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* Benefiting from deliberately failing linkage
Realizing that I have not written anything here for two /years/ lets just start writing again:
Compilation times for template-heavy C++ codebases such as the one at [[https://openlb.net][the center of my daily life]] can be a real pain.
This mostly got worse since I started to really get my hands dirty in its depths during the [[https://www.helmholtz-hirse.de/series/2022_12_01-seminar_9.html][extensive refactoring]] towards SIMD and GPU support[fn:0].
The current sad high point in compilation times was reached when compiling the first GPU-enabled simulation cases: More than 100 seconds for a single compile on my not too shabby system.
This article will detail how I significantly reduced this on the build system level while gaining useful features.
#+BEGIN_SRC bash
λ ~/p/c/o/e/t/nozzle3d (openlb-env-cuda-env) • time make
make -C ../../.. core
make[1]: Entering directory '/home/common/projects/contrib/openlb-master'
make[1]: Nothing to be done for 'core'.
make[1]: Leaving directory '/home/common/projects/contrib/openlb-master'
nvcc -pthread --forward-unknown-to-host-compiler -x cu -O3 -std=c++17 --generate-code=arch=compute_75,code=[compute_75,sm_75] --extended-lambda --expt-relaxed-constexpr -rdc=true -DPLATFORM_CPU_SISD -DPLATFORM_GPU_CUDA -DDEFAULT_FLOATING_POINT_TYPE=float -I../../../src -c -o nozzle3d.o nozzle3d.cpp
nvcc nozzle3d.o -o nozzle3d -lolbcore -lpthread -lz -ltinyxml -L/run/opengl-driver/lib -lcuda -lcudadevrt -lcudart -L../../../build/lib
________________________________________________________
Executed in 112.27 secs fish external
usr time 109.46 secs 149.00 micros 109.46 secs
sys time 2.42 secs 76.00 micros 2.42 secs
#+END_SRC
Even when considering that this compiles many dozens of individual CUDA kernels for multiple run-time selectable physical models and boundary conditions in addition to the simulation scaffold[fn:1] it still takes too long for comfortably iterating during development.
Needless to say, things did not improve when I started working on heterogeneous execution and the single executable needed to also contain vectorized versions of all models for execution on CPUs in addition to MPI and OpenMP routines.
Even worse, you really want to use Intel's C++ compilers when running CPU-based simulations on Intel-based clusters[fn:2] which plainly is not possible in such a /homogeneous/ compiler setup where everyhing has to pass through =nvcc=.
#+BEGIN_SRC bash
λ ~/p/c/o/e/t/nozzle3d (openlb-env-gcc-env) • time make
g++ -O3 -Wall -march=native -mtune=native -std=c++17 -pthread -DPLATFORM_CPU_SISD -I../../../src -c -o nozzle3d.o nozzle3d.cpp
g++ nozzle3d.o -o nozzle3d -lolbcore -lpthread -lz -ltinyxml -L../../../build/lib
________________________________________________________
Executed in 31.77 secs fish external
usr time 31.21 secs 0.00 micros 31.21 secs
sys time 0.55 secs 693.00 micros 0.55 secs
#+END_SRC
Comparing the GPU build to the previous CPU-only compilation time of around 32 seconds -- while nothing to write home about -- it was still clear that time would be best spent on separating out the CUDA side of things, both to mitigate its performance impact and to enabled a /mixed/ compiler environment.
[fn:0] Definitely a double edged sword: On the one side it enables concise DSL-like compositions of physical models while supporting automatic code optimization and efficient execution accross heterogeneous hardware. On the other side my much younger, Pascal-fluent, self would not be happy with how cryptic and unmaintainable many of my listings can look to the outsider.
In any case, OpenLB as a heavily templatized and meta-programmed C++ software library is a foundational design decision.
[fn:1] Data structures, pre- and post-processing logic, IO routines, ...
[fn:2] Commonly improving performance by quite a few percent
** Requirements
Firstly, any solution would need to exist within the existing plain Makefile based build system[fn:3] and should not complicate the existing build workflow for our users[fn:4].
Secondly, it should allow for defining completely different compilers and configuration flags for the CPU- and the GPU-side of the application.
The intial driving force of speeding up GPU-targeted compilation would then be satisfied as a side effect due to the ability of only recompiling the CPU-side of things as long as no new physical models are introduced. This restriction is useful in the present context as GPU kernels execute the computationally expensive part, i.e. the actual simulation, but generally do not change often during development of new simulation cases after the initial choice of physical model.
[fn:3] Which was a deliberate design decision in order to minimize dependencies considering the minimal build complexity required by OpenLB as a plain CPU-only MPI code. While this could of course be reconsidered in the face of increased target complexity it was not the time to open that bottle.
[fn:4] Mostly domain experts from process engineering, physics or mathematics without much experience in software engineering.
