Presented By: Michigan Institute for Computational Discovery and Engineering
MICDE - ME seminar: Phani Motamarri, Indian Institute of Science, Bangalore
Inexact yet Accurate: Unlocking Quantum Modeling of Materials at Scale through Approximation-Tolerant Algorithms
Abstract:
Modern computing architectures increasingly rely on iterative solvers that employ reduced-precision computation and communication-reduction techniques to lower time-to-solution and improve scalability. However, eigensolvers in scientific simulations have struggled to exploit such approximations without compromising accuracy. We present an eigensolver R-ChFSI, a residual-based reformulation of Chebyshev Filtered Subspace Iteration (ChFSI) provably tolerant to inexact matrix–vector products. By expressing the Chebyshev recurrence in terms of residuals rather than eigenvector estimates, R-ChFSI naturally accommodates multiple sources of approximation, including reduced-precision arithmetic (FP32 and TF32) in the filtering step, lossy compression with compression ratios exceeding 4x for inter-process communication, and approximate inverses for generalized eigenproblems, while preserving eigensolver robustness. Large-scale experiments on GPU accelerators are conducted using finite-element discretized generalized eigenproblems arising in Kohn–Sham density functional theory for quantum modeling of materials. The results demonstrate that R-ChFSI achieves eigen-residual norms orders of magnitude smaller than standard ChFSI under comparable inexactness, while delivering substantial performance gains. This work provides a practical pathway toward approximation-tolerant eigensolvers enabling accurate and scalable simulations on modern computing architectures.
Bio:
Phani Motamarri is an Assistant Professor in the Department of Computational and Data Sciences at the Indian Institute of Science, Bengaluru, where he leads the MATRIX Lab. He is an alumnus of the University of Michigan–Ann Arbor, where he earned his PhD in Mechanical Engineering.
His research lies at the intersection of computational mechanics, materials science, numerical analysis, and high-performance computing. His work focuses on developing mathematical techniques and hardware-aware algorithms for quantum modeling of materials, with applications to structural and functional materials and multiscale modeling methodologies. He is also interested in machine learning frameworks for accelerating materials discovery and quantum computing, particularly in the context of quantum-centric supercomputing.
Motamarri’s research contributions include advances in finite-element methods, numerical analysis, and large-scale scientific software development. He is one of the lead developers of DFT-FE, an open-source, massively parallel finite-element code for density functional theory calculations. He received the ACM Gordon Bell Prize in 2023 and was a finalist for the ACM Gordon Bell Prize in 2019.
Modern computing architectures increasingly rely on iterative solvers that employ reduced-precision computation and communication-reduction techniques to lower time-to-solution and improve scalability. However, eigensolvers in scientific simulations have struggled to exploit such approximations without compromising accuracy. We present an eigensolver R-ChFSI, a residual-based reformulation of Chebyshev Filtered Subspace Iteration (ChFSI) provably tolerant to inexact matrix–vector products. By expressing the Chebyshev recurrence in terms of residuals rather than eigenvector estimates, R-ChFSI naturally accommodates multiple sources of approximation, including reduced-precision arithmetic (FP32 and TF32) in the filtering step, lossy compression with compression ratios exceeding 4x for inter-process communication, and approximate inverses for generalized eigenproblems, while preserving eigensolver robustness. Large-scale experiments on GPU accelerators are conducted using finite-element discretized generalized eigenproblems arising in Kohn–Sham density functional theory for quantum modeling of materials. The results demonstrate that R-ChFSI achieves eigen-residual norms orders of magnitude smaller than standard ChFSI under comparable inexactness, while delivering substantial performance gains. This work provides a practical pathway toward approximation-tolerant eigensolvers enabling accurate and scalable simulations on modern computing architectures.
Bio:
Phani Motamarri is an Assistant Professor in the Department of Computational and Data Sciences at the Indian Institute of Science, Bengaluru, where he leads the MATRIX Lab. He is an alumnus of the University of Michigan–Ann Arbor, where he earned his PhD in Mechanical Engineering.
His research lies at the intersection of computational mechanics, materials science, numerical analysis, and high-performance computing. His work focuses on developing mathematical techniques and hardware-aware algorithms for quantum modeling of materials, with applications to structural and functional materials and multiscale modeling methodologies. He is also interested in machine learning frameworks for accelerating materials discovery and quantum computing, particularly in the context of quantum-centric supercomputing.
Motamarri’s research contributions include advances in finite-element methods, numerical analysis, and large-scale scientific software development. He is one of the lead developers of DFT-FE, an open-source, massively parallel finite-element code for density functional theory calculations. He received the ACM Gordon Bell Prize in 2023 and was a finalist for the ACM Gordon Bell Prize in 2019.
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