“accelerator”的搜索结果

Statically analyze a Simulink model for Rapid Accelerator (raccel) mode incompatibilities. Identifies unsupported blocks (Interpreted MATLAB Function, Level-2 S-Functions without TLC), configuration issues, and data type restrictions that prevent code generation. Classifies each issue as BLOCKER (build error) or WARNING (build warning). Use BEFORE calling sim() in rapid-accelerator mode, when buildRapidAcceleratorTarget fails, or when the user asks "why won't my model run in raccel?" TRIGGER: any request explicitly involving rapid accelerator mode, raccel, raccel build errors, or buildRapidAcceleratorTarget failures.

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用于创建和更新投资者演示材料、财务模型、资金用途表及申请文件，并核对多份材料中的业务业绩、收入假设、融资金额和团队信息是否一致。

• 创建或修改投资者演示材料
• 编写投资者概要或备忘录
• 制作收入预测和资金用途表

When the user wants to apply to startup accelerators, incubators, or fellowship programs. Also use when the user mentions "YC application", "Techstars", "accelerator", or "apply to programs".

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Think and work like an expert Accelerator Physicist. Use when a task calls for Accelerator Physicist judgment. Reasons from beam optics, RF cavities, emittance budgets, and loss maps while treating halo and impedance-driven instabilities as first-class failure modes.

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Write accelerator applications. TRIGGERS - Use when user needs help with accelerator-application related tasks.

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Accelerator Application Specialist. Use to shortlist startup programs, draft applications, prepare founder videos, and rehearse accelerator interviews with a disciplined founder narrative.

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Write accelerator applications. TRIGGERS - Use when user needs help with accelerator-application related tasks.

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Build an HTML fundraising presentation for accelerator partners and angel investors. Organize the opening hook, traction, competitive advantage, and funding request into slides using a dedicated template, with speaker notes and presentation controls.

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• Prepare a Demo Day fundraising presentation
• Explain traction and competitive advantage to investors
• Build an HTML deck with speaker notes

Review Zoom meetings, calls, recordings, and transcripts to catch up on discussions and extract decisions, action items, and owners. Use Zoom's official connections to manage meetings and access supported chat, document, task, and whiteboard features.

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• Catch up on a missed meeting from recordings or transcripts
• Extract decisions, action items, and owners
• Create, update, or delete Zoom meetings

Inspect CPU, memory, disk, and accelerator information alongside container and scheduler limits. Produce a JSON snapshot with identifying details removed and conservative worker and memory estimates for planning local workloads, without running stress tests.

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• Choosing worker counts before a large local job
• Checking CPU and memory limits inside a container
• Comparing resource snapshots before and after changes

Finds the base image needed to create a custom GKE node image. It translates requirements such as GKE version, operating system, processor architecture, accelerators, and sandbox settings into a matching image name.

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• Finding a base image for custom GKE nodes
• Looking up images for GPU or TPU configurations
• Matching images to COS, Ubuntu, ARM64, or X86_64

Use this skill when the user is doing hands-on DOCA DPA host-side work on a BlueField — creating the `doca_dpa` Core context, loading a DPACC-compiled DPA app image (`doca_dpa_app`), creating DPA threads, launching kernels via `doca_dpa_kernel_launch_update_*`, draining `doca_dpa_completion`, running `doca_dpa_cap_*` discovery, choosing between the DPA comm component (inter-DPA messaging) and the DPA verbs component (in-kernel RDMA), or debugging `DOCA_ERROR_*` from `doca_dpa_*`. Trigger even without "DOCA DPA" or "Data-Path Accelerator": "run compute on the DPA from my host", "DPA kernel hangs, no completion", "DOCA_ERROR_DRIVER on launch", "DOCA/DPACC version skew", or "does this BlueField expose a DPA". Route elsewhere for DPA-side kernel programming itself, DPACC compiler internals, host↔DPU messaging (doca-comch), host-side RDMA (doca-rdma), and GPU-initiated networking (doca-gpunetio).

