2 citations · 2 across the 6 of their papers we have counts for
8 papers
Product-Aware Deterministic Rounding for Quantized Matrix Multiplication
Piyush Sao, Narasinga Miniskar, Pedro Valero-Lara +2
Scalar rounding decisions interact through matrix multiplication. We study deterministic product-aware rounding after scales, clipping bounds, and grids are fixed, with each active…
A Nuclear-Norm Lower Bound for Dithered Scalar Quantization of Matrix Products
Piyush Sao, Narasinga Miniskar, Pedro Valero-Lara +2
We consider the problem of minimizing error in quantized matrix multiplication . Scalar quantization of the factors introduces rounding errors whose scale depends on the maxi…
Report of the 2026 Workshop on Next-Generation Ecosystems for Scientific Computing: Harnessing Community, Software, and AI for Cross-Disciplinary Team Science
Lois Curfman McInnes, Dorian Arnold, Prasanna Balaprakash +45
Scientific computing is undergoing rapid transformation as advances in artificial intelligence, heterogeneous computing, automation, and data-intensive research reshape not only co…
A Second-Moment Theory for Floating-Point Reduction Trees
Piyush Sao, Narasinga Miniskar, Pedro Valero-Lara +2
Summation error depends on partial-sum order, which standard worst-case bounds omit. To capture this dependence, we derive an exact mean-square error (MSE) recurrence for a binary…
Contraction-Gauge Preconditioning for Quantized Matrix Multiplication
Piyush Sao, Narasinga Miniskar, Pedro Valero-Lara +2
We study low-precision computation of C=AB with both factors quantized. We derive an exact finite-dimensional identity for the expected squared product error under independent, zer…
LLM-powered reasoning in agent-based modeling
Sifat Afroj Moon, Dakotah Maguire, Adam Spannaus +5
Agent-based modeling (ABM) has the capability to model millions of individuals and their interactions, which is useful for policy making. However, ABMs have traditionally relied on…
A Parallel Alternative for Energy-Efficient Neural Network Training and Inferencing
Sudip K. Seal, Maksudul Alam, Jorge Ramirez +2
Energy efficiency of training and inferencing with large neural network models is a critical challenge facing the future of sustainable large-scale machine learning workloads. This…
An Active Learning-Based Streaming Pipeline for Reduced Data Training of Structure Finding Models in Neutron Diffractometry
Tianle Wang, Jorge Ramirez, Cristina Garcia-Cardona +3
Structure determination workloads in neutron diffractometry are computationally expensive and routinely require several hours to many days to determine the structure of a material…