5 repository-uri
Awesome GitHub RepositoriesPerformance Optimization Patterns
Coding patterns and techniques for optimizing performance in hot code paths, including loop selection and allocation avoidance.
Distinguishing note: None of the candidates relate to performance optimization; they all describe agentic or code refinement loops in AI/ML contexts.
Explore 5 awesome GitHub repositories matching software engineering & architecture · Performance Optimization Patterns. Refine with filters or upvote what's useful.
Awesome Performance Optimization Patterns GitHub Repositories
Găsește cele mai bune repo-uri cu AI.Vom căuta cele mai potrivite repository-uri folosind AI.
- braydie/howtobeaprogrammer
braydie/HowToBeAProgrammer
16,218Vezi pe GitHubHowToBeAProgrammer is a comprehensive software engineering career guide and professional development framework. It serves as a curated-knowledge repository and handbook designed to help programmers acquire technical habits and social competencies necessary for professional advancement. The project distinguishes itself by integrating technical craftsmanship with a detailed manual for technical leadership and organizational navigation. It provides specific strategies for career progression, such as compensation negotiation, promotion readiness, and the management of professional boundaries to p
Describes coding patterns for reducing execution time by minimizing expensive memory or I/O calls in hot paths.
learningprogramming - piglei/one-python-craftsman
piglei/one-python-craftsman
7,211Vezi pe GitHubThis project is a comprehensive Python coding guide and software engineering resource focused on professional development practices. It provides a detailed collection of idiomatic techniques, design patterns, and architectural strategies to improve code quality and maintainability. The guide emphasizes advanced design patterns such as dependency injection, data-driven design, and the application of SOLID principles for object-oriented design. It distinguishes itself by covering sophisticated structural strategies, including class-based decorators, the separation of interfaces from implementat
Details performance optimization patterns such as efficient iterator use and container selection to reduce memory usage.
articlesbookpython - juanitofatas/fast-ruby
JuanitoFatas/fast-ruby
5,730Vezi pe GitHubThis project is a Ruby performance optimization guide and refactoring resource. It provides a collection of benchmarked coding patterns and idiomatic comparisons designed to increase execution speed and reduce memory allocations in Ruby applications. The resource focuses on mapping common language constructs to their most computationally efficient equivalents. It uses comparative timing analysis and allocation-count profiling to identify high-performance idioms that replace object-heavy expressions. The project covers application runtime tuning and memory management by identifying patterns t
Provides benchmarked coding patterns and techniques for optimizing performance in hot code paths.
Ruby - databricks/scala-style-guide
databricks/scala-style-guide
2,784Vezi pe GitHubProvides guidelines for using while loops over for loops to avoid iterator allocation in performance-critical code.
- packtpublishing/learn-cuda-programming
PacktPublishing/Learn-CUDA-Programming
1,258Vezi pe GitHubAcest proiect servește drept resursă educațională cuprinzătoare pentru învățarea programării paralele și a calculului de înaltă performanță folosind unități de procesare grafică. Oferă îndrumări tehnice privind paradigmele fundamentale necesare pentru a descărca sarcinile intensive din punct de vedere computațional de la un sistem gazdă către acceleratoare hardware specializate. Materialele acoperă metodologiile de bază pentru gestionarea operațiunilor de date-paralele, inclusiv orchestrarea memoriei între spațiile gazdă și dispozitiv și organizarea firelor de execuție în grid-uri și blocuri structurate. Detaliază modelele de execuție necesare pentru a distribui sarcinile de lucru pe mai multe nuclee de procesare, permițând dezvoltatorilor să scaleze eficient aplicațiile intensive în date. Dincolo de implementarea de bază, resursa include practici de diagnosticare pentru analizarea metricilor de execuție și identificarea blocajelor de performanță. Oferă strategii pentru optimizarea execuției kernel-ului și depanarea erorilor logice în cadrul bazelor de cod concurente pentru a asigura un throughput și o eficiență maximă în mediile de calcul accelerate.
Provides comprehensive strategies for analyzing execution metrics, identifying bottlenecks, and optimizing kernel performance in parallel computing environments.
Cuda
