arXiv subjects

Osman Seckin Simsek

Publications and source records attributed to Osman Seckin Simsek.

4 recordsLinked to original sources

arXiv2026

From particles to precision. Simulating subsonic turbulence with Smoothed Particle Hydrodynamics

The numerical simulation of subsonic turbulence with smoothed particle hydrodynamics (SPH) has traditionally been hampered by zeroth-order (E0) errors, inaccurate gradient evaluations, and excessive numerical dissipation. We aim to investigate whether a modern SPH formulation can overcome these challenges by comparing its results to those obtained using moving-mesh and meshless finite-volume methods such as AREPO and GIZMO. For this purpose, we used SPH-EXA, a highly scalable, natively GPU-accelerated, state-of-the-art SPH code. Our results show that SPH-EXA accurately reproduces the Kolmogorov inertial range scaling in the subsonic regime with increasing resolution, closely matching the results of the reference methods. We also identify accurate grad-h terms as critical: a noisy standard implementation can imprint spurious granulation in the density field once dissipation is sufficiently reduced. These results demonstrate that, with appropriate methodological advances, SPH can achieve a level of fidelity in modeling subsonic turbulence comparable to the most advanced Eulerian and moving-mesh approaches.

astro-ph.IM↗

arXiv2025

An Autonomy Loop for Dynamic HPC Job Time Limit Adjustment

High Performance Computing (HPC) systems rely on fixed user-provided estimates of job time limits. These estimates are often inaccurate, resulting in inefficient resource use and the loss of unsaved work if a job times out shortly before reaching its next checkpoint. This work proposes a novel feedback-driven autonomy loop that dynamically adjusts HPC job time limits based on checkpoint progress reported by applications. Our approach monitors checkpoint intervals and queued jobs, enabling informed decisions to either early cancel a job after its last completed checkpoint or extend the time limit sufficiently to accommodate the next checkpoint. The objective is to minimize tail waste, that is, the computation that occurs between the last checkpoint and the termination of a job, which is not saved and hence wasted. Through experiments conducted on a subset of a production workload trace, we show a 95% reduction of tail waste, which equates to saving approximately 1.3% of the total CPU time that would otherwise be wasted. We propose various policies that combine early cancellation and time limit extension, achieving tail waste reduction while improving scheduling metrics such as weighted average job wait time. This work contributes an autonomy loop for improved scheduling in HPC environments, where system job schedulers and applications collaborate to significantly reduce resource waste and improve scheduling performance.

cs.DC↗

arXiv2025

Scalable Genomic Context Analysis with GCsnap2 on HPC Clusters

GCsnap2 Cluster is a scalable, high performance tool for genomic context analysis, developed to overcome the limitations of its predecessor, GCsnap1 Desktop. Leveraging distributed computing with mpi4py[.]futures, GCsnap2 Cluster achieved a 22x improvement in execution time and can now perform genomic context analysis for hundreds of thousands of input sequences in HPC clusters. Its modular architecture enables the creation of task-specific workflows and flexible deployment in various computational environments, making it well suited for bioinformatics studies of large-scale datasets. This work highlights the potential for applying similar approaches to solve scalability challenges in other scientific domains that rely on large-scale data analysis pipelines.

cs.DC↗

arXiv2023

Accurate Measurement of Application-level Energy Consumption for Energy-Aware Large-Scale Simulations

Sustainability in high performance computing (HPC) is a major challenge not only for HPC centers and their users, but also for society as the climate goals become stricter. A lot of effort went into reducing the energy consumption of systems in general. Even though certain efforts to optimize the energy-efficiency of HPC workloads exist, most such efforts propose solutions targeting CPUs. As HPC systems shift more and more to GPU-centric architectures, simulation codes increasingly adopt GPU-programming models. This leads to an urgent need to increase the energy-efficiency of GPU-enabled codes. However, studies for reducing the energy consumption of large-scale simulations executing on CPUs and GPUs have received insufficient attention. In this work, we enable accurate power and energy measurements using an open-source toolkit across a range of CPU+GPU node architectures. We use this approach in SPH-EXA, an open-source GPU-centric astrophysical and cosmological simulation framework. We show that with simple code instrumentation, users can accurately measure power and energy related data about their application, beyond data provided by HPC systems alone. The accurate power and energy data provide significant insight to users for conducting energy-aware computational experiments and future energy-aware code development.

cs.DC↗