Publications
Showing 88 results for Author: Mallikarjun (Arjun) Shankar
May, 2026
Journal
Data readiness pipeline patterns for scientific AI at scale: Insights from climate, fusion, life sciences, and materials
This article examines how data readiness for AI principles apply to large scientific datasets used to train foundation models. We analyze archetypal workflows across four representative domains—climate, nuclear fusion, life sciences, and materials—to identify common preprocessing patterns and domain-specific constraints. We introduce a two-dimensional readiness model that…
May, 2026
Conference Paper
Data Readiness for Scientific AI at Scale
This paper examines how Data Readiness for AI (DRAI) principles apply to leadership-scale scientific datasets used to train foundation models. We analyze archetypal workflows across four representative domains—climate, nuclear fusion, bio/health, and materials—to identify common preprocessing patterns and domain-specific constraints. We introduce a two-dimensional readines…
Apr, 2026
Journal
Mixed-precision numerics in scientific applications: survey and perspectives
The explosive demand for artificial intelligence (AI) workloads has led to a significant increase in silicon area dedicated to lower-precision computations on recent high-performance computing hardware designs. However, mixed-precision capabilities, which can achieve performance improvements of up to 8 compared to double-precision in extreme compute-intensive workloads, re…
Mar, 2026
Conference Paper
RingX: Scalable Parallel Attention for Long-Context Learning on HPC
The attention mechanism has become foundational for remarkable AI breakthroughs since the introduction of the Transformer, driving the demand for increasingly longer context to power frontier models such as large-scale reasoning language models and high-resolution image/video generators. However, its quadratic computational and memory complexities present substantial chall…
Mar, 2026
Conference Paper
Enabling Seamless Transitions from Experimental to Production HPC for Interactive Workflows
The evolving landscape of scientific computing requires seamless transitions from experimental to production HPC environments for interactive workflows. This paper presents a structured transition pathway developed at OLCF that bridges the gap between development testbeds and production systems. We address both technological and policy challenges, introducing frameworks fo…
Mar, 2026
Conference Paper
A Survey on Next-generation Power Grid Data Architecture
The operation and control of power grids will increasingly rely on data. A high-speed, reliable, flexible and secure data architecture is the prerequisite of the next-generation power grid. This paper summarizes the challenges in collecting and utilizing power grid data, and then provides reference data architecture for future power grids. Based on the data architecture de…
Nov, 2024
ORNL Report
OLCF’s Advanced Computing Ecosystem (ACE): FY24 Efforts for the DOE Integrated Research Infrastructure (IRI) Program
This report highlights significant strides made by Oak Ridge National Laboratory’s Oak Ridge Leadership Computing Facility (OLCF) in advancing computational research and infrastructure. Through the Advanced Computing Ecosystem (ACE) strategic initiative, OLCF has been successfully integrated with DOE’s Integrated Research Infrastructure (IRI) program, establishing itself a…