** Approach
Following the requirements, a basic approach is to split the application into two compilation units: One containing only the CPU-implementation consisting of the high level algorithmic structure, pre- and post-processing, communication logic, CPU-targeted simulation kernels and calls to the GPU code.
The other containing only the GPU code consisting of CUDA kernels and their immediate wrappers called from the CPU-side of things -- i.e. only those parts that truly need to be compiled using NVIDIA's =nvcc=.
Given two separated files =cpustuff.cpp= and =gpustuff.cu= it would be easy to compile them using separate configurations and then link them together into a single executable.
The main implementation problem is how to generate two such separated compilation units that can be cleanly linked together, i.e. without duplicating symbols and similar hurdles.
** Implementation
In days past the build system actually contained an option for such separated compilation: termed as the /pre-compiled mode/ in OpenLB speak.
This mode consisted of a somewhat rough and leaky separation between interface and implementation headers that was augmented by many hand-written C++ files containing explicit template instantiations of the aforementioned implementations for certain common arguments.
These C++ files could then be compiled once into a shared library that was linked to the application unit compiled without access to the implementation headers.
While this worked it was always a struggle to keep these files maintained.
Additionally any benefit for the, at that time CPU-only, codebase was negligible and in the end not worth the effort any more causing it to be dropped somewhere on the road to release 1.4.
Nevertheless, the basic approach of compiling a shared libary of explicit template instantiations is sound if we can find a way to automatically generate the instantiations per-case instead of manually maintaining them.
A starting point for this is to take a closer look at the linker errors produced when compiling a simulation case including only the interface headers for the GPU code.
These errors contain partial signatures of all relevant methods from plain function calls
#+BEGIN_SRC bash
λ ~/p/c/o/e/l/cavity3dBenchmark (openlb-env-gcc-openmpi-cuda-env) • mpic++ cavity3d.o -lpthread -lz -ltinyxml -L../../../build/lib -lolbcore
cavity3d.cpp:(...): undefined reference to `olb::gpu::cuda::device::synchronize()'
#+END_SRC
to bulk and boundary collision operator constructions
#+BEGIN_SRC bash
cavity3d.cpp:(...): undefined reference to `olb::ConcreteBlockCollisionO<float, olb::descriptors::D3Q19<>, (olb::Platform)2, olb::dynamics::Tuple<float, olb::descriptors::D3Q19<>, olb::momenta::Tuple<olb::momenta::BulkDensity, olb::momenta::BulkMomentum, olb::momenta::BulkStress, olb::momenta::DefineToNEq>, olb::equilibria::SecondOrder, olb::collision::BGK, olb::dynamics::DefaultCombination> >::ConcreteBlockCollisionO()'
cavity3d.cpp:(...): undefined reference to `olb::ConcreteBlockCollisionO<float, olb::descriptors::D3Q19<>, (olb::Platform)2, olb::CombinedRLBdynamics<float, olb::descriptors::D3Q19<>, olb::dynamics::Tuple<float, olb::descriptors::D3Q19<>, olb::momenta::Tuple<olb::momenta::BulkDensity, olb::momenta::BulkMomentum, olb::momenta::BulkStress, olb::momenta::DefineToNEq>, olb::equilibria::SecondOrder, olb::collision::BGK, olb::dynamics::DefaultCombination>, olb::momenta::Tuple<olb::momenta::InnerCornerDensity3D<1, -1, 1>, olb::momenta::FixedVelocityMomentumGeneric, olb::momenta::InnerCornerStress3D<1, -1, 1>, olb::momenta::DefineSeparately> > >::ConcreteBlockCollisionO()'
#+END_SRC
as well as core data structure accessors:
#+BEGIN_SRC bash
cavity3d.cpp:(.text._ZN3olb20ConcreteBlockLatticeIfNS_11descriptors5D3Q19IJEEELNS_8PlatformE2EE21getPopulationPointersEj[_ZN3olb20ConcreteBlockLatticeIfNS_11descriptors5D3Q19IJEEELNS_8PlatformE2EE21getPopulationPointersEj]+0x37): undefined reference to `olb::gpu::cuda::CyclicColumn<float>::operator[](unsigned long)'
#+END_SRC
These errors are easily turned into a sorted list of unique missing symbols using basic piping
#+BEGIN_SRC makefile
build/missing.txt: $(OBJ_FILES)
$(CXX) $^ $(LDFLAGS) -lolbcore 2>&1 \
| grep -oP ".*undefined reference to \`\K[^']+\)" \
| sort \
| uniq > $@
#+END_SRC
which only assumes that the locale is set to english and -- surprisingly -- works consistently accross any relevant C++ compilers[fn:5], likely due to shared or very similar linkers.
