Imlau Dagostini, J. (CSE) – Intent-Driven Orchestration for Scientific Computing

The growing complexity of high-performance computing (HPC) systems poses a fundamental challenge for domain scientists, whose primary objective is to obtain scientifically valid results rather than to optimize resource utilization. Modern leadership-class facilities combine heterogeneous CPUs, GPUs, and specialized accelerators across systems that simultaneously support traditional scientific simulations and AI-driven workloads. This creates a vast, machine-dependent configuration space that even experienced systems researchers find difficult to navigate. In practice, users must explicitly specify resources, node counts, and walltime estimates before submitting jobs to an orchestrator, resulting in iterative trial-and-error that wastes both human effort and compute resources.

This thesis proposes an intent-driven orchestration middleware for scientific computing, in which domain scientists express high-level computational goals rather than low-level resource parameters, and the system assumes responsibility for identifying configurations that satisfy those goals efficiently. This thesis proposal builds on a completed study of the computational performance of pangenome mapping, a representative workload of data-intensive pipelines increasingly common in modern science. We demonstrate that tailoring tuning parameters to specific inputs and architectures yields significant performance improvements while exposing the depth of the configuration search problem that motivates this thesis. We then present an in-progress user-aware, intent-driven middleware that uses surrogate models to aid this exploration and map high-level goals to suitable configurations. We end this presentation by proposing a cluster-aware orchestrator that enables existing HPC resource managers to support intent-aware decision-making.

Event Host: Jessica Imlau Dagostini, Ph.D. Student, Computer Science & Engineering

Advisor: Abel Souza

Zoom: https://ucsc.zoom.us/j/93851280425?pwd=v4ONi9N5UlfZmsMqiI4gSkxFXe0oaX.1

Passcode: 835985