5 रिपॉजिटरी
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
AI के साथ बेहतरीन रिपॉजिटरी खोजें।हम AI का उपयोग करके सबसे सटीक रिपॉजिटरी खोजेंगे।
- braydie/howtobeaprogrammer
braydie/HowToBeAProgrammer
16,218GitHub पर देखेंHowToBeAProgrammer 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
7,211GitHub पर देखेंThis 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
5,730GitHub पर देखेंयह प्रोजेक्ट एक Ruby प्रदर्शन अनुकूलन गाइड और रिफ़ैक्टरिंग संसाधन है। यह Ruby एप्लिकेशन्स में निष्पादन गति बढ़ाने और मेमोरी आवंटन को कम करने के लिए डिज़ाइन किए गए बेंचमार्क कोडिंग पैटर्न और मुहावरेदार (idiomatic) तुलनाओं का एक संग्रह प्रदान करती है। यह संसाधन सामान्य भाषा निर्माणों को उनके सबसे कम्प्यूटेशनल रूप से कुशल समकक्षों के साथ मैप करने पर केंद्रित है। यह ऑब्जेक्ट-हैवी एक्सप्रेशन्स को बदलने वाले उच्च-प्रदर्शन वाले मुहावरों की पहचान करने के लिए तुलनात्मक टाइमिंग एनालिसिस और एलोकेशन-काउंट प्रोफ़ाइलिंग का उपयोग करता है। यह प्रोजेक्ट उन पैटर्न्स की पहचान करके एप्लिकेशन रनटाइम ट्यूनिंग और मेमोरी मैनेजमेंट को कवर करता है जो गारबेज कलेक्शन ओवरहेड को कम करते हैं। यह सिस्टम मेमोरी और निष्पादन गति पर विशिष्ट भाषा सुविधाओं के प्रभाव का मूल्यांकन करने के लिए बेंचमार्क-संचालित विकास को नियोजित करता है।
Provides benchmarked coding patterns and techniques for optimizing performance in hot code paths.
Ruby - databricks/scala-style-guide
databricks/scala-style-guide
2,784GitHub पर देखेंProvides guidelines for using while loops over for loops to avoid iterator allocation in performance-critical code.
- packtpublishing/learn-cuda-programming
PacktPublishing/Learn-CUDA-Programming
1,258GitHub पर देखेंThis 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
