activity
20232026
most citedPIM-Opt: Demystifying Distributed Optimization Algorithms on a Real-World Processing-In-Memory System

1 citations · 1 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CR2026

HULK: An Efficient Algorithm for Enabling Fast Bulk Bitwise Operations on Homomorphically Encrypted Data

Mayank Kabra, Phillip Widdowson, Harshita Gupta +9

A wide range of important cloud applications, e.g., database analytics, graph analytics, and genome analysis, spend a considerable amount of time performing bulk bitwise operations…

cs.AR2026

CertiFlash: A Formal Verification Framework for Flash Translation Layers in Computational Solid State Drives

Harshita Gupta, Mayank Kabra, Rakesh Nadig +10

Data-intensive applications move large amounts of data from storage to the compute unit, incurring significant data movement overhead. Storage-centric computing reduces this overhe…

cs.CR2026

HE-PIM: Demystifying Homomorphic Operations on a Real-world Processing-in-Memory System

Harshita Gupta, Mayank Kabra, Jaewoo Park +9

Homomorphic encryption (HE) enables computation over encrypted data, offering strong privacy guarantees for untrusted computing environments. Practical adoption remains limited by…

cs.AR2026

Conduit: Programmer-Transparent Near-Data Processing Using Multiple Compute-Capable Resources in Solid State Drives

Rakesh Nadig, Vamanan Arulchelvan, Mayank Kabra +9

Solid-state drives (SSDs) are well suited for near-data processing (NDP) because they: (1) store large application datasets, and (2) support three NDP paradigms: in-storage process…

cs.CR2025

CIPHERMATCH: Accelerating Homomorphic Encryption-Based String Matching via Memory-Efficient Data Packing and In-Flash Processing

Mayank Kabra, Rakesh Nadig, Harshita Gupta +7

Homomorphic encryption (HE) allows secure computation on encrypted data without revealing the original data, providing significant benefits for privacy-sensitive applications. Many…

cs.AR2024★ 1 cited

PIM-Opt: Demystifying Distributed Optimization Algorithms on a Real-World Processing-In-Memory System

Steve Rhyner, Haocong Luo, Juan Gómez-Luna +6

Modern Machine Learning (ML) training on large-scale datasets is a very time-consuming workload. It relies on the optimization algorithm Stochastic Gradient Descent (SGD) due to it…

cs.CR2023

Evaluating Homomorphic Operations on a Real-World Processing-In-Memory System

Harshita Gupta, Mayank Kabra, Juan Gómez-Luna +2

Computing on encrypted data is a promising approach to reduce data security and privacy risks, with homomorphic encryption serving as a facilitator in achieving this goal. In this…