Tanu Malik

Talha Azaz, Raza Ahmad, Md Saiful Islam et al.

Notebooks provide an author-friendly environment for iterative development, modular execution, and easy sharing. Distributed workflows are increasingly being authored and executed in notebooks, yet sharing and reproducing them remains challenging. Even small code or parameter changes often force full end-to-end re-execution of the distributed workflow, limiting iterative development for such workloads. Current methods for improving notebook execution operate on single-node workflows, while optimization techniques for distributed workflows typically sacrifice reproducibility. We introduce NBRewind, a notebook kernel system for efficient, reproducible execution of distributed workflows in notebooks. NBRewind consists of two kernels--audit and repeat. The audit kernel performs incremental, cell-level checkpointing to avoid unnecessary re-runs; repeat reconstructs checkpoints and enables partial re-execution including notebook cells that manage distributed workflow. Both kernel methods are based on data-flow analysis across cells. We show how checkpoints and logs when packaged as part of standardized notebook specification improve sharing and reproducibility. Using real-world case studies we show that creating incremental checkpoints adds minimal overhead and enables portable, cross-site reproducibility of notebook-based distributed workflows on HPC systems.

Bhanu Prakash Vangala, Ali Adibifar, Ashish Gehani et al.

The rise of Large Language Models (LLMs) as coding agents promises to accelerate software development, but their impact on generated code reproducibility remains largely unexplored. This paper presents an empirical study investigating whether LLM-generated code can be executed successfully in a clean environment with only OS packages and using only the dependencies that the model specifies. We evaluate three state-of-the-art LLM coding agents (Claude Code, OpenAI Codex, and Gemini) across 300 projects generated from 100 standardized prompts in Python, JavaScript, and Java. We introduce a three-layer dependency framework (distinguishing between claimed, working, and runtime dependencies) to quantify execution reproducibility. Our results show that only 68.3% of projects execute out-of-the-box, with substantial variation across languages (Python 89.2%, Java 44.0%). We also find a 13.5 times average expansion from declared to actual runtime dependencies, revealing significant hidden dependencies.

Bhanu Prakash Vangala, Tanu Malik

Self-hosting large language models (LLMs) is increasingly appealing for organizations seeking privacy, cost control, and customization. Yet deploying and maintaining in-house models poses challenges in GPU utilization, workload routing, and reliability. We introduce Pick and Spin, a practical framework that makes self-hosted LLM orchestration scalable and economical. Built on Kubernetes, it integrates a unified Helm-based deployment system, adaptive scale-to-zero automation, and a hybrid routing module that balances cost, latency, and accuracy using both keyword heuristics and a lightweight DistilBERT classifier. We evaluate four models, Llama-3 (90B), Gemma-3 (27B), Qwen-3 (235B), and DeepSeek-R1 (685B) across eight public benchmark datasets, with five inference strategies, and two routing variants encompassing 31,019 prompts and 163,720 inference runs. Pick and Spin achieves up to 21.6% higher success rates, 30% lower latency, and 33% lower GPU cost per query compared with static deployments of the same models.