arXiv subjects

Nikolaos Tselepidis

Publications and source records attributed to Nikolaos Tselepidis.

3 recordsLinked to original sources

arXiv2026

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

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 APIs. For Fortran applications, language features such as 'do concurrent' loops open the door for compilers to implement multi-threaded, GPU-accelerated, and even distributed multi-node code with only the standard language. Here, we explore the current status of using 'do concurrent' for GPU-accelerated Fortran applications across three major GPU vendors (NVIDIA, AMD, and Intel). Using a production application, we test their current capabilities, showing where the standard language alone can be used, and where augmenting the code with a directive-based API (e.g., OpenMP) is still desirable or required. Multi-GPU tests are performed with GPU-aware MPI libraries. We find that the three GPU vendors can now GPU-accelerate pure Fortran (zero directives), but that manual data movement directives can help with performance and compatibility. The results show that there is rapid advancement towards making GPU-accelerated scientific HPC code performance portable using the Fortran standard language.

cs.PL↗

arXiv2025

Simulating many-engine spacecraft: Exceeding 1 quadrillion degrees of freedom via information geometric regularization

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 along 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 on well-conditioned numerics is used. 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 seen 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.

physics.comp-ph↗

arXiv2020

Two-Level K-FAC Preconditioning for Deep Learning

In the context of deep learning, many optimization methods use gradient covariance information in order to accelerate the convergence of Stochastic Gradient Descent. In particular, starting with Adagrad, a seemingly endless line of research advocates the use of diagonal approximations of the so-called empirical Fisher matrix in stochastic gradient-based algorithms, with the most prominent one arguably being Adam. However, in recent years, several works cast doubt on the theoretical basis of preconditioning with the empirical Fisher matrix, and it has been shown that more sophisticated approximations of the actual Fisher matrix more closely resemble the theoretically well-motivated Natural Gradient Descent. One particularly successful variant of such methods is the so-called K-FAC optimizer, which uses a Kronecker-factored block-diagonal Fisher approximation as preconditioner. In this work, drawing inspiration from two-level domain decomposition methods used as preconditioners in the field of scientific computing, we extend K-FAC by enriching it with off-diagonal (i.e. global) curvature information in a computationally efficient way. We achieve this by adding a coarse-space correction term to the preconditioner, which captures the global Fisher information matrix at a coarser scale. We present a small set of experimental results suggesting improved convergence behaviour of our proposed method.

cs.LG↗