The resulting plain list of C++ method signatures showcases the reasonably structured and consistent template /language/ employed by OpenLB:
#+BEGIN_SRC cpp
olb::ConcreteBlockCollisionO<float, olb::descriptors::D3Q19<>, (olb::Platform)2, olb::CombinedRLBdynamics<float, olb::descriptors::D3Q19<>, olb::dynamics::Tuple<float, olb::descriptors::D3Q19<>, olb::momenta::Tuple<olb::momenta::BulkDensity, olb::momenta::BulkMomentum, olb::momenta::BulkStress, olb::momenta::DefineToNEq>, olb::equilibria::SecondOrder, olb::collision::BGK, olb::dynamics::DefaultCombination>, olb::momenta::Tuple<olb::momenta::VelocityBoundaryDensity<0, -1>, olb::momenta::FixedVelocityMomentumGeneric, olb::momenta::RegularizedBoundaryStress<0, -1>, olb::momenta::DefineSeparately> > >::ConcreteBlockCollisionO()
olb::ConcreteBlockCollisionO<float, olb::descriptors::D3Q19<>, (olb::Platform)2, olb::CombinedRLBdynamics<float, olb::descriptors::D3Q19<>, olb::dynamics::Tuple<float, olb::descriptors::D3Q19<>, olb::momenta::Tuple<olb::momenta::BulkDensity, olb::momenta::BulkMomentum, olb::momenta::BulkStress, olb::momenta::DefineToNEq>, olb::equilibria::SecondOrder, olb::collision::BGK, olb::dynamics::DefaultCombination>, olb::momenta::Tuple<olb::momenta::VelocityBoundaryDensity<0, 1>, olb::momenta::FixedVelocityMomentumGeneric, olb::momenta::RegularizedBoundaryStress<0, 1>, olb::momenta::DefineSeparately> > >::ConcreteBlockCollisionO()
// [...]
#+END_SRC
For example, local cell models -- /Dynamics/ in OpenLB speak -- are mostly implemented as tuples of momenta, equilibrium functions and collision operators[fn:6].
All such relevant classes tend to follow a consistent structure in what methods with which arguments and return types they implement.
We can use this domain knowledge of our codebase to transform the incomplete signatures in our new =missing.txt= into a full list of explicit template instantiations written in valid C++.
#+BEGIN_SRC makefile
build/olbcuda.cu: build/missing.txt
# Generate includes of the case source
# (replaceable by '#include <olb.h>' if no custom operators are implemented in the application)
echo -e '$(CPP_FILES:%=\n#include "../%")' > $@
# Transform missing symbols into explicit template instantiations by:
# - filtering for a set of known and automatically instantiable methods
# - excluding destructors
# - dropping resulting empty lines
# - adding the explicit instantiation prefix (all supported methods are void, luckily)
cat build/missing.txt \
| grep '$(subst $() $(),\|,$(EXPLICIT_METHOD_INSTANTIATION))' \
| grep -wv '.*\~.*\|FieldTypeRegistry()' \
| xargs -0 -n1 | grep . \
| sed -e 's/.*/template void &;/' -e 's/void void/void/' >> $@
# - filtering for a set of known and automatically instantiable classes
# - dropping method cruft and wrapping into explicit class instantiation
# - removing duplicates
cat build/missing.txt \
| grep '.*\($(subst $() $(),\|,$(EXPLICIT_CLASS_INSTANTIATION))\)<' \
| sed -e 's/\.*>::.*/>/' -e 's/.*/template class &;/' -e 's/class void/class/' \
| sort | uniq >> $@
#+END_SRC
Note that this is only possible due to full knowledge of and control over the target codebase.
In case this is not clear already: In no way do I recommend that this approach be followed in a more general context[fn:7].
It was only the quickest and most maintainable approach to achieving the stated requirements given the particulars of OpenLB.
As soon as the build system dumped the first =olbcuda.cu= file into the =build= directory I thought that all that remained was to compile this into a shared library and link it all together.
However, the resulting shared library contained not only the explicitly instantiated symbols but also additional stuff that they required.
This caused quite a few duplicate symbol errors when I tried to link the library and the main executable.
While linking could still be forced by ignoring these errors, the resulting executable was not running properly.
This is where I encountered something unfamiliar to me: Linker version scripts.
[fn:5] Which spans various versions of GCC, Clang and Intel C++
[fn:6] Momenta representing how to compute macroscopic quantities such as density and velocity, equilibrium representing the /undistrubed/ representation of said quantities in terms of population values and the collision operator representing the specific function used to /relax/ the current population towards this equilibrium. For more details on LBM see e.g. my articles on [[/article/fun_with_compute_shaders_and_fluid_dynamics/][Fun with Compute Shaders and Fluid Dynamics]], a [[/article/year_of_lbm/][Year of LBM]]
or even my just-in-time visualized [[https://literatelb.org][literate implementation]].
[fn:7] However, implementing such a explicit instantiation generator that works for any C++ project could be an interesting project for… somebody.
** Conclusion
Surprisingly, this quick and dirty approach turned out to be unexpectedly stable and portable accross systems and compilers.
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