arXiv subjects

Ahmad Tarraf

Publications and source records attributed to Ahmad Tarraf.

3 recordsLinked to original sources

arXiv2025

When AI Bends Metal: AI-Assisted Optimization of Design Parameters in Sheet Metal Forming

Numerical simulations have revolutionized the industrial design process by reducing prototyping costs, design iterations, and enabling product engineers to explore the design space more efficiently. However, the growing scale of simulations demands substantial expert knowledge, computational resources, and time. A key challenge is identifying input parameters that yield optimal results, as iterative simulations are costly and can have a large environmental impact. This paper presents an AI-assisted workflow that reduces expert involvement in parameter optimization through the use of Bayesian optimization. Furthermore, we present an active learning variant of the approach, assisting the expert if desired. A deep learning model provides an initial parameter estimate, from which the optimization cycle iteratively refines the design until a termination condition (e.g.,energy budget or iteration limit) is met. We demonstrate our approach, based on a sheet metal forming process, and show how it enables us to accelerate the exploration of the design space while reducing the need for expert involvement.

cs.AI↗

arXiv2025

Denoising Application Performance Models with Noise-Resilient Priors

As parallel codes are scaled to larger computing systems, performance models play a crucial role in identifying potential bottlenecks. However, constructing these models analytically is often challenging. Empirical models based on performance measurements provide a practical alternative, but measurements on high-performance computing (HPC) systems are frequently affected by noise, which can lead to misleading predictions. To mitigate the impact of noise, we introduce application-specific dynamic priors into the modeling process. These priors are derived from noise-resilient measurements of computational effort, combined with domain knowledge about common algorithms used in communication routines. By incorporating these priors, we effectively constrain the model's search space, eliminating complexity classes that capture noise rather than true performance characteristics. This approach keeps the models closely aligned with theoretical expectations and substantially enhances their predictive accuracy. Moreover, it reduces experimental overhead by cutting the number of repeated measurements by half.

cs.PF↗

arXiv2024

Capturing Periodic I/O Using Frequency Techniques

Many HPC applications perform their I/O in bursts that follow a periodic pattern. This allows for making predictions as to when a burst occurs. System providers can take advantage of such knowledge to reduce file-system contention by actively scheduling I/O bandwidth. The effectiveness of this approach, however, depends on the ability to detect and quantify the periodicity of I/O patterns online. In this paper, we introduce FTIO, an online method to detect periodic I/O phases, which is based on discrete Fourier transform (DFT), combined with outlier detection. We provide metrics that gauge the confidence in the output and tell how far from being periodic the signal is. We validate our approach with large-scale experiments on a production system and examine its limitations extensively. Our experiments show that FTIO has a mean error below 11%. Finally, we demonstrate that FTIO allowed the I/O scheduler Set- 10 to boost system utilization by 26% and reduce I/O slowdown by 56%.

cs.DC↗