5 Repos
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.
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Awesome Performance Optimization Patterns GitHub Repositories
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- braydie/howtobeaprogrammer
braydie/HowToBeAProgrammer
16,218Auf GitHub ansehenHowToBeAProgrammer 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
7,211Auf GitHub ansehenThis 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
5,730Auf GitHub ansehenDieses Projekt ist ein Leitfaden zur Performance-Optimierung und eine Ressource für Refactoring in Ruby. Es bietet eine Sammlung von gebenchmarkten Coding-Mustern und idiomatischen Vergleichen, die darauf ausgelegt sind, die Ausführungsgeschwindigkeit zu erhöhen und Speicherallokationen in Ruby-Anwendungen zu reduzieren. Die Ressource konzentriert sich darauf, gängige Sprachkonstrukte ihren recheneffizientesten Äquivalenten zuzuordnen. Sie nutzt vergleichende Timing-Analysen und Profiling der Allokationsanzahl, um hochperformante Idiome zu identifizieren, die objektlastige Ausdrücke ersetzen. Das Projekt deckt das Tuning der Anwendungslaufzeit und das Speichermanagement ab, indem es Muster identifiziert, die den Overhead der Garbage Collection reduzieren. Es setzt auf Benchmark-getriebene Entwicklung, um die Auswirkungen spezifischer Sprachfeatures auf Systemspeicher und Ausführungsgeschwindigkeit zu bewerten.
Provides benchmarked coding patterns and techniques for optimizing performance in hot code paths.
Ruby - databricks/scala-style-guide
databricks/scala-style-guide
2,784Auf GitHub ansehenProvides guidelines for using while loops over for loops to avoid iterator allocation in performance-critical code.
- packtpublishing/learn-cuda-programming
PacktPublishing/Learn-CUDA-Programming
1,258Auf GitHub ansehenThis project serves as a comprehensive educational resource for learning parallel programming and high-performance computing using graphics processing units. It provides technical guidance on the fundamental paradigms required to offload computationally intensive tasks from a host system to specialized hardware accelerators. The materials cover the core methodologies for managing data-parallel operations, including the orchestration of memory between host and device spaces and the organization of threads into structured grids and blocks. It details the execution models necessary to distribute
Provides comprehensive strategies for analyzing execution metrics, identifying bottlenecks, and optimizing kernel performance in parallel computing environments.
Cuda