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Expert-level particle physics knowledge. Use when working with the Standard Model, quarks, leptons, bosons, fundamental forces, Feynman diagrams, quantum field theory, particle accelerators, or particle detectors. Also use when the user mentions 'Standard Model', 'quark', 'lepton', 'boson', 'Higgs', 'gluon', 'neutrino', 'antimatter', 'Feynman diagram', 'QCD', 'QED', 'electroweak', 'LHC', or 'particle accelerator'.

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Add a Keyboard Shortcuts help window (Ctrl+question) listing all app accelerators, plus the standard GNOME accelerator set. Use when wiring keyboard shortcuts or the Keyboard Shortcuts menu item.

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帮助撰写融资所需的投资者首封邮件、引荐请求和会后跟进，结合对方投资方向与具体业务成果，形成简洁且下一步行动明确的文案。

• 撰写发给投资者的首封邮件
• 附可转发简介的引荐请求
• 规划未获回复时的跟进

分阶段排查大量数据导入、历史补录、转换和同步的耗时，比较处理方法并加速，在执行后核对文件数、行数与时间戳的一致性，形成可重复使用的流程。

• 加速大量数据导入与导出
• 缩短历史数据补录时间
• 比较批次大小与并行任务数

Adapt existing PyTorch training scripts to use multiple GPUs or machines with limited code changes. The skill guides configuration, launch commands, mixed precision, which uses lower-precision numbers, and DeepSpeed or FSDP settings for distributing training work and memory.

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• Move a single-GPU training script to multiple GPUs
• Configure mixed precision training
• Set up DeepSpeed or FSDP to reduce memory use

Reduce LLM weight usage through pruning, which removes selected model parameters. Explore Wanda and SparseGPT for compression without retraining, choose hardware-friendly patterns for faster inference, and compare the compressed model's accuracy with the original.

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• Compressing an LLM without retraining
• Comparing pruning methods
• Exploring faster inference on supported hardware

Adapt PyTorch training scripts for multiple GPUs or machines with Accelerate. This skill guides code changes, configuration, and launch commands, including mixed precision, which combines numerical formats, and DeepSpeed or FSDP integration for distributed training.

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• Moving single-GPU training to multiple GPUs
• Configuring mixed precision training
• Setting up DeepSpeed or FSDP

Investigate slow TPU training on GKE by comparing diagnostic events with one-minute monitoring metrics. Separate memory or CPU bottlenecks from hardware and network issues, and identify the appropriate next steps.

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• Investigating slower TPU training without pod crashes
• Checking PERFORMANCE_DEGRADATION events
• Separating resource bottlenecks from infrastructure issues

Configure GKE ComputeClasses, which define preferred computing resources for workloads. Set fallback options between Spot and on-demand VMs, target GPUs or machine families, control class usage, and investigate pods waiting for suitable nodes.

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• Configuring fallback from Spot VMs to on-demand capacity
• Selecting GPUs, TPUs, or machine families
• Restricting ComputeClass editing and workload usage

Plan and troubleshoot large language and vision model training and inference. This guide uses hardware measurements to identify bottlenecks, address memory shortages and unstable training, and improve checkpointing, recovery, and performance across multiple GPUs and machines.

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• Diagnosing slow training and GPU memory shortages
• Investigating loss spikes and invalid numerical values
• Debugging communication hangs across GPUs and machines

Guides LLM compression through pruning, which removes selected model weights. Covers Wanda and SparseGPT methods without retraining, accuracy comparisons, and structured weight patterns that can accelerate inference on compatible hardware.

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• Trying model compression without retraining
• Comparing accuracy before and after pruning
• Choosing between Wanda and SparseGPT

Compare computing devices for a defined AI model workload using memory capacity, computing speed, data transfer, energy use, and cost. Build a comparison that separates predicted performance from measurements and identifies the constraints that determine the choice.

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• Choosing hardware for a specific AI model
• Checking whether a model fits in device memory
• Explaining gaps between advertised and measured performance