arXiv subjects

René Widera

Publications and source records attributed to René Widera.

13 recordsLinked to original sources

arXiv2025

Optimizations on Graph-Level for Domain Specific Computations in Julia and Application to QED

Complex computational problems in science often consist of smaller parts that can have largely distinct compute requirements from one another. For optimal efficiency, analyzing each subtask and scheduling it on the best-suited hardware would be necessary. Other considerations must be taken into account, too, such as parallelism, dependencies between different subtasks, and data transfer speeds between devices. To achieve this, directed acyclic graphs are often employed to represent these problems and enable utilizing as much hardware as possible on a given machine. In this paper, we present a software framework written in Julia capable of automatically and dynamically producing statically scheduled and compiled code. We lay theoretical foundations and add domain-specific information about the computation to the existing concepts of DAG scheduling, enabling optimizations that would otherwise be impossible. To illustrate the theory we implement an example application: the computation of matrix elements for scattering processes with many external particles in quantum electrodynamics.

cs.DC↗

arXiv2025

Radiative Signatures of Magnetic Reconnection: An Approach to Remote Probing of Reconnection Dynamics

Magnetic reconnection drives a wide range of astrophysical phenomena, including geomagnetic storms, solar flares, and activity in blazars. However, direct measurement of key reconnection observables remains challenging due to the remote and extreme nature of these environments. While high-energy particle showers observed on Earth are often attributed to reconnection, the underlying mechanisms are not fully understood, and clear diagnostic signatures are lacking. We present a theoretical, data-driven approach for identifying reconnection radiation signatures and enabling remote diagnostics of reconnection in astrophysical settings through radiation spectra. Using particle-in-cell (PIC) simulations of magnetic reconnection, we generate radiation spectra and establish connections between spectral features and the underlying reconnection dynamics. We develop a method to estimate the ratio of the reconnection electric field to the plasmoid magnetic field from spectral data. Analytic calculations show that other parameters can be extracted in the ultra-relativistic reconnection regime, such as the magnetic field or the current sheet width.

astro-ph.HE↗

arXiv2024

Enabling High-Throughput Parallel I/O in Particle-in-Cell Monte Carlo Simulations with openPMD and Darshan I/O Monitoring

Large-scale HPC simulations of plasma dynamics in fusion devices require efficient parallel I/O to avoid slowing down the simulation and to enable the post-processing of critical information. Such complex simulations lacking parallel I/O capabilities may encounter performance bottlenecks, hindering their effectiveness in data-intensive computing tasks. In this work, we focus on introducing and enhancing the efficiency of parallel I/O operations in Particle-in-Cell Monte Carlo simulations. We first evaluate the scalability of BIT1, a massively-parallel electrostatic PIC MC code, determining its initial write throughput capabilities and performance bottlenecks using an HPC I/O performance monitoring tool, Darshan. We design and develop an adaptor to the openPMD I/O interface that allows us to stream PIC particle and field information to I/O using the BP4 backend, aggressively optimized for I/O efficiency, including the highly efficient ADIOS2 interface. Next, we explore advanced optimization techniques such as data compression, aggregation, and Lustre file striping, achieving write throughput improvements while enhancing data storage efficiency. Finally, we analyze the enhanced high-throughput parallel I/O and storage capabilities achieved through the integration of openPMD with rapid metadata extraction in BP4 format. Our study demonstrates that the integration of openPMD and advanced I/O optimizations significantly enhances BIT1's I/O performance and storage capabilities, successfully introducing high throughput parallel I/O and surpassing the capabilities of traditional file I/O.

cs.DC↗

arXiv2022

LLAMA: The Low-Level Abstraction For Memory Access

The performance gap between CPU and memory widens continuously. Choosing the best memory layout for each hardware architecture is increasingly important as more and more programs become memory bound. For portable codes that run across heterogeneous hardware architectures, the choice of the memory layout for data structures is ideally decoupled from the rest of a program. This can be accomplished via a zero-runtime-overhead abstraction layer, underneath which memory layouts can be freely exchanged. We present the Low-Level Abstraction of Memory Access (LLAMA), a C++ library that provides such a data structure abstraction layer with example implementations for multidimensional arrays of nested, structured data. LLAMA provides fully C++ compliant methods for defining and switching custom memory layouts for user-defined data types. The library is extensible with third-party allocators. Providing two close-to-life examples, we show that the LLAMA-generated AoS (Array of Structs) and SoA (Struct of Arrays) layouts produce identical code with the same performance characteristics as manually written data structures. Integrations into the SPEC CPU\textsuperscript{\textregistered} lbm benchmark and the particle-in-cell simulation PIConGPU demonstrate LLAMA's abilities in real-world applications. LLAMA's layout-aware copy routines can significantly speed up transfer and reshuffling of data between layouts compared with naive element-wise copying. LLAMA provides a novel tool for the development of high-performance C++ applications in a heterogeneous environment.

