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4 repositorios

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

Encuentra los mejores repositorios con IA.Buscaremos los repositorios que mejor coincidan usando IA.
  • syllo/nvtopAvatar de Syllo

    Syllo/nvtop

    10,150Ver en GitHub↗

    nvtop es un panel basado en terminal utilizado para monitorear el rendimiento, el uso de memoria y la temperatura de múltiples procesadores gráficos y aceleradores de hardware. Funciona como una herramienta de administración centralizada para rastrear el estado y la carga de cómputo de varios dispositivos en un solo sistema. La herramienta se distingue por correlacionar los ID de procesos del sistema con el consumo de recursos de hardware, lo que permite a los usuarios identificar aplicaciones específicas que consumen recursos de GPU. Emplea una capa de abstracción agnóstica del proveedor para admitir hardware de diferentes fabricantes dentro de una sola interfaz. El software proporciona métricas de rendimiento en tiempo real y atribución de recursos por proceso utilizando una interfaz basada en texto. Los usuarios pueden gestionar los diseños de la interfaz y guardar las preferencias de visualización a través de un archivo de configuración local para mantener la configuración entre sesiones.

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

    Cadrenoamdapple
    Ver en GitHub↗10,150
  • tingsongyu/pytorch_tutorialAvatar de TingsongYu

    TingsongYu/PyTorch_Tutorial

    8,018Ver en 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
    Ver en GitHub↗8,018
  • xuehaipan/nvitopAvatar de XuehaiPan

    XuehaiPan/nvitop

    6,577Ver en GitHub↗

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

    Pythoncommand-line-toolconsolecuda
    Ver en GitHub↗6,577
  • mandiant/capaAvatar de mandiant

    mandiant/capa

    6,062Ver en 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
    Ver en 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

Explorar subetiquetas

  • 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.