R
Roshan Dathathri
Researcher at University of Texas at Austin
Publications - 35
Citations - 898
Roshan Dathathri is an academic researcher from University of Texas at Austin. The author has contributed to research in topics: Compiler & Graph (abstract data type). The author has an hindex of 14, co-authored 35 publications receiving 515 citations. Previous affiliations of Roshan Dathathri include Indian Institute of Science & University of Illinois at Urbana–Champaign.
Papers
More filters
Proceedings ArticleDOI
CHET: an optimizing compiler for fully-homomorphic neural-network inferencing
Roshan Dathathri,Olli Saarikivi,Hao Chen,Kim Laine,Kristin E. Lauter,Saeed Maleki,Madanlal Musuvathi,Todd Mytkowicz +7 more
TL;DR: CHET is a domain-specific optimizing compiler designed to make the task of programming FHE applications easier, and generates homomorphic circuits that outperform expert-tuned circuits and makes it easy to switch across different encryption schemes.
Proceedings ArticleDOI
Gluon: a communication-optimizing substrate for distributed heterogeneous graph analytics
Roshan Dathathri,Gurbinder Gill,Loc Hoang,Hoang-Vu Dang,Alex Brooks,Nikoli Dryden,Marc Snir,Keshav Pingali +7 more
TL;DR: This paper introduces a new approach to building distributed-memory graph analytics systems that exploits heterogeneity in processor types (CPU and GPU), partitioning policies, and programming models, and Gluon, a communication-optimizing substrate that enables these programs to run on heterogeneous clusters and optimizes communication in a novel way.
Proceedings ArticleDOI
EVA: an encrypted vector arithmetic language and compiler for efficient homomorphic computation
TL;DR: This paper presents a new FHE language called Encrypted Vector Arithmetic (EVA), which includes an optimizing compiler that generates correct and secure FHE programs, while hiding all the complexities of the target FHE scheme.
Proceedings ArticleDOI
EVA: An Encrypted Vector Arithmetic Language and Compiler for Efficient Homomorphic Computation
TL;DR: Encrypted Vector Arithmetic (EVA) as discussed by the authors is an FHE language that includes an optimizing compiler that generates correct and secure FHE programs, while hiding all the complexities of the target FHE scheme.
Journal ArticleDOI
Pangolin: an efficient and flexible graph mining system on CPU and GPU
TL;DR: Pangolin this paper is an efficient and flexible in-memory graph pattern mining (GPM) framework targeting shared-memory CPUs and GPUs that provides high-level abstractions for GPU processing.