cs.PF↗

arXiv2022

Challenges Porting a C++ Template-Metaprogramming Abstraction Layer to Directive-based Offloading

HPC systems employ a growing variety of compute accelerators with different architectures and from different vendors. Large scientific applications are required to run efficiently across these systems but need to retain a single code-base in order to not stifle development. Directive-based offloading programming models set out to provide the required portability, but, to existing codes, they themselves represent yet another API to port to. Here, we present our approach of porting the GPU-accelerated particle-in-cell code PIConGPU to OpenACC and OpenMP target by adding two new backends to its existing C++-template metaprogramming-based offloading abstraction layer alpaka and avoiding other modifications to the application code. We introduce our approach in the face of conflicts between requirements and available features in the standards as well as practical hurdles posed by immature compiler support.

cs.PL↗

arXiv2022

Transitioning from file-based HPC workflows to streaming data pipelines with openPMD and ADIOS2

This paper aims to create a transition path from file-based IO to streaming-based workflows for scientific applications in an HPC environment. By using the openPMP-api, traditional workflows limited by filesystem bottlenecks can be overcome and flexibly extended for in situ analysis. The openPMD-api is a library for the description of scientific data according to the Open Standard for Particle-Mesh Data (openPMD). Its approach towards recent challenges posed by hardware heterogeneity lies in the decoupling of data description in domain sciences, such as plasma physics simulations, from concrete implementations in hardware and IO. The streaming backend is provided by the ADIOS2 framework, developed at Oak Ridge National Laboratory. This paper surveys two openPMD-based loosely-coupled setups to demonstrate flexible applicability and to evaluate performance. In loose coupling, as opposed to tight coupling, two (or more) applications are executed separately, e.g. in individual MPI contexts, yet cooperate by exchanging data. This way, a streaming-based workflow allows for standalone codes instead of tightly-coupled plugins, using a unified streaming-aware API and leveraging high-speed communication infrastructure available in modern compute clusters for massive data exchange. We determine new challenges in resource allocation and in the need of strategies for a flexible data distribution, demonstrating their influence on efficiency and scaling on the Summit compute system. The presented setups show the potential for a more flexible use of compute resources brought by streaming IO as well as the ability to increase throughput by avoiding filesystem bottlenecks.

cs.DC↗

arXiv2021

Metrics and Design of an Instruction Roofline Model for AMD GPUs

Due to the recent announcement of the Frontier supercomputer, many scientific application developers are working to make their applications compatible with AMD architectures (CPU-GPU), which means moving away from the traditional CPU and NVIDIA-GPU systems. Due to the current limitations of profiling tools for AMD GPUs, this shift leaves a void in how to measure application performance on AMD GPUs. In this paper, we design an instruction roofline model for AMD GPUs using AMD's ROCProfiler and a benchmarking tool, BabelStream (the HIP implementation), as a way to measure an application's performance in instructions and memory transactions on new AMD hardware. Specifically, we create instruction roofline models for a case study scientific application, PIConGPU, an open source particle-in-cell (PIC) simulations application used for plasma and laser-plasma physics on the NVIDIA V100, AMD Radeon Instinct MI60, and AMD Instinct MI100 GPUs. When looking at the performance of multiple kernels of interest in PIConGPU we find that although the AMD MI100 GPU achieves a similar, or better, execution time compared to the NVIDIA V100 GPU, profiling tool differences make comparing performance of these two architectures hard. When looking at execution time, GIPS, and instruction intensity, the AMD MI60 achieves the worst performance out of the three GPUs used in this work.

cs.DC↗

arXiv2020

Spectral Control via Multi-Species Effects in PW-Class Laser-Ion Acceleration

Laser-ion acceleration with ultra-short pulse, PW-class lasers is dominated by non-thermal, intra-pulse plasma dynamics. The presence of multiple ion species or multiple charge states in targets leads to characteristic modulations and even mono-energetic features, depending on the choice of target material. As spectral signatures of generated ion beams are frequently used to characterize underlying acceleration mechanisms, thermal, multi-fluid descriptions require a revision for predictive capabilities and control in next-generation particle beam sources. We present an analytical model with explicit inter-species interactions, supported by extensive ab initio simulations. This enables us to derive important ensemble properties from the spectral distribution resulting from those multi-species effects for arbitrary mixtures. We further propose a potential experimental implementation with a novel cryogenic target, delivering jets with variable mixtures of hydrogen and deuterium. Free from contaminants and without strong influence of hardly controllable processes such as ionization dynamics, this would allow a systematic realization of our predictions for the multi-species effect.

