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Anand Radhakrishnan

dblp:192/3111 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2025
0000-0001-5127-2741ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 1 · 1 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
1 paper
High-performance computing · 38% Memory systems · 38% Energy-efficient computing · 12%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
High-performance computing › scientific computing systems
computational fluid dynamics
0.912025
Simulating many-engine spacecraft: Exceeding 1 quadrillion degrees of freedom via information geometric regularization · SC 2025
Memory systems › virtual memory management
unified memory
0.912025
Simulating many-engine spacecraft: Exceeding 1 quadrillion degrees of freedom via information geometric regularization · SC 2025
Performance modeling and evaluation › parallel system performance
weak scaling
0.312025
Simulating many-engine spacecraft: Exceeding 1 quadrillion degrees of freedom via information geometric regularization · SC 2025

Methods — techniques the papers use, named apart from their topics

mixed-precision arithmetic · 0.9information geometric regularization · 0.9
YearPublicationVenuePosition
2025 Simulating many-engine spacecraft: Exceeding 1 quadrillion degrees of freedom via information geometric regularization
abstract
We present an optimized implementation of the recently proposed information geometric regularization (IGR) for unprecedented scale simulation of compressible fluid flows applied to multi-engine spacecraft boosters. We improve upon state-of-the-art computational fluid dynamics (CFD) techniques in terms of computational cost, memory footprint, and energy-to-solution metrics. Unified memory on coupled CPU–GPU or APU platforms increases problem size with negligible overhead. Mixed half/single-precision storage and computation are used on well-conditioned numerics. We simulate flow at 200 trillion grid points and 1 quadrillion degrees of freedom, exceeding the current record by a factor of 20. A factor of 4 wall-time speedup is achieved over optimized baselines. Ideal weak scaling is observed on OLCF Frontier, LLNL El Capitan, and CSCS Alps using the full systems. Strong scaling is near ideal at extreme conditions, including 80% efficiency on CSCS Alps with an 8 node baseline and stretching to the full system.
Benjamin Wilfong, Anand Radhakrishnan, Henry Le Berre, Daniel Vickers, Tanush Prathi, Nikolaos Tselepidis, Benedikt Dorschner, Reuben D. Budiardja, Brian Cornille, Stephen Abbott, Florian Schäfer 0001, Spencer H. Bryngelson
SC2