High-Level Support Projects

The Astro-Lab engages in code modernization projects fulfilling the specifications for high-level support in the Gauss Centre for Supercomputing.

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Recent Highlights

DPEcho: General Relativity with SYCL for the 2020s and beyond

AstroLab contact: Salvatore Cielo
Application partners:  
Alexander Pöppl (Intel Corporation, Munich), Luca Del Zanna (Università degli Studi di Firenze), Matteo Bugli (Università degli Studi di Torino)

Numerical sciences are experiencing a renaissance thanks to GPUs and heterogeneous computing, which open for simulations a quantitatively and qualitatively larger class of problems, albeit at the cost of code complexity. The SYCL programming language offers a standard approach to heterogeneity that is scalable, portable, and open.

After ECHO-3DHPC, code for General-Relativistic Magneto-Hydrodynamycs (GR-MHD), written in Fortran with hybrid parallelism via MPI+OpenMP, here we introduce DPEcho, the MPI+SYCL porting of Echo, used to model instabilities, turbulence, propagation of waves, stellar winds and magnetospheres, and astrophysical processes around Black Holes. It supports classic and relativistic MHD, both in Minkowski or any coded GR metric.  The public version of DPEcho is available on GitHub under an Apache II license. 
DPEcho revolves around SYCL device-centric constructs (parallel_for, Unified Shared Memory, ...) in order to minimize data transfer and use the devices at best.

Usage of USM in DPEcho: dynamic allocation on device memory for direct access
// Initialize the SYCL device and queue
sycl::gpu_selector sDev;  sycl::queue qDev(sDev);

// Allocate main variables with USM
double *v[VAR_NUM], *f[VAR_NUM], [...];
for (int i=0; i < VAR_NUM; ++i) {
  // Primitives and fluxes for each variable
  v[i] = malloc_device<double>(grid.numCells, qDev);
  f[i] = malloc_device<double>(grid.numCells, qDev);

We present a scaling comparison of DPEcho versus the baseline ECHO, running the same problem setup. We first conduct a weak scaling test on the Intel Xeon 8174 nodes of SuperMUC-NG at the Leibniz-Rechenzentrum (left). We observe essentially flawless scaling up to 16 nodes, and performance up to 4x higher than the baseline version. The reduced memory footprint of DPEcho allowed the placement of 3843 cells per MPI task, instead of 1923. The inclusion of the Intel® Iris® Xe MAX Graphics (right) increases performance up to 7x, surpassing even non-accelerated HPC hardware. This is a testament to the superior portability and efficiency of SYCL code, making the most efficient use of all classes of hardware.

More resources on DPEcho:

Modernization of the Gasoline code (2021-2022)

AstroLab contact: Jonathan Coles, Salvatore Cielo
Application partner:  Aura Oberja (Universitäts-Sternwarte München, LMU), Tobias Buck (Leibniz-Institut für Astrophysik Potsdam)
Project partner: Christoph Pospiech (Lenovo)

Gasoline2 is a smoother particle hydrodynamics code, built on top of the N-body code PKDGRAV. It is used for cosmological galaxy formation simulation, such as the NIHAO project (Wang et al. 2015). The follow up project, NIHAO2 -- which was granted a total of 20 Mio-CPU hours on SuperMUC-NG at LRZ --, aims to construct a large library of high resolution galaxies at redshift (𝑧) > 3, covering a mass range from massive dwarfs to high-𝑧 quasar host galaxies, while also improving on thermal balance, radiation and chemical enrichment physics. These simulations will help to answer some of the open questions in galaxy formation and to interpret new observational measurements from the James Webb Space Telescope, and the future Athena X-ray Observatory.

The modernization project of Gasoline2_LAC (Gasoline 2 with Local Photoionization Feedback (Obreja et al. 2019), Amiga halo finder for black hole seeding and accretion feedback (Blank et al. 2019) and Chemical enrichment (Buck et al. 2021)) had three initial goals: 1) ensure that the code compiles and runs without errors, 2) lower the memory footprint, and 3) improve the code scaling. After starting the project, we also decided to work on improving the accuracy of the radiation fields needed by the Local Photoionization Feedback. As the radiation fluxes were computing on the tree at the same time with the accelerations, the most natural step forward was to use a higher order (the initial code was only zero-order) for the radiation fields’ multipoles.

