Sahil Vartak [Texas A&M University]
Sahil Vartak is a Master's student in Computer Engineering at Texas A&M University specializing in full-stack development and AI integration. As Lead Web Developer at Texas A&M High Performance Research Computing, he builds and maintains production software serving over 1,000 researchers daily. His work spans modern web applications, backend services, and machine learning pipelines. Previously, he developed ML-driven analytics dashboards at McDermott International and contributed to AI research published in PerCom 2025. Sahil is passionate about building reliable, scalable software and leveraging AI tools to accelerate development.
Sessions
As HPC expands beyond computer science, many users encounter command-line interfaces for the first time. HPCMosaic addresses this gap with a modular OOD dashboard that simplifies cluster management for non-technical researchers while staying customizable for experienced users. This talk presents a framework that reduces support burden by enabling self-service for common tasks (file quotas, account management, and job control) decreasing repetitive tickets. Targeted at HPC developers and cluster admins, this session covers modern dashboard development focused on customizability and user experience. Attendees will learn how to build accessible HPC interfaces that let researchers focus on their work instead of troubleshooting, reducing user frustration and support workload.
LAMMPS's complex, non-uniform GPU feature support creates a dilemma: users struggle to find a compatible build, leading to wasted time, admin overload, and underutilized GPUs (e.g., Intel Max). We present an intelligent, wizard-style utility that performs automatic three-way matchmaking between available LAMMPS software builds, GPU accelerators, and user input requirements. The utility suggests optimal, GPU-accelerated builds and clearly explains why others are incompatible. Built atop the Drona job launcher, the wizard uses dynamically repopulating menus to guide users through the selection process, and finally injects the full configuration directly into a ready-to-run Slurm job script.