「distributed」の検索結果

Use when CPU usage is high, memory grows unboundedly, goroutines leak, mutex contention shows in traces, or escape analysis suggests excess heap allocation. Triggers: net/http/pprof endpoint exposure, go tool pprof analysis, flamegraph generation, allocs vs inuse_space heap profiles, runtime/trace event timeline, mutex profiling, block profiling, goroutine dumps, GC pressure measurement. NOT for non-Go languages, distributed tracing (use OpenTelemetry skill), or production telemetry pipelines.

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Building resilient distributed systems with circuit breakers, retries with full-jitter exponential backoff, retry budgets (per-request 3-attempt + per-client 10% ratio per Google SRE), deadline propagation, and the cascading-failure math (4 layers × 3 retries = 64x amplification). Grounded in Resilience4j, Microsoft Cloud Patterns, AWS Architecture Blog (Marc Brooker), and Google SRE Book.

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Choose multi-agent architectures using environment-first analysis, coordination pressure, commitment tuning, and knowledge levels. Use for autonomy design, protocol choice, and coordination failures. NOT for single-agent planning, centralized schedulers, or prompt-only swarms.

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Foundational concurrent computation model where actors communicate exclusively through asynchronous message passing

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Framework RLHF de alta performance com aceleração Ray+vLLM. Use para treinamento PPO, GRPO, RLOO, DPO de modelos grandes (7B-70B+). Construído em Ray, vLLM, ZeRO-3. 2× mais rápido que DeepSpeedChat com arquitetura distribuída e compartilhamento de recursos GPU.

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Fornece orientação para treinar LLMs com aprendizado por reforço usando verl (Volcano Engine RL). Use ao implementar RLHF, GRPO, PPO ou outros algoritmos de RL para pós-treinamento de LLMs em escala com backends de infraestrutura flexíveis.

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Mecanismo de busca de similaridade vetorial de alto desempenho para RAG e busca semântica. Use ao construir sistemas RAG em produção que exigem busca de vizinhos mais próximos rápida, busca híbrida com filtragem ou armazenamento vetorial escalável com desempenho impulsionado por Rust.

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Framework PyTorch de alto nível com classe Trainer, treinamento distribuído automático (DDP/FSDP/DeepSpeed), sistema de callbacks e boilerplate mínimo. Escala de laptop para supercomputador com o mesmo código. Use quando quiser loops de treinamento limpos com boas práticas integradas.

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Orquestração de treinamento distribuído em clusters. Escala PyTorch/TensorFlow/HuggingFace do laptop para milhares de nós. Ajuste de hiperparâmetros integrado com Ray Tune, tolerância a falhas, escalabilidade elástica. Use ao treinar modelos massivos em múltiplas máquinas ou executar varreduras distribuídas de hiperparâmetros.

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Processamento escalável de dados para workloads de ML. Execução em streaming em CPU/GPU, suporta Parquet/CSV/JSON/imagens. Integra-se com Ray Train, PyTorch, TensorFlow. Escala de uma única máquina para centenas de nós. Use para inferência em lote, pré-processamento de dados, carregamento de dados multi-modal ou pipelines distribuídos de ETL.

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Treina modelos de linguagem grandes (2B-462B de parâmetros) usando NVIDIA Megatron-Core com estratégias avançadas de paralelismo. Use ao treinar modelos >1B de parâmetros, precisar de máxima eficiência de GPU (47% MFU em H100), ou necessitar de paralelismo tensor/pipeline/sequência/contexto/expert. Framework pronto para produção usado em Nemotron, LLaMA, DeepSeek.

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API de treinamento distribuído mais simples. 4 linhas para adicionar suporte distribuído a qualquer script PyTorch. API unificada para DeepSpeed/FSDP/Megatron/DDP. Posicionamento automático de device, precisão mista (FP16/BF16/FP8). Config interativo, comando de launch único. Padrão do ecossistema HuggingFace.

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[Debugging] Use when analyzing or optimizing performance bottlenecks: database queries, N+1 fan-out, indexing, API latency, memory/GC, concurrency and pool saturation, algorithmic complexity (O(n²)), network/protocol round trips, frontend rendering and Core Web Vitals, caching, and distributed/resilience paths. Calibration constants and domain laws (latency ladder, Little's Law, utilization knee, CWV thresholds, symptom→cause triage) live in references/performance-knowledge.md.

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[Architecture] Use when grading project architecture and scalability quality for greenfield init or brownfield audit: build/CI scalability, distributed-monolith risk, module isolation, dependency discipline, loose coupling, horizontal scaling, DRY, abstraction, clean architecture, observability, and delivery.

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Senior System Architect for SaaS specifications. Security-first design with ISO 27001 compliance. Use for system architecture, tech specs, API design, technology choices, distributed systems, cloud infrastructure.

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Azure Cosmos DB SDK for Python (NoSQL API). Use for document CRUD, queries, containers, and globally distributed data. Triggers: "cosmos db", "CosmosClient", "container", "document", "NoSQL", "partition key".

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Detect anomalies in DNP3 (Distributed Network Protocol 3) communications used in SCADA systems by monitoring for unauthorized control commands, firmware update attempts, protocol violations, and deviations from baseline traffic patterns using deep packet inspection and machine learning approaches.

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Track and correlate events across multiple timezones with automatic conversion. Use when analyzing distributed system incidents, coordinating cross-regional operations, or creating time-normalized reports from logs/events spanning PST/CST/EST/JST or other timezones. Handles daylight saving transitions automatically.

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Use when designing, implementing, reviewing, debugging, testing, profiling, or porting concurrent, parallel, distributed, high-performance, network dataplane, kernel-bypass, or real-time software, including locks, semaphores, monitors, MPI, CSP rendezvous, DPDK, VPP, eBPF, XDP, AF_XDP, SR-IOV, RSS, RoCEv2/RDMA, RMS/EDF, priority inversion, and environment selection from silicon to edge.

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JaegerとTempoによる分散トレーシングを実装して、マイクロサービス間のリクエストを追跡し、パフォーマンスボトルネックを特定します。マイクロサービスのデバッグ、リクエストフローの分析、分散システムの可観測性実装時に使用します。

Design microservices architectures with service boundaries, event-driven communication, and resilience patterns. Use when building distributed systems, decomposing monoliths, or implementing microservices.

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Build GitLab CI/CD pipelines with multi-stage workflows, caching, and distributed runners for scalable automation. Use when implementing GitLab CI/CD, optimizing pipeline performance, or setting up automated testing and deployment.

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Expert backend architect specializing in scalable API design, microservices architecture, and distributed systems.

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Instrument applications with Azure Monitor and OpenTelemetry for JavaScript (@azure/monitor-opentelemetry). Use when adding distributed tracing, metrics, and logs to Node.js applications with Appli...

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