The speedups of all kernels involved in the optimization are shown in the left figure. The Gravity and GravTree components present the most significant speedups (20% and >80%, respectively), leading to an overall ~25% improvement.  The right figure shows a single-node scaling test with the g2.19e11 test case (halo mass ∼2 × 1011 M⊙ at redshift 0), featuring the time to solution of individual kernels. While the scaling, on this test case, deviates still early from the ideal (likely due to load imbalance), the optimized performance is still > 25% better than the initial. The figure is also a guide for for finding bottlenecks in view of future optimization projects. Due to its improved scaling, GravTree is no more the top-pressure bottleneck.

We are now able to run simulations more reliably, use more MPI ranks per node with less performance loss, and reach increased accuracy in the radiation fields thanks to the new physics modules. This opens to the possibility of running higher-resolution galaxies with the same resources, or to increase the number of galaxies we can simulate with a given computational budget, for both the LRZ allocation, and in the framework of NIHAO2.

This is measured on the g2.19e11 test, for a single node using the full 48 MPI tasks.

The full report of the optimization project is available here: Gasoline_optimization_report.pdf

 Other projects

AstroLab contact: Luigi Iapichino, Salvatore Cielo
Application partner:  Oliver Porth (Uni Amsterdam), Hector Olivares (Radboud Univ.)
Project partner: Fabio Baruffa (Intel), Anupam Karmakar (Lenovo)

BHAC (the Black Hole Accretion Code) is a multidimensional general relativistic magnetohydrodynamics code based on the MPI-AMRVAC framework. BHAC solves the equations of ideal general relativistic magnetohydrodynamics in one, two or three dimensions on arbitrary stationary space-times, using an efficient block based approach.

BHAC has proven efficient scaling up to ~200 compute node in pure MPI. The hybrid parallelization scheme with OpenMP introduced by the developers could successfully extend scaling up to ~1600 nodes, at the cost of a drop in efficiency to 40% or 65% (with 4 or 8 threads per node, respectively). The main goal for the modernization of the code is thus to check, profile and optimize the OpenMP implementation and node-level performance, including vectorization and memory access.

Early tests revealed a rather high degree of vectorization, though with some room for improvement as the code contains a mix of compute-bound and memory-bound (the slight majority)  kernels. 
VTune’s threading analysis exposed an OpenMP imbalance,  addressed  by adding dynamic scheduling to the loops with significant workload. This yielded an average performance improvement of ∼ 5%. Yet the same analysis identified the main bottleneck as large serial code blocks in the ghost-cell exchange. Restructuring of the code allowed to fully OpenMP-parallelize this section of the code, which led to an average performance increase of 27% compared to the initial code.

At the end of the project, the hybrid implementation is capable to efficiently utilize over 30 000 cores, allowing to study large scale problems. The improvements made through in the AstroLab project are already merged into the staging branch of BHAC and will become part of the next public release.

The full report of the optimization project is available here: AstroLab_BHAC.pdf

AstroLab contact: Luigi Iapichino, Salvatore Cielo
Application partners: Elias Most, Jens Papenfort (Institute for Theoretical Physics, Univ. Frankfurt)
Project partner: Fabio Baruffa (Intel)

The gravitational waves (GW) events produced by collision and merging of compact objects such as neutron stars have recently been observed by the LIGO network for the first time. Understanding the observed electro-magnetic counterparts of these events can grant a complete, multi-messenger view of these extreme phenomena, but requires extensive numerical work to include the strong magnetic fields and turbulence involved. As these require extremely high resolution and computing power, scaling parallel simulation codes to large core units is a must.

