Skip to content


Switch branches/tags

Name already in use

A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch?

Latest commit


Git stats


Failed to load latest commit information.
Latest commit message
Commit time

CEED benchmarks with deal.II

This project provides small programs to evaluate the ceed benchmark cases with the matrix-free evaluation routines provided by the deal.II finite element library,

Results for mass operator (BP1)

As a first experiment, we run the mass matrix operator within a conjugate gradient iterative solver with the identity preconditioner and record the time for one solver iteration, i.e., we run the solver until convergence to a tolerance of 1e-6 and divide the total solver time by the number of iterations. On SuperMUC Phase 1 (2x8 cores Sandy Bridge E5-2680 2.7 GHz), the following results are obtained:

alt text alt text

When we turn to a more detailed analysis of the behavior at p=6 and q=8, we get the following data: alt text

One solver iteration gets slower on large computations in the left part of the plot, which is mainly due to the MPI communication in the two inner products per iteration in the CG solver. However, when looking specifically at the time per matrix-vector product, we also see that it is 2x faster than the CG iterations. In other words, the three vector updates and two inner products in CG represent half the compute time in the CG solver. Again, this is not too surprising since the kernel is mostly memory bandwidth bound.

Finally, the time for the matrix-vector product on a newer architecture, namely one node of Intel Broadwell E5-2690 v4 (2x14 cores at 2.6 GHz nominal frequency, Turbo Boost to 2.9 GHz enabled) is as follows: alt text

The timings for this system are better then before. For those sizes that fit into the L3 cache, the performance is more than twice as high at 2.77 Gdofs/s versus 1.11 Gdofs/s on 16 cores of Sandy Bridge, reflecting the increased arithmetic power of 28 cores of a newer generation that includes fused multiply-add (FMA) instructions. However, at around 10^7 dofs the throughput is only higher by 65% (1.4 Gdofs/s vs 883 Mdofs/s) because of the memory bandwidth limitations.

Results for Laplace operator (BP3)

We now turn to the evaluation of the three-dimensional Laplacian on a general geometry (bake-off problem 3). Here, we only look at the details of the matrix-vector product and not the conjugate gradient solver as a whole, because the vector operations (that again take a signicant proportion of the time for p>2) are the same as before. On one node of Intel Broadwell E5-2690 v4 (2x14 cores at 2.6 GHz nominal frequency, Turbo Boost to 2.9 GHz enabled) the following throughput is reached: alt text

As opposed to the mass matrix where the only sensible option is to read the precomputed Jacobian determinant from memory, there are several options to implement the variable coefficients for the Laplacian. In the figure above, we used the final geometry tensor, i.e. we store the 6 independent components of the symmetric matrix det(J) J^{-1} J^{-T} and load it in each quadrature point. As for the mass matrix, a clear peak in throughput is visible when the all data fits into the level 3 cache. Since this is six times more data than for the mass matrix, the high performance is limited to significantly smaller problem sizes (and won't be visible at all once we have many nodes with MPI due to the communication overhead).

In the next figure we have looked at some of the alternatives. One alternative we have considered is the default option in deal.II targeting generic differential operators (rather than the specialized code for the Laplacian) where both the Jacobian matrix J^{-T} and the determinant det(J) are stored for each quadrature point and accessed from memory during operator evaluation. Since this option does both 5/3 more memory transfers and slightly more computations, the throughput is lower. Higher throughputs can be recorded when the geometry is computed on the fly. In the case linear geometry on the fly, the Jacobians of the geometry are evaluated from the vertex positions (or rather, a combination of the vertex positions in terms of the factors in the reference coordinates, to simplify the evaluation of the Jacobian on the fly) of the cells in a trilinear way. This variant is slower than merged coefficient tensor for small sizes when the latter fits into the 70 MB of L3 cache, but it is faster for larger sizes because it significantly reduces the memory transfer. To be more specific, the linear geometry case is compute bound (i.e., bound by the instruction throughput without significant phases where the execution waits for data from memory to arrive). In case a curved representation of elements is desired, the linear geometry approximation is of course inadequate and another strategy must be used. One option to compute the Jacobian on the fly is represented by q-geometry on the fly that evaluates the Jacobian from the precomputed location of quadrature points in real space using a collocation derivative. This variant uses only half the memory transfer of merged coefficient tensor, but it is compute bound rather than memory bound on this particular system. Thus, the resuling performance is slower than merged coefficient tensor, but still faster than plain Jacobian. We refer to [Kronbichler and Kormann, 2017] for details on the variants.

