activity
20152026
most citedEnabling Homomorphically Encrypted Inference for Large DNN Models

31 citations · 40 across the 7 of their papers we have counts for

collaborators

7 papers

cs.PL2026

Portability of Fortran's 'do concurrent' on GPUs II

Ronald M. Caplan, Miko M. Stulajter, Jon A. Linker +6

There continues to be growing interest in using standard language constructs for parallel and accelerated HPC computing, avoiding the need for (sometimes vendor-specific) external…

cs.PF2026

Unified Shared Memory in OpenMP: Implementation, Programmability, and Performance on Intel Accelerators

Harald Servat, François Dugast, Alejandro Duran +2

OpenMP 5.0 introduced the Unified Shared Memory (USM) feature through the requires directive. The feature simplifies the adoption of the OpenMP programming model by providing a uni…

cs.AR2026

Performance and Energy Benefits of MRDIMMs

Pau Díaz, Mariana Carmin, Pouya Esmaili-Dokht +7

Multiplexed Rank DIMMs (MRDIMMs) have recently emerged as memory devices that enable higher bandwidth without increasing DRAM chip frequencies. This paper presents a detailed perfo…

cs.CR2021★ 31 cited

Enabling Homomorphically Encrypted Inference for Large DNN Models

Guillermo Lloret-Talavera, Marc Jorda, Harald Servat +5

The proliferation of machine learning services in the last few years has raised data privacy concerns. Homomorphic encryption (HE) enables inference using encrypted data but it inc…

cs.PF2020★ 3 cited

Understanding Memory Access Patterns Using the BSC Performance Tools

Harald Servat, Jesús Labarta, Hans-Christian Hoppe +2

The growing gap between processor and memory speeds results in complex memory hierarchies as processors evolve to mitigate such divergence by taking advantage of the locality of re…

cs.CV2019

MetH: A family of high-resolution and variable-shape image challenges

Ferran Parés, Dario Garcia-Gasulla, Harald Servat +2

High-resolution and variable-shape images have not yet been properly addressed by the AI community. The approach of down-sampling data often used with convolutional neural networks…

cs.PF2015★ 6 cited

Solving the Klein-Gordon equation using Fourier spectral methods: A benchmark test for computer performance

S. Aseeri, O. Batrašev, M. Icardi +12

The cubic Klein-Gordon equation is a simple but non-trivial partial differential equation whose numerical solution has the main building blocks required for the solution of many ot…