Siegfried Benkner

University of Vienna — Austria

Computer Science · Physical Sciences

26h-index2.0kcitations197works1.02yr avg
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Research Topics

Distributed and Parallel Computing Systems(126), Parallel Computing and Optimization Techniques(86), Cloud Computing and Resource Management(49), Scientific Computing and Data Management(46), Advanced Data Storage Technologies(33)

Publications197 total

Deadline-Aware Resource Allocation and Scheduling of Serverless Workloads on Heterogeneous Clusters
2025
Exploring NCCL Tuning Strategies for Distributed Deep Learning
2025
Accelerating Graph Neural Networks Using a Novel Computation-Friendly Matrix Compression Format
2025
Python to Kubernetes: A Programming and Resource Management Framework for Compute-and Data-intensive Applications
2024· 3 cited
Accelerating Graph Neural Networks with a Novel Matrix Compression Format
arXiv (Cornell University)·2024· 2 citedOpen Access
A Novel Triangular Space-Filling Curve for Cache-Oblivious In-Place Transposition of Square Matrices
2023· 1 cited
Experiences in Architectural Design and Deployment of eHealth and Environmental Applications for Cloud-Edge Continuum
Lecture notes in networks and systems·2023· 1 cited
Tuning a Portfolio Management Model on Historical Data using a Parallel Three-Stage Stochastic Programming Model
2022
The OCR-Vx experience: lessons learned from designing and implementing a task-based runtime system
The Journal of Supercomputing·2022· 3 citedOpen Access
Matching Program Implementations and Heterogeneous Computing Systems
Lecture notes in computer science·2022
Task-Based Performance Portability in HPC
HAL (Le Centre pour la Communication Scientifique Directe)·2021· 5 citedOpen Access
NUMA-aware CPU core allocation in cooperating dynamic applications
2020· 1 cited
Automatic Placement of Tasks to NUMA Nodes in Iterative Applications
2020· 3 cited
A benchmark set of highly-efficient CUDA and OpenCL kernels and its dynamic autotuning with Kernel Tuning Toolkit
Future Generation Computer Systems·2020· 33 citedOpen Access
Programming languages for data-Intensive HPC applications: A systematic mapping study
Parallel Computing·2019· 35 citedOpen Access
Companion data of a Systematic Mapping Study of Programming Languages for Data-Intensive HPC Applications
Figshare·2019Open Access
Companion data of a Systematic Mapping Study of Programming Languages for Data-Intensive HPC Applications
Zenodo (CERN European Organization for Nuclear Research)·2019Open Access
Companion data of a Systematic Mapping Study of Programming Languages for Data-Intensive HPC Applications
Figshare·2019Open Access
Companion data of a Systematic Mapping Study of Programming Languages for Data-Intensive HPC Applications
Figshare·2019Open Access
Companion data of a Systematic Mapping Study of Programming Languages for Data-Intensive HPC Applications
Zenodo (CERN European Organization for Nuclear Research)·2019Open Access
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Frequent Co-authors

Sabri Pllana(15), Gerhard Engelbrecht(11), Ivona Brandić(9), Martin Sandrieser(6), Jochen Fingberg(5), Antonio Arbona(5), Christoph M. Friedrich(5), Christoph Keßler(4), Rainer Schmidt(4), Alessandro Chiarini(4), Robert Dunlop(4), Alejandro F. Frangi(4), Rod Hose(4), Jimison Iavindrasana(4), Luigi Lo Iacono(4), Hariharan Rajasekaran(4), Paul Summers(4), Martin Hofmann‐Apitius(4), Enes Bajrović(4), Jesper Larsson Träff(3)