17 citations · 44 across the 11 of their papers we have counts for
12 papers
HPL and HPL-MxP at Exascale on Aurora: Performance Engineering and Characterization
Kazushige Goto, Huda Ibeid, Kalyan Kumaran +3
Running HPL and HPL-MxP at exascale requires more than optimized numerical kernels; it requires coordinated performance engineering across communication, resource mapping, runtime…
Scaling MPI Applications on Aurora
Huda Ibeid, Anthony-Trung Nguyen, Aditya Nishtala +10
The Aurora supercomputer, which was deployed at Argonne National Laboratory in 2024, is currently one of three Exascale machines in the world on the Top500 list. The Aurora system…
Aurora: Architecting Argonne's First Exascale Supercomputer for Accelerated Scientific Discovery
William E. Allcock, Benjamin S. Allen, James Anchell +106
Aurora is Argonne National Laboratory's pioneering Exascale supercomputer, designed to accelerate scientific discovery with cutting-edge architectural innovations. Key new technolo…
Performance Analysis of HPC applications on the Aurora Supercomputer: Exploring the Impact of HBM-Enabled Intel Xeon Max CPUs
Huda Ibeid, Vikram Narayana, Jeongnim Kim +3
The Aurora supercomputer is an exascale-class system designed to tackle some of the most demanding computational workloads. Equipped with both High Bandwidth Memory (HBM) and DDR m…
Simulation-Based Performance Prediction of HPC Applications: A Case Study of HPL
Gen Xu, Huda Ibeid, Xin Jiang +2
We propose a simulation-based approach for performance modeling of parallel applications on high-performance computing platforms. Our approach enables full-system performance model…
FFT, FMM, and Multigrid on the Road to Exascale: performance challenges and opportunities
Huda Ibeid, Luke Olson, William Gropp
FFT, FMM, and multigrid methods are widely used fast and highly scalable solvers for elliptic PDEs. However, emerging large-scale computing systems are introducing challenges in co…
Learning with Analytical Models
Huda Ibeid, Siping Meng, Oliver Dobon +2
To understand and predict the performance of scientific applications, several analytical and machine learning approaches have been proposed, each having its advantages and disadvan…
Communication Reducing Algorithms for Distributed Hierarchical N-Body Problems with Boundary Distributions
Mustafa Abduljabbar, George Markomanolis, Huda Ibeid +2
Reduction of communication and efficient partitioning are key issues for achieving scalability in hierarchical -Body algorithms like FMM. In the present work, we propose four in…
A Matrix-free Preconditioner for the Helmholtz Equation based on the Fast Multipole Method
Huda Ibeid, Rio Yokota, David Keyes
Fast multipole methods (FMM) were originally developed for accelerating -body problems for particle-based methods. FMM is more than an -body solver, however. Recent efforts t…
Fast Multipole Method as a Matrix-Free Hierarchical Low-Rank Approximation
Rio Yokota, Huda Ibeid, David Keyes
There has been a large increase in the amount of work on hierarchical low-rank approximation methods, where the interest is shared by multiple communities that previously did not i…
A Performance Model for the Communication in Fast Multipole Methods on HPC Platforms
Huda Ibeid, Rio Yokota, David Keyes
Exascale systems are predicted to have approximately one billion cores, assuming Gigahertz cores. Limitations on affordable network topologies for distributed memory systems of suc…
Fast Multipole Preconditioners for Sparse Matrices Arising from Elliptic Equations
Huda Ibeid, Rio Yokota, Jennifer Pestana +1
Among optimal hierarchical algorithms for the computational solution of elliptic problems, the Fast Multipole Method (FMM) stands out for its adaptability to emerging architectures…