arXiv subjects

Alejandro Duran

Publications and source records attributed to Alejandro Duran.

3 recordsLinked to original sources

arXiv2026

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

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 unique and common address space between the accelerators and the host and allowing the access (dereference) of the same memory address on different devices, thus avoiding the burden of explicit data transfers to maintain the consistency between the address spaces. Hence, the feature eases quick prototyping and porting of applications to OpenMP with accelerators. In this paper, we introduce the Intel implementation for USM. We briefly discuss its implementation in the software stack (OS kernel, compiler, and runtime), then assess its adoption complexity in existing HPC applications using OpenMP for accelerators, and, finally, evaluate the performance of these applications when adopting USM on an Intel Battlemage GPU. USM is not expected to grant performance uplifts to already optimized applications with explicit, granular data-motion control and our results show an overhead with a geometric mean below 1.2x (1.03x seems achievable with further optimizations). Yet, in this paper we show there exist applications that benefit from this feature, making it attractive even for already ported applications.

cs.PF↗

arXiv2022

GRChombo: An adaptable numerical relativity code for fundamental physics

GRChombo is an open-source code for performing Numerical Relativity time evolutions, built on top of the publicly available Chombo software for the solution of PDEs. Whilst GRChombo uses standard techniques in NR, it focusses on applications in theoretical physics where adaptability, both in terms of grid structure, and in terms of code modification, are key drivers.

gr-qc↗

arXiv2019

Raising the Performance of the Tinker-HP Molecular Modeling Package [Article v1.0]

This living paper reviews the present High Performance Computing (HPC) capabilities of the Tinker-HP molecular modeling package. We focus here on the reference, double precision, massively parallel molecular dynamics engine present in Tinker-HP and dedicated to perform large scale simulations. We show how it can be adapted to recent Intel Central Processing Unit (CPU) petascale architectures. First, we discuss the new set of Intel Advanced Vector Extensions 512 (Intel AVX-512) instructions present in recent Intel processors (e.g., the Intel Xeon Scalable and Intel Xeon Phi 2nd generation processors) allowing for larger vectorization enhancements. These instructions constitute the central source of potential computational gains when using the latest processors, justifying important vectorization efforts for developers. We then briefly review the organization of the Tinker-HP code and identify the computational hotspots which require Intel AVX-512 optimization and we propose a general and optimal strategy to vectorize those particular parts of the code. We intended to present our optimization strategy in a pedagogical way so it could benefit to other researchers and students interested in gaining performances in their own software. Finally we present the performance enhancements obtained compared to the unoptimized code both sequentially and at the scaling limit in parallel for classical non-polarizable (CHARMM) and polarizable force fields (AMOEBA). This paper never ceases to be updated as we accumulate new data on the associated Github repository between new versions of this living paper.

cs.MS↗