Demo Data from EDBT 2026 / reviewers in the wild / expert
Phani Motamarri
dblp:118/9502
· DBLP profile ↗
3ranked-venue papers
0as first author
2since 2021 · last 2024
0000-0003-3160-7334ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 3 · 2 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer architecture, parallel and distributed computing, and storage systems
2 papers |
High-performance computing · 60% Processor architecture and microarchitecture · 22% GPUs and heterogeneous computing · 18% | |
| Interdisciplinary, comprehensive, and emerging computing
2 papers |
Computational science and engineering · 100% |
Topics — the 4 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computational science and engineering › computational chemistry › electronic structure calculation
density functional theory |
1.0 | 2 | 2023 | Large-Scale Materials Modeling at Quantum Accuracy: Ab Initio Simulations of Quasicrystals and Interacting Extended Defects in Metallic Alloys · SC 2023 Fast, scalable and accurate finite-element based ab initio calculations using mixed precision computing: 46 PFLOPS simulation of a metallic dislocation system · SC 2019 |
High-performance computing
scientific computing systems |
1.0 | 2 | 2023 | Large-Scale Materials Modeling at Quantum Accuracy: Ab Initio Simulations of Quasicrystals and Interacting Extended Defects in Metallic Alloys · SC 2023 Fast, scalable and accurate finite-element based ab initio calculations using mixed precision computing: 46 PFLOPS simulation of a metallic dislocation system · SC 2019 |
Processor architecture and microarchitecture › computer arithmetic › floating-point arithmetic
mixed-precision arithmetic |
0.4 | 1 | 2019 | Fast, scalable and accurate finite-element based ab initio calculations using mixed precision computing: 46 PFLOPS simulation of a metallic dislocation system · SC 2019 |
GPUs and heterogeneous computing › GPU-accelerated scientific computing
GPU-accelerated simulation |
0.3 | 2 | 2023 | Large-Scale Materials Modeling at Quantum Accuracy: Ab Initio Simulations of Quasicrystals and Interacting Extended Defects in Metallic Alloys · SC 2023 Fast, scalable and accurate finite-element based ab initio calculations using mixed precision computing: 46 PFLOPS simulation of a metallic dislocation system · SC 2019 |
Methods — techniques the papers use, named apart from their topics
mixed-precision algorithms · 1.3machine-learned density functional · 1.3asynchronous compute-communication · 1.3finite-element discretization · 1.0finite element discretization · 1.0mixed precision · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Fast hardware-aware matrix-free algorithms for higher-order finite-element discretized matrix multivector products on distributed systems
Gourab Panigrahi, Nikhil Kodali, Debashis Panda, Phani Motamarri |
J. Parallel Distributed Comput. | 4 |
| 2023 | Large-Scale Materials Modeling at Quantum Accuracy: Ab Initio Simulations of Quasicrystals and Interacting Extended Defects in Metallic AlloysabstractAb initio electronic-structure has remained dichotomous between achievable accuracy and length-scale. Quantum many-body (QMB) methods realize quantum accuracy but fail to scale. Density functional theory (DFT) scales favorably but remains far from quantum accuracy. We present a framework that breaks this dichotomy by use of three interconnected modules: (i) invDFT: a methodological advance in inverse DFT linking QMB methods to DFT; (ii) MLXC: a machine-learned density functional trained with invDFT data, commensurate with quantum accuracy; (iii) DFT-FE-MLXC: an adaptive higher-order spectral finite-element (FE) based DFT implementation that integrates MLXC with efficient solver strategies and HPC innovations in FE-specific dense linear algebra, mixed-precision algorithms, and asynchronous compute-communication. We demonstrate a paradigm shift in DFT that not only provides an accuracy commensurate with QMB methods in ground-state energies, but also attains an unprecedented performance of 659.7 PFLOPS (43.1% peak FP64 performance) on 619,124 electrons using 8,000 GPU nodes of Frontier supercomputer. Sambit Das, Bikash Kanungo, Vishal Subramanian, Gourab Panigrahi, Phani Motamarri, David M. Rogers 0001, Paul M. Zimmerman, Vikram Gavini |
SC | 5 |
| 2019 | Fast, scalable and accurate finite-element based ab initio calculations using mixed precision computing: 46 PFLOPS simulation of a metallic dislocation systemabstractAccurate large-scale first principles calculations based on density functional theory (DFT) in metallic systems are prohibitively expensive due to the asymptotic cubic scaling computational complexity with number of electrons. Using algorithmic advances in employing finite-element discretization for DFT (DFT-FE) in conjunction with efficient computational methodologies and mixed precision strategies, we delay the onset of this cubic scaling by significantly reducing the computational prefactor while increasing the arithmetic intensity and lowering the data movement costs. This has enabled fast, accurate and massively parallel DFT calculations on large-scale metallic systems on both many-core and heterogeneous architectures, with time-to-solution being an order of magnitude faster than state-of-the-art plane-wave DFT codes. We demonstrate an unprecedented sustained performance of 46 PFLOPS (27.8% peak FP64 performance) on a dislocation system in Magnesium containing 105,080 electrons using 3,800 GPU nodes of Summit supercomputer, which is the highest performance to-date among DFT codes. Sambit Das, Phani Motamarri, Vikram Gavini, Bruno Turcksin, Ying Wai Li, Brent Leback |
SC | 2 |