'parallel' 검색 결과

Coordinate multiple coding agents through Superset in separate workspaces. Split work into bounded tasks, track dependencies and progress, send follow-ups, and collect structured results covering changed files, verification checks, blockers, and the order for combining work.

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• Splitting independent coding tasks across agents
• Passing completed work to dependent tasks
• Monitoring workers on local or remote hosts

Review how you use Superset and identify unused features that fit your workflow. Get a short list of suggestions, learn about each option, and set up selected features such as scheduled agents, parallel workspaces, or shared tasks.

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• Find unused Superset features worth exploring
• Set up scheduled agent tasks
• Start parallel work in separate workspaces

Implement and troubleshoot large-model training with DeepSpeed across multiple GPUs. The skill covers memory optimization, parallel training, and numerical precision choices, alongside examples for storage transfers and models that divide work among specialized components.

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• Review settings for distributed model training
• Tune transfers between NVMe storage and tensors
• Configure training with specialized model components

Split independent research or data-entry tasks among parallel workers. Provides a workflow for tracking completion in a table, testing one task first, and using a Python playbook to retry unfinished work and resume interrupted runs.

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• Researching multiple competitors in parallel
• Resuming unfinished tasks after an interruption
• Dividing data collection by region or time period

Run independent development tasks in parallel, each with a separate git worktree and implementation agent. Each agent opens its own pull request, which receives cross-review before being handed to a human for review and merging.

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• Implementing independent changes in parallel
• Assigning separate working directories to agents
• Preparing an individual pull request for each task

Coordinate temporary agents for code exploration, independent implementation tasks, verification, reviews, or complex debugging. Define self-contained assignments, run independent work in parallel, and check returned evidence before combining the results.

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• Delegating codebase exploration to inform a decision
• Running independent implementation tasks in parallel
• Getting an independent check of a high-risk change

Search the web and specialized subject areas, run multiple searches in parallel, and extract page text from URLs. Use the bundled command-line tools to gather current information, check claims, and read beyond search snippets.

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• Researching current news and documentation
• Checking claims against multiple sources
• Searching specialized finance or academic sources

Carry out a validated development plan in an isolated Git worktree, a separate working directory. Coordinate parallel implementation, run quality checks, update documentation, and take the changes through pull request creation, merging, execution metrics, and cleanup.

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• Implementing a validated development plan
• Running dependent tasks in parallel stages
• Preparing failing tests before implementation

Helps a QwenPaw Worker choose between direct work, short parallel checks, and temporary agents with custom roles. It guides dependent task execution, shared files, progress updates, result merging, and removal of temporary agents after the work ends.

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• Choosing how to split work within one Worker
• Running parallel reviews with specialized roles
• Executing tasks with dependencies

Convert .NET tests from xUnit.net v2 or v3 to MSTest v4 while keeping the existing test runner and target framework. Preserve assertions, shared setup, and parallel execution, then compare test counts and outcomes.

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• Converting xUnit tests to MSTest v4
• Preserving shared setup and parallel execution
• Checking test counts and outcomes after migration

Prepare research papers for RAG, a method that supplies retrieved documents to AI. Download PDFs, extract and split text, and build a local searchable database, with parallel processing for document batches.

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• Build a RAG document collection from selected research papers
• Download and extract text from batches of paper PDFs
• Create a local vector database without paid embedding APIs

Generate a Stata .do file that compares every subset of optional control variables across parallel instances. The script ranks combinations by the focal variable’s absolute t-statistic, reports the top ten, and fits the highest-ranked model.

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• Comparing all subsets of optional control variables
• Keeping mandatory controls in every regression
• Preparing parallel regression runs in Stata

Codex-only team orchestration: run a named team of cooperating Codex workers with durable, script-managed state. MUST USE when the user asks Codex to create, run, coordinate, inspect, archive, or delete a team of agents/threads/sessions, or to work on something as a team in parallel. FIRST inspects the active tool surface (checking tool_search for deferred tools) and tells the user the route: native MultiAgentV2 agents (flat spawn_agent with task_name) when available, Codex App threads as the fallback, or a plain-subagent split when neither set exists. The main session is always the leader; members are defined by a concrete part, ownership area, or perspective - never a vague job role; a bundled cross-platform script writes the .omo/teams state plus an auto-generated member field manual. Use a team when the work is not perfectly isolated but parallelizing helps; use plain subagents when scope is perfectly isolated or the goal is ambiguous. Triggers: team mode, teammode, make a team, run as a team, team of agents, coordinate threads, parallel Codex threads, archive the team.

