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4 repository-uri

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

Găsește cele mai bune repo-uri cu AI.Vom căuta cele mai potrivite repository-uri folosind AI.
  • syllo/nvtopAvatar Syllo

    Syllo/nvtop

    10,150Vezi pe GitHub↗

    nvtop este un dashboard bazat pe terminal utilizat pentru monitorizarea performanței, utilizării memoriei și temperaturii mai multor procesoare grafice și acceleratoare hardware. Acesta funcționează ca un instrument de administrare centralizat pentru a urmări starea și sarcina de calcul a mai multor dispozitive pe un singur sistem. Instrumentul se distinge prin corelarea ID-urilor proceselor de sistem cu consumul de resurse hardware, permițând utilizatorilor să identifice aplicații specifice care consumă resurse GPU. Acesta utilizează un strat de abstractizare agnostic față de furnizor pentru a suporta hardware de la mai mulți producători diferiți în cadrul unei singure interfețe. Software-ul oferă metrici de performanță în timp real și atribuirea resurselor per proces folosind o interfață bazată pe text. Utilizatorii pot gestiona layout-urile interfeței și pot salva preferințele de afișare printr-un fișier de configurare local pentru a menține setările între sesiuni.

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

    Cadrenoamdapple
    Vezi pe GitHub↗10,150
  • tingsongyu/pytorch_tutorialAvatar TingsongYu

    TingsongYu/PyTorch_Tutorial

    8,018Vezi pe 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
    Vezi pe GitHub↗8,018
  • xuehaipan/nvitopAvatar XuehaiPan

    XuehaiPan/nvitop

    6,577Vezi pe GitHub↗

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

    Pythoncommand-line-toolconsolecuda
    Vezi pe GitHub↗6,577
  • mandiant/capaAvatar mandiant

    mandiant/capa

    6,062Vezi pe 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
    Vezi pe 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

Explorează sub-etichetele

  • 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.