Skill UI

This Java client library provides comprehensive SDK support for Azure Batch service. It enables developers to manage and execute large-scale, parallel, and high-performance computing (HPC) batch jobs in Azure. Users can programmatically manage compute pools, create structured jobs, schedule tasks, and monitor job lifecycle using Java.

This Java client library provides comprehensive SDK support for Azure Batch service. It enables developers to manage and execute large-scale, parallel, and high-performance computing (HPC) batch jobs in Azure. Users can programmatically manage compute pools, create structured jobs, schedule tasks, and monitor job lifecycle using Java.

Cloud-native Rowan orchestrates medicinal chemistry and molecular modeling pipelines via a Python API, offering pKa prediction, docking, descriptors, dynamics, and AI-driven structure workflows for scalable batch screening without local HPC.

Nextflow is a powerful workflow language and runtime for building reproducible, portable, and scalable data pipelines. It is dominant in bioinformatics, enabling complex dataflow programming and managing workflows across diverse environments, including local machines, HPC clusters (SLURM), and cloud platforms (AWS, Azure). This skill covers the full lifecycle: running established pipelines (like nf-core), developing custom modular workflows, configuring containerization, and ensuring computational reproducibility.

A comprehensive guide for computer science authors targeting high-performance parallel and distributed computing venues like HPDC. It assists in conference fit evaluation, manuscript re-framing (from journal/arXiv to conference narrative), identifying evidence gaps, and structuring contributions for optimal review success in cloud/HPC systems and distributed settings. Ideal for determining submission strategy.

A comprehensive guide for researchers preparing academic artifacts for voluntary, post-acceptance evaluation (e.g., HPCA). It outlines best practices for structuring submissions for cold-start evaluators, defining reproducibility tiers (full reproduction, regeneration from logs, inspection), and managing computational budgets to successfully earn IEEE reproducibility badges.

A comprehensive guide for authors responding to technical reviews and revising conference papers during a limited, combined rebuttal window. It teaches how to strategically triage objections, run decisive experiments, craft arguments for a program committee champion, and ensure minimal, faithful revisions to maximize acceptance chances.

This guide details the comprehensive process of transforming a reviewed, double-blind academic paper into a final, IEEE-published camera-ready manuscript and a conference talk. It covers crucial steps including de-anonymization, applying the official IEEE template and rights forms, integrating reproducibility badges, confirming DOIs, and structuring the presentation content for the winter slot.

A comprehensive guide for auditing and improving the scientific rigor of hardware architectural evaluations (e.g., HPCA style). It emphasizes establishing a clear 'fidelity contract' defining tools, scope, and configurations. It requires that all performance claims are backed by credible instruments, mandates capturing full machine state provenance, ensuring honest baseline tuning, and reporting results via per-workload distributions rather than single means.

A detailed guide on positioning a computer architecture paper within the broader academic literature. It teaches authors to write a 'lineage with structural deltas,' moving beyond mere citation lists. The guidelines cover sweeping multiple databases (like dblp and IEEE Xplore), identifying concrete structural improvements (deltas), and adhering to strict academic rules, such as citing all authors and maintaining un-blinded bibliography integrity.

This guide outlines the rigorous methodology for ensuring that computational research results, especially those involving hardware architecture and simulation, are fully reproducible. It emphasizes pinning the entire result chain—from simulator commits and configuration files to workloads, sampling recipes, and statistics scripts—and mandates creating a manifest for every figure. Key practices include recording machine state for silicon runs and containerizing the environment to prevent data drift.

This guide details the academic review process for HPCA, including the double-blind review format, the structure of the single combined rebuttal/revision window, and the dynamics of program-committee meetings. Use this to set clear expectations, predict outcomes, and strategically prepare your rebuttal to maximize your paper's chances of acceptance.