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Parallel/distributed computing. Scale pandas/NumPy beyond memory, parallel DataFrames/Arrays, multi-file processing, task graphs, for larger-than-RAM datasets and parallel workflows.

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Operational guide for choosing and combining parallelism strategies in Megatron Bridge, including sizing rules, hardware topology mapping, and combined parallelism configuration.

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Validate and use MoE expert-parallel communication overlap in Megatron-Bridge, including overlap_moe_expert_parallel_comm, delay_wgrad_compute, and flex dispatcher backends such as DeepEP and HybridEP.

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Coordinates parallel investigation threads to simultaneously explore multiple hypotheses or root causes across different system areas. Use when debugging production incidents, slow API performance, multi-system integration failures, or complex bugs where the root cause is unclear and multiple plausible theories exist; when serial troubleshooting is too slow; or when multiple investigators can divide root-cause analysis work. Provides structured phases for problem decomposition, thread assignment, sync points with Continue/Pivot/Converge decisions, and final report synthesis.

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Patterns for parallel subagent execution using the Agent tool (formerly Task). Use when coordinating multiple independent tasks, spawning dynamic subagents, or implementing features that can be parallelized.

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Use this skill to run multiple Simulink simulations in parallel with parsim, batchsim, and DesignStudy. TRIGGER when: user asks for parameter sweep, sweeping/varying parameters across simulations, Monte Carlo study, batch simulation, running multiple simulations, parallel simulations, large-scale multiple simulations, any task involving repeated simulations with different parameter values, user mentions having an existing script/function for parameter sweeps, or user wants to migrate from parfor/set_param/for-loop patterns to recommended workflows.

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Speed up local parfor, parfeval, or spmd by switching to a thread-based parallel pool. Trigger when a user describes slow or disappointing local parallel performance, even if they don't mention threads. Symptoms: parfor on a laptop/workstation is slower than expected or "only slightly faster than for"; parfor scales poorly with the number of workers; ticBytes/tocBytes, the Parallel Pool dashboard, mpiprofile, or system tools show large per-worker data transfer; large broadcast variables or sliced inputs make iterations slow; opening a process pool dominates a short workload; user mentions serialisation or data transfer overhead. Also trigger on any question about whether code or a function works on a thread pool. For non-pool MATLAB performance work (vectorisation, preallocation, profiling), defer to matlab-optimize-performance.

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Decision protocol for the map-reduce / dynamic fan-out pattern in LM pipelines — "given list L, run f(item) for each item in parallel, then combine". Activates when the coder agent is about to process N items with N LM calls (per-doc summarize, per-query retrieve, per-candidate rank, parallel tool fan-out). Encodes the *when*, *how many at once*, *what to do when one fails*, and *how to reduce* — not the API of any single framework. Cross-framework: LangGraph `Send`, CrewAI parallel tasks / Flow, `asyncio.gather`, `ThreadPoolExecutor`, LlamaIndex batch retrieval.

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Tracks per-agent token usage and flags waste in parallel dispatch. Use when evaluating parallel agent efficiency or after a multi-agent run.

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Parallel multi-AI cross-validation research workflow (大版本). Dispatch N internal sub-agents + grok + gemini in parallel, automatically cross-validate findings, tier by confidence (strong consensus / partial / conflict / insufficient), generate tiered action items with arbitration. Use when user says "多 AI 调研", "交叉验证", "独立共识", "三脑调研", "multi-ai research", "parallel research", "cross-validate", or needs deep research that benefits from internal data + external 2026 consensus. NOT for quick factual Q&A, pure code reasoning, or tasks needing deep project context.

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Plan, implement, or review dispatching parallel agents work in an existing codebase with compatibility, security, and verification controls. Use when the user explicitly requests dispatching parallel agents work.

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