'cpu' 검색 결과

Debug Node.js from the terminal by pausing execution, inspecting variables, and tracing function calls. Use interactive commands or scripts to investigate failing tests, collect runtime state, and capture CPU profiles or memory snapshots.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Inspecting intermediate state in failing Node.js tests
• Tracing function calls and unexpected variable values
• Investigating stalled asynchronous processing

Analyze application and system performance to find bottlenecks, the points that slow processing. Use resource usage and performance data to plan improvements to resource allocation, load balancing, caching, and algorithms, then compare results after changes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating slow application processing
• Reviewing CPU and memory allocation
• Improving workload distribution across nodes

Optimize scientific Python workloads for NVIDIA GPUs by choosing suitable libraries and reducing bottlenecks. Compare outputs with the CPU version and measure complete processing time, including data transfers, to determine whether the GPU implementation provides a useful improvement.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Assess whether slow Python processing would benefit from a GPU
• Move NumPy or pandas workloads to suitable GPU libraries
• Validate GPU results against CPU outputs

Measure scripts, running processes, or whole systems to find performance bottlenecks. Select suitable tools, add measurement code when needed, and report CPU, GPU, memory, and communication findings with prioritized recommendations and a record of file changes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding slow functions in a Python script
• Investigating CPU and GPU memory usage
• Measuring communication overhead across multiple GPUs

Improve MNN's Vulkan backend by separating CPU scheduling bottlenecks from GPU computation costs. It guides operator and shader changes, new feature integration with compatibility fallbacks, and device testing to verify both correctness and end-to-end performance.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding CPU or GPU bottlenecks in Vulkan inference
• Optimizing a specific operator or shader
• Integrating cooperative matrix or subgroup features

Set up application monitoring with structured logs, performance metrics, and traces that follow requests across services. Build dashboards and alerts, run load tests, investigate CPU and memory bottlenecks, and estimate future infrastructure capacity.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Set up application dashboards and alerts
• Investigate production issues using logs, metrics, and traces
• Run load tests before traffic increases

Diagnose slow systems, crashes, and resource problems on servers, PCs, virtual machines, and containers. Quick checks identify affected components, while deeper analysis investigates causes and provides optimization advice or repair plans.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating a slow server or PC
• Finding causes of high CPU or low memory
• Checking disk space and network problems

Monitor agent and system performance through live metrics, resource tracking, bottleneck analysis, and anomaly detection. Track response times and availability against service targets, and prepare dashboard data and performance reports.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigate slow agent coordination
• Track CPU and memory usage
• Monitor response times against service targets

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

Find relevant Google Cloud monitoring metrics for a service or resource. Retrieves current metric definitions through the API, filters them by the requested concepts, and presents selected metrics with their names, data types, units, and monitored resources.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find CPU metrics for Compute Engine
• Identify Cloud Storage write throughput and request metrics
• Check metric data types and units

Use to lock/cap Jetson CPU/GPU/EMC clocks, toggle EMC/CPU DVFS, or change cpufreq governors by editing BPMP DTB and nvpower.sh pre-flash. Do NOT use for live tuning or nvpmodel edits.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when diagnosing NPU + PyTorch or LLM Serving Host CPU affinity, NUMA locality, cgroup/cpuset constraints, CPU range conflicts, PyTorch/runtime threading, DataLoader, tokenizer, scheduler, vLLM-Ascend, SGLang, TTFT, TPOT, tokens/s, QPS, or multi-rank/multi-worker CPU binding issues.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Create container-based hosted agents in Azure AI Foundry using Python and the Azure AI Projects SDK. Follow examples for resource settings, authentication, version management, and checks needed before treating a deployment as healthy.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating a hosted agent from a custom container image
• Configuring CPU, memory, and environment variables
• Listing and deleting agent versions

Design and improve Unity games that process large numbers of entities. Learn ECS, an approach that separates data from behavior, and apply Jobs and Burst patterns to organize parallel processing and address CPU performance bottlenecks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing Unity games with thousands of entities
• Converting object-oriented game logic to ECS
• Addressing CPU performance bottlenecks

Write and register SGLang unit and end-to-end tests, from choosing models and file locations to reusing server fixtures. Follow project rules for meaningful assertions, CI registration, dependency mocking, and safe server cleanup after tests.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding CPU-only unit tests for component logic
• Testing features through a running SGLang server
• Registering tests in an appropriate CI suite

Detect unauthorized cryptocurrency mining in cloud workloads by combining billing changes, CPU usage, mining pool connections, and process alerts. Follow workflows to preserve evidence, isolate affected resources, and trace how attackers gained access.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating sudden cloud billing increases
• Responding to GuardDuty cryptocurrency alerts
• Checking containers for unauthorized mining processes

Build data processing pipelines for machine learning, from preprocessing to batch predictions. Uses Ray Data to handle datasets larger than memory, distribute work across CPUs and GPUs, and feed data into training frameworks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preprocessing datasets larger than memory
• Running batch predictions across multiple GPUs
• Preparing image and tabular data for training

Investigate slow Node.js, Python, and Go applications by measuring CPU use, memory, database queries, and data transfer. Identify bottlenecks, inspect frontend bundle sizes, run load tests, and compare measurements before and after changes to verify improvements.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating slow API responses
• Tracking memory growth and suspected leaks
• Analyzing bundle growth after adding dependencies

Install the free OpenWork desktop app on macOS, Windows, or Linux using the appropriate download for the computer. Guide initial setup, folder authorization, model selection, and a first task such as summarizing local files.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Installing OpenWork on a personal computer
• Choosing an installer for the OS and CPU
• Setting up a work folder and model

Create or update Kubernetes YAML configuration files for GKE Autopilot and Standard. Translates application requirements into manifests covering container permissions, CPU and memory allocation, health checks, storage, external routing, and GPU resources for AI model serving.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating GKE application deployment manifests
• Reviewing container security and resource settings
• Configuring external routes with GKE Gateway API

Helps manage and troubleshoot Linux, macOS, and Windows systems. Guides checks of CPU, memory, disks, networks, and logs, then supports service configuration and security hardening with explanations suited to the operating system.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating slow systems and resource usage
• Finding causes of low disk space
• Checking failed services and their logs

Guide MNN Metal backend development and optimization, from finding performance bottlenecks to changing GPU computation, combining operators, and adjusting execution scheduling. A task-based document index directs work toward relevant implementation rules, benchmarks, and correctness checks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding bottlenecks in Metal inference
• Adding or modifying Metal kernels
• Combining operators into one GPU dispatch

Build data processing pipelines for machine learning, from preprocessing to batch predictions. Uses Ray Data to handle datasets larger than memory, distribute work across CPUs and GPUs, and feed data into training frameworks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preprocessing datasets larger than memory
• Running batch predictions across multiple GPUs
• Preparing image and tabular data for training

Investigate slow rendering, uneven scrolling, and excessive updates in SwiftUI apps by reviewing code first. When needed, use runtime measurements to assess likely causes, prioritize fixes, and compare performance before and after changes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigate uneven scrolling and dropped frames
• Find causes of excessive view updates
• Review high CPU use or memory growth