arXiv subjects

Rene Widera

Publications and source records attributed to Rene Widera.

5 recordsLinked to original sources

arXiv2025

A Parallel and Highly-Portable HPC Poisson Solver: Preconditioned Bi-CGSTAB with alpaka

This paper presents the design, implementation, and performance analysis of a parallel and GPU-accelerated Poisson solver based on the Preconditioned Bi-Conjugate Gradient Stabilized (Bi-CGSTAB) method. The implementation utilizes the MPI standard for distributed-memory parallelism, while on-node computation is handled using the alpaka framework: this ensures both shared-memory parallelism and inherent performance portability across different hardware architectures. We evaluate the solver's performances on CPUs and GPUs (NVIDIA Hopper H100 and AMD MI250X), comparing different preconditioning strategies, including Block Jacobi and Chebyshev iteration, and analyzing the performances both at single and multi-node level. The execution efficiency is characterized with a strong scaling test and using the AMD Omnitrace profiling tool. Our results indicate that a communication-free preconditioner based on the Chebyshev iteration can speed up the solver by more than six times. The solver shows comparable performances across different GPU architectures, achieving a speed-up in computation up to 50 times compared to the CPU implementation. In addition, it shows a strong scaling efficiency greater than 90% up to 64 devices.

cs.DC↗

arXiv2025

The Artificial Scientist -- in-transit Machine Learning of Plasma Simulations

Increasing HPC cluster sizes and large-scale simulations that produce petabytes of data per run, create massive IO and storage challenges for analysis. Deep learning-based techniques, in particular, make use of these amounts of domain data to extract patterns that help build scientific understanding. Here, we demonstrate a streaming workflow in which simulation data is streamed directly to a machine-learning (ML) framework, circumventing the file system bottleneck. Data is transformed in transit, asynchronously to the simulation and the training of the model. With the presented workflow, data operations can be performed in common and easy-to-use programming languages, freeing the application user from adapting the application output routines. As a proof-of-concept we consider a GPU accelerated particle-in-cell (PIConGPU) simulation of the Kelvin- Helmholtz instability (KHI). We employ experience replay to avoid catastrophic forgetting in learning from this non-steady process in a continual manner. We detail challenges addressed while porting and scaling to Frontier exascale system.

physics.comp-ph↗

arXiv2023

EZ: An Efficient, Charge Conserving Current Deposition Algorithm for Electromagnetic Particle-In-Cell Simulations

We present EZ, a novel current deposition algorithm for particle-in-cell (PIC) simulations. EZ calculates the current density on the electromagnetic grid due to macro-particle motion within a time step by solving the continuity equation of electrodynamics. Being a charge conserving hybridization of Esirkepov's method and ZigZag, we refer to it as ``EZ'' as shorthand for ``Esirkepov meets ZigZag''. Simulations of a warm, relativistic plasma with PIConGPU show that EZ achieves the same level of charge conservation as the commonly used method by Esirkepov, yet reaches higher performance for macro-particle assignment-functions up to third-order. In addition to a detailed description of the functioning of EZ, reasons for the expected and observed performance increase are given, and guidelines for its implementation aiming at highest performance on GPUs are provided.

physics.comp-ph↗

arXiv2022

Application Experiences on a GPU-Accelerated Arm-based HPC Testbed

This paper assesses and reports the experience of ten teams working to port,validate, and benchmark several High Performance Computing applications on a novel GPU-accelerated Arm testbed system. The testbed consists of eight NVIDIA Arm HPC Developer Kit systems built by GIGABYTE, each one equipped with a server-class Arm CPU from Ampere Computing and A100 data center GPU from NVIDIA Corp. The systems are connected together using Infiniband high-bandwidth low-latency interconnect. The selected applications and mini-apps are written using several programming languages and use multiple accelerator-based programming models for GPUs such as CUDA, OpenACC, and OpenMP offloading. Working on application porting requires a robust and easy-to-access programming environment, including a variety of compilers and optimized scientific libraries. The goal of this work is to evaluate platform readiness and assess the effort required from developers to deploy well-established scientific workloads on current and future generation Arm-based GPU-accelerated HPC systems. The reported case studies demonstrate that the current level of maturity and diversity of software and tools is already adequate for large-scale production deployments.

cs.DC↗

arXiv2017

On the Scalability of Data Reduction Techniques in Current and Upcoming HPC Systems from an Application Perspective

We implement and benchmark parallel I/O methods for the fully-manycore driven particle-in-cell code PIConGPU. Identifying throughput and overall I/O size as a major challenge for applications on today's and future HPC systems, we present a scaling law characterizing performance bottlenecks in state-of-the-art approaches for data reduction. Consequently, we propose, implement and verify multi-threaded data-transformations for the I/O library ADIOS as a feasible way to trade underutilized host-side compute potential on heterogeneous systems for reduced I/O latency.

cs.PF↗