physics.plasm-ph↗

arXiv2018

Quantitatively consistent computation of coherent and incoherent radiation in particle-in-cell codes - a general form factor formalism for macro-particles

Quantitative predictions from synthetic radiation diagnostics often have to consider all accelerated particles. For particle-in-cell (PIC) codes, this not only means including all macro-particles but also taking into account the discrete electron distribution associated with them. This paper presents a general form factor formalism that allows to determine the radiation from this discrete electron distribution in order to compute the coherent and incoherent radiation self-consistently. Furthermore, we discuss a memory-efficient implementation that allows PIC simulations with billions of macro-particles. The impact on the radiation spectra is demonstrated on a large scale LWFA simulation.

physics.comp-ph↗

arXiv2017

Tuning and optimization for a variety of many-core architectures without changing a single line of implementation code using the Alpaka library

We present an analysis on optimizing performance of a single C++11 source code using the Alpaka hardware abstraction library. For this we use the general matrix multiplication (GEMM) algorithm in order to show that compilers can optimize Alpaka code effectively when tuning key parameters of the algorithm. We do not intend to rival existing, highly optimized DGEMM versions, but merely choose this example to prove that Alpaka allows for platform-specific tuning with a single source code. In addition we analyze the optimization potential available with vendor-specific compilers when confronted with the heavily templated abstractions of Alpaka. We specifically test the code for bleeding edge architectures such as Nvidia's Tesla P100, Intel's Knights Landing (KNL) and Haswell architecture as well as IBM's Power8 system. On some of these we are able to reach almost 50\% of the peak floating point operation performance using the aforementioned means. When adding compiler-specific #pragmas we are able to reach 5 TFLOPS/s on a P100 and over 1 TFLOPS/s on a KNL system.

cs.DC↗

arXiv2016

In situ, steerable, hardware-independent and data-structure agnostic visualization with ISAAC

The computation power of supercomputers grows faster than the bandwidth of their storage and network. Especially applications using hardware accelerators like Nvidia GPUs cannot save enough data to be analyzed in a later step. There is a high risk of loosing important scientific information. We introduce the in situ template library ISAAC which enables arbitrary applications like scientific simulations to live visualize their data without the need of deep copy operations or data transformation using the very same compute node and hardware accelerator the data is already residing on. Arbitrary meta data can be added to the renderings and user defined steering commands can be asynchronously sent back to the running application. Using an aggregating server, ISAAC streams the interactive visualization video and enables user to access their applications from everywhere.

cs.DC↗

arXiv2016

Performance-Portable Many-Core Plasma Simulations: Porting PIConGPU to OpenPower and Beyond

With the appearance of the heterogeneous platform OpenPower,many-core accelerator devices have been coupled with Power host processors for the first time. Towards utilizing their full potential, it is worth investigating performance portable algorithms that allow to choose the best-fitting hardware for each domain-specific compute task. Suiting even the high level of parallelism on modern GPGPUs, our presented approach relies heavily on abstract meta-programming techniques, which are essential to focus on fine-grained tuning rather than code porting. With this in mind, the CUDA-based open-source plasma simulation code PIConGPU is currently being abstracted to support the heterogeneous OpenPower platform using our fast porting interface cupla, which wraps the abstract parallel C++11 kernel acceleration library Alpaka. We demonstrate how PIConGPU can benefit from the tunable kernel execution strategies of the Alpaka library, achieving portability and performance with single-source kernels on conventional CPUs, Power8 CPUs and NVIDIA GPUs.

cs.DC↗

arXiv2016

Alpaka - An Abstraction Library for Parallel Kernel Acceleration

Porting applications to new hardware or programming models is a tedious and error prone process. Every help that eases these burdens is saving developer time that can then be invested into the advancement of the application itself instead of preserving the status-quo on a new platform. The Alpaka library defines and implements an abstract hierarchical redundant parallelism model. The model exploits parallelism and memory hierarchies on a node at all levels available in current hardware. By doing so, it allows to achieve platform and performance portability across various types of accelerators by ignoring specific unsupported levels and utilizing only the ones supported on a specific accelerator. All hardware types (multi- and many-core CPUs, GPUs and other accelerators) are supported for and can be programmed in the same way. The Alpaka C++ template interface allows for straightforward extension of the library to support other accelerators and specialization of its internals for optimization. Running Alpaka applications on a new (and supported) platform requires the change of only one source code line instead of a lot of \#ifdefs.

cs.DC↗