The GReX code has  successfully run simulations of neutron star mergers up to 32 k cores on SuperMUC-NG, while taking large advantage of the SIMD capabilities of modern CPUs.  Scaling to extreme core counts (> 100k) is however required to leverage on the power of the coming Exascale Supercomputers. During the optimization project with the Astro-Lab, we identified the main bottlenecks in load balancing and communication when using a large number of AMR grids with many cells. After a detailed characterization through Application Performance Snapshot and Intel Trace Analyzer and Collector,  we performed several node-level scaling tests and investigated the effect of local grid tiling on the performance up to 256 nodes.  However the best results were obtained by an by rewriting the way the code exchanges ghost cells around grids via MPI (see full report below). 

Thus we were able to achieve a scaling close to ideal up to about 50 k cores during the LRZ Extreme Scaling Workshop. The performance degrades significanlty around 70 k cores, however succesful runs with stil satisfactory perfomance were now possible up to about 150 k cores. The reason for this loss is not yet completely understood, but it is likely associated with a different load-balancing at intermediate core counts for the specific problem setup, and will be ivestigated in the future. Both parties were very satisfied with the achieved optimization and look forward to further collaborations.

GReX magnetic amplification in AMR neutron star merging simulation.      

The full report of the optimization project is available here: AstroLab_GReX_report.pdf

AstroLab contact: Luigi Iapichino
Application partner: Matteo Bugli (CEA Saclay, France)
Project partner: Fabio Baruffa (Intel)

In this project we improved the parallelization scheme of ECHO-3DHPC, an efficient astrophysical code used in the modelling of relativistic plasmas. With the help of the Intel Software Development Tools, like Fortran compiler and Profile-Guided Optimization (PGO), Intel MPI library, VTune Amplifier and Inspector we have investigated the performance issues and improved the application scalability and the time to solution. The node-level performance is improved by 2.3x and, thanks to the improved threading parallelisation, the hybrid MPI-OpenMP version of the code outperforms the MPI-only, thus lowering the MPI communication overhead. 

Parallel speed-up at node level (OpenMP-only) for the baseline and optimized code versions.

More details: see article on  Intel Parallel Universe Magazine 34, p. 49.
ArXiv version here.

AstroLab contact: Nicolay Hammer
Application partners: Oliver Porth, Yosuke Mizuno, Elias Most, Ludwig Papenfort, Hector Olivares, Lukas Weih (Institute for Theoretical Physics, Univ. Frankfurt)
Project partner: Florian Merz (Lenovo)

The LRZ AstroLab support project for BHAC targeted the modernisation of the code's solver scheme towards a task-based algorithm. A series of profiling (using Intel VTune Amplifier) and analysis steps before and after the implementation of the tasking revealed further bottlenecks in the memory access pattern. These performance issues were tackled by refactoring important loops of the source code during a dedicated hackathon/workshop for the BHAC developers, held on-site at LRZ. These activities were completed by further efforts which tackled parallel I/O challenges.

More details on BHAC here.

AstroLab contacts: Vytautas Jančauskas, Stephan Hachinger
Application Partner:
Wolfgang Kerzendorf (ESO, Garching near Munich)

TARDIS is a numerical code for Monte-Carlo simulations of supernova spectrum formation. It serves as a tool to analyse the conditions generating the spectra of observed supernovae, i.e. to trace back the supernova structure from the observations.

With this ADVISOR 2016 project, our aim was to obtain an overview of possible performance bottlenecks (and of discovery strategies for bottlenecks in the future), and first steps towards an optimised TARDIS code. The code is largely parallelised with OpenMP, and job-farming techniques (e.g. with MPI) are used to perform ensemble runs on larger machines.

In the context of ADVISOR 2016 and LRZ Astro-Lab, a comprehensive standard profiling/scaling test of the TARDIS code was performed. The profiling brought out no obvious, easy-to-resolve bottlenecks or problems, but was very valuable for planning alogrithmic and conceptual improvements (e.g. an improved convergence strategy) for TARDIS v2.0.

More details: see TARDIS on GitHub, TARDIS on readthedocs, TARDIS v1.0.1 via DOI.