alt text

On the Broadwell system, we can enable hyperthreading and use 56 MPI ranks rather than 28. Since our implementation is bound by the instruction throughput with phases of vector access (gather/scatter) and computations (sum factorization, geometry evaluation) interleaved, 2-way simultaneous multithreading (hyperthreading in Intel language) can be used to improve instruction flow. We record around 10-15% higher throughput with this variant: alt text

Finally, we show the analysis of the four ways to represent the geometry also for a higher polynomial degree of eight. The performance is very similar to the case p=5. alt text

Results for Laplace operator (BP5)

The next benchmark to evaluate is the BP5 problem, which represents a 3D Laplacian with the integrals computed with the Gauss-Lobatto quadrature rule rather than the Gauss quadrature, and using p+1 rather than p+2 quadrature points. This setup comes along with a collocation between node points and quadrature points. This allows to cut the work in the sum factorization code paths (related to interpolation of values and gradients between the node values and quadrature points) into half. On one node of Intel Broadwell E5-2690 v4 (2x14 cores at 2.6 GHz nominal frequency, Turbo Boost to 2.9 GHz enabled) the following throughput is reached for the full BP5 problem, including the conjugate gradient solver and a Jacobi preconditioner on the diagonal of the matrix: alt text

By looking only at the matrix-vector product, the numbers get considerably higher, as for the BP1 and BP3 problems. Again, we spend more than 50% of time on vector operations in the CG solver. Furthermore, we clearly see the effect of the L3 cache. alt text

Next, we report results for a newer architecture, namely Intel Xeon Scalable Platinum 8168 (Skylake) with 2x24 cores. The BP5 benchmark runs about 1.85 times (8.0e8 DoFs/sec/it on one node vs 4.3e8 DoFs/sec/it for p=7) as fast for large sizes that run from main memory. Near the peak at 500,000 DoFs per node, Skylake reaches 3.0e9 DoFs/s versus 1.3e9 on Broadwell. These values are close to the capabilities of hardware, as we have measured up to 220 GB/s of memory speed on Skylake versus 120 GB/s on Broadwell (1.83 times) and the arithmetic peak on Skylake at 3840 GFlops/node versus Broadwell's 1300 GFlops/node (2.95 times, speedup was 2.31). alt text

The matrix-vector product behaves similarly, again showing about a 2x boost by Skylake. alt text


Prerequisites and installation

The benchmark problems are designed as small programs that run against a compiled deal.II finite element library that in turn needs a C++ compiler adhering to the C++11 standard, an MPI implementation, and cmake. The following software packages are needed:

  • deal.II, using at least version 8.5.0, see deal.II must be configured to also include the following external packages (no direct access to this packages is necessary, except for the interface through deal.II):

  • MPI

  • p4est for providing parallel adaptive mesh management on forests of quad-trees (2D) or oct-trees (3D). For obtaining p4est, see p4est of at least version is needed for running this project. Installation of p4est can be done via a script provided by deal.II (the last argument specifies the desired installation directory for p4est, e.g. $HOME/sw/p4est):

/path/to/dealii/doc/external-libs/ p4est-1.1.tar.gz /path/to/p4est/install

Given these dependencies, the configuration of deal.II can be done through the following script:

cmake \
    -D CMAKE_CXX_FLAGS="-march=native" \
    -D CMAKE_INSTALL_PREFIX="/path/to/dealii/install/" \
    -D P4EST_DIR="/path/to/p4est/install/" \

Since the matrix-free algorithms in deal.II make intensive use of advanced processor instruction sets (e.g. vectorization through AVX or similar), it is recommended to enable processor-specific optimizations (second line, -march=native). The path on the third line specifies the desired installation directory of deal.II, and the last line points to the location of the source code of deal.II relative to the folder where the cmake script is run. After configuration, run

make -j8
make install

to compile deal.II and install it in the given directory. After installation, the deal.II source and build folder are no longer necessary (unless you find bugs in deal.II and need to modify that code). It is also possible to build the test cases against a build folder of deal.II (which is what the author of this package does almost exclusively).


ceed benchmark evaluation with deal.II library






No releases published


No packages published