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4 dépôts

Awesome GitHub RepositoriesGPU Process Analysis

Analysis of individual process resource consumption on GPUs to identify bottlenecks and leaks.

Distinct from GPU Performance Profilers: Distinct from GPU Performance Profilers: focuses on process-level attribution and identification rather than kernel-level throughput analysis.

Explore 4 awesome GitHub repositories matching testing & quality assurance · GPU Process Analysis. Refine with filters or upvote what's useful.

Awesome GPU Process Analysis GitHub Repositories

Trouvez les meilleurs dépôts grâce à l'IA.Nous recherchons les dépôts les plus pertinents grâce à l'IA.
  • syllo/nvtopAvatar de Syllo

    Syllo/nvtop

    10,150Voir sur GitHub↗

    nvtop est un tableau de bord basé sur le terminal utilisé pour surveiller les performances, l'utilisation de la mémoire et la température de plusieurs processeurs graphiques et accélérateurs matériels. Il fonctionne comme un outil d'administration centralisé pour suivre la santé et la charge de calcul de plusieurs appareils sur un seul système. L'outil se distingue en corrélant les ID de processus système avec la consommation de ressources matérielles, permettant aux utilisateurs d'identifier les applications spécifiques consommant des ressources GPU. Il utilise une couche d'abstraction agnostique du fournisseur pour prendre en charge le matériel de plusieurs fabricants différents au sein d'une seule interface. Le logiciel fournit des métriques de performance en temps réel et une attribution des ressources par processus en utilisant une interface textuelle. Les utilisateurs peuvent gérer les mises en page de l'interface et enregistrer les préférences d'affichage via un fichier de configuration local pour maintenir les paramètres entre les sessions.

    Analyzes individual process resource consumption on GPUs to identify bottlenecks and memory leaks.

    Cadrenoamdapple
    Voir sur GitHub↗10,150
  • tingsongyu/pytorch_tutorialAvatar de TingsongYu

    TingsongYu/PyTorch_Tutorial

    8,018Voir sur GitHub↗

    This project is a comprehensive collection of educational examples and reference implementations for building vision and language models using PyTorch. It serves as a deep learning tutorial covering the end-to-end process of developing neural networks, from initial architecture definition to final production deployment. The repository provides detailed guides on implementing a wide range of domain-specific models, including convolutional neural networks for object detection and segmentation, as well as transformer and recurrent architectures for natural language processing. It emphasizes gene

    Tracks CPU and GPU utilization and data throughput to identify system-level application bottlenecks.

    Python
    Voir sur GitHub↗8,018
  • xuehaipan/nvitopAvatar de XuehaiPan

    XuehaiPan/nvitop

    6,577Voir sur GitHub↗

    Lists running GPU processes with PID, user, and memory, and allows termination from the interface.

    Pythoncommand-line-toolconsolecuda
    Voir sur GitHub↗6,577
  • mandiant/capaAvatar de mandiant

    mandiant/capa

    6,062Voir sur GitHub↗

    capa is a binary capability scanner that identifies high-level behaviors and actions an executable can perform, such as network communication or file manipulation. It functions as a malware behavior analysis tool and a MITRE ATT&CK mapping framework, scanning PE, ELF, .NET, and shellcode files through both static analysis and dynamic sandbox report processing. The tool distinguishes itself through a YAML-based detection rule engine that defines detection logic in human-readable files, with conditions expressed as feature combinations and logical operators. It integrates with IDA Pro, Ghidra,

    Limits analysis to specific processes by PID when processing dynamic sandbox reports.

    Python
    Voir sur GitHub↗6,062
  1. Home
  2. Testing & Quality Assurance
  3. Performance Testing and Analysis
  4. Performance Profiling
  5. GPU Performance Profilers
  6. GPU Process Analysis

Explorer les sous-tags

  • Colorized GPU Status OutputsDetailed, colorized information about GPU devices and their running processes returned programmatically. **Distinct from GPU Process Analysis:** Distinct from GPU Process Analysis: focuses on colorized output formatting rather than performance bottleneck analysis.
  • GPU Process Management InterfacesInterfaces for listing GPU processes with PID, user, and memory consumption, and terminating them directly. **Distinct from GPU Process Analysis:** Distinct from GPU Process Analysis: focuses on interactive management and termination rather than performance bottleneck analysis.
  • GPU Process Sorters and FiltersRearranges and narrows lists of running GPU processes by user-defined criteria directly in the monitor view. **Distinct from GPU Process Analysis:** Distinct from GPU Process Analysis: focuses on interactive sorting and filtering rather than performance analysis.
  • GPU Process Tree ViewersDisplays hierarchical trees of GPU processes and their parent processes for easier navigation. **Distinct from GPU Process Analysis:** Distinct from GPU Process Analysis: focuses on hierarchical display rather than resource consumption analysis.
  • Process KillersTerminating GPU processes directly from the monitoring interface. **Distinct from GPU Process Analysis:** Distinct from GPU Process Analysis: focuses on termination action rather than analysis of resource consumption.
  • Process-Scoped Sandbox AnalysisLimits analysis to specific processes by PID when processing dynamic sandbox reports. **Distinct from GPU Process Analysis:** Distinct from GPU Process Analysis: focuses on scoping sandbox report analysis by PID, not GPU resource profiling.