financial modeling teams

Abstract: Exploiting discounted spot instances for HPC requires an application to change its resource allocation at runtime, shrinking ahead of an interruption and expanding onto replacement capacity. Existing elasticity mechanisms implement rescaling as a full process teardown followed by a cold restart at the new processor count, and the restart stage accounts for up to 95% of the total overhead, growing with node count and increasing substantially on GPUs due to CUDA context initialization. In this paper, we present a no-restart rescaling mechanism for the Charm++ runtime system in which surviving processes never exit: a rescaling operation consists of a membership negotiation with a lightweight external coordinator, reconciliation of the UCX communication endpoints with the new cluster view, and a return to the top of the runtime initialization path that preserves live application state. This reduces the cost of a rescaling operation, excluding the load balancing step that any rescaling model requires, from multiple seconds to 8--15ms on CPUs and 7--11ms on GPUs, at 4 to 32 instances. Because processes survive, GPU device state persists in place, eliminating the checkpointing daemons previously required for GPU elasticity, and a launcher-independent bootstrap mechanism removes the dependence on supervised process managers, which are incompatible with spot instance interruptions. Integrated with an existing spot instance management framework, the mechanism cuts the end-to-end overhead of eight simultaneous interruptions to 0.2% of runtime on CPUs and 0.6% on GPUs, and raises the rate at which a job can be rescaled below 1% overhead by a factor of seven.

Abstract: Automated operations research (OR) modeling requires LLMs to translate natural-language decision problems into correct mathematical programs. Existing methods can improve individual formulations, but they often solve problems in isolation, retaining little reusable experience and repeating similar formulation errors. Prior memory-based approaches store examples, thoughts, or insights as references, while OR modeling requires reusable formulation skills that transfer across problem narratives and guide concrete modeling decisions. We propose OptiSkill, a skill-augmented framework that builds a hierarchical and evolving SkillBank for LLM-based OR modeling. SkillBank stores solver-verified experience as reusable skills, with Global Strategies for problem-level formulation skeletons and Step Experiences for local error-prevention rules. It is further refined through stable batch-level test-time evolution, where candidate skills are incorporated only after validation. Experiments on eight OR modeling benchmarks show that OptiSkill improves formulation accuracy across LLM backbones, outperforms strong agentic baselines, and gains further by expanding SkillBank coverage and reliability. Code and data are available at https://github.com/rachhhhing/OptiSkill