----
[Euler_ADERDG-no-output-generic.csv](/uploads/f671c93a33e70c9549853f1f51518c9d/Euler_ADERDG-no-output-generic.csv)
[Euler_ADERDG-no-output-generic.likwid.csv](/uploads/0d52ad214f0b1d6cfae4d658a9997cb5/Euler_ADERDG-no-output-generic.likwid.csv)
[Euler_ADERDG-no-output-optimised.csv](/uploads/422288cdc222b1b250b3aad9e2ad73a1/Euler_ADERDG-no-output-optimised.csv)
[Euler_ADERDG-no-output-optimised-nonsymbolic.csv](/uploads/0f84240941230d4bc9f06c76b154396b/Euler_ADERDG-no-output-optimised-nonsymbolic.csv)
[Euler_ADERDG-no-output-optimised.likwid.csv](/uploads/135bd71c668ca0c4c4ee5b7988ea2f17/Euler_ADERDG-no-output-optimised.likwid.csv)
[Euler_ADERDG-no-output-optimised-nonsymbolic.likwid.csv](/uploads/7606eefe559499a1f6de62b5f33905d0/Euler_ADERDG-no-output-optimised-nonsymbolic.likwid.csv)