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Awesome GitHub RepositoriesMemory Layouts

Optimized data storage structures for cache-efficient processing.

Distinguishing note: Focuses on low-level memory organization rather than high-level database management.

Explore 24 awesome GitHub repositories matching data & databases · Memory Layouts. Refine with filters or upvote what's useful.

Awesome Memory Layouts GitHub Repositories

AI के साथ बेहतरीन रिपॉजिटरी खोजें।हम AI का उपयोग करके सबसे सटीक रिपॉजिटरी खोजेंगे।
  • pandas-dev/pandaspandas-dev का अवतार

    pandas-dev/pandas

    49,039GitHub पर देखें↗

    Pandas is a high-performance data analysis library that provides a comprehensive framework for manipulating, cleaning, and transforming structured datasets. It centers on labeled one-dimensional and two-dimensional data structures, allowing users to construct, filter, and reshape tabular information while performing complex arithmetic and logical operations. The library distinguishes itself through a sophisticated indexing engine that enables automatic data alignment during calculations and relational merges. By utilizing a block-based memory layout, it optimizes cache locality for vectorized

    Provides contiguous memory block storage to optimize cache locality and vectorized operations.

    Pythonalignmentdata-analysisdata-science
    GitHub पर देखें↗49,039
  • numpy/numpynumpy का अवतार

    numpy/numpy

    32,207GitHub पर देखें↗

    NumPy is a foundational library for scientific computing in Python, providing a comprehensive framework for managing and manipulating large-scale numerical information. It centers on high-performance multidimensional array objects that serve as the primary data structure for complex mathematical operations and data analysis workflows. The library distinguishes itself through specialized mechanisms for handling multidimensional data, including advanced indexing, slicing, and broadcasting techniques that allow for efficient operations across arrays of varying shapes. It utilizes strided metadat

    Uses strided metadata to enable efficient, zero-copy slicing of multidimensional arrays.

    Pythonnumpypython
    GitHub पर देखें↗32,207
  • ml-explore/mlxml-explore का अवतार

    ml-explore/mlx

    27,047GitHub पर देखें↗

    This project is a machine learning array framework and tensor computation library designed for high-performance numerical computing. It provides a comprehensive suite of tools for constructing and training neural networks, featuring an automatic differentiation engine that facilitates gradient-based optimization and complex mathematical modeling. The library distinguishes itself through a unified memory architecture that allows data to be shared across CPU and GPU devices without explicit copies, significantly reducing data movement overhead. Its execution model relies on a lazy evaluation en

    Forces arrays into row-major memory layouts to ensure alignment and compatibility.

    C++mlx
    GitHub पर देखें↗27,047
  • gfx-rs/wgpugfx-rs का अवतार

    gfx-rs/wgpu

    17,382GitHub पर देखें↗

    This project is a cross-platform graphics and compute framework that provides a unified, hardware-agnostic abstraction layer for rendering and parallel processing. It enables developers to build high-performance applications that execute consistently across diverse operating systems and hardware backends, including Vulkan, Metal, and DirectX. By mapping high-level graphics commands to native APIs, it serves as a portable foundation for both real-time 3D rendering and general-purpose GPU computing. The framework distinguishes itself through a robust architecture that supports both native deskt

    Configures alignment and padding of vertex, index, and uniform data structures for hardware-specific memory requirements.

    Rustd3d12gpuhacktoberfest
    GitHub पर देखें↗17,382
  • apache/arrowapache का अवतार

    apache/arrow

    16,529GitHub पर देखें↗

    Arrow is a cross-language development platform for in-memory data. It provides a standardized, language-independent columnar memory format designed to accelerate analytical operations and improve memory efficiency on modern computing hardware. By utilizing a schema-driven approach, the framework enables the efficient organization of both flat and nested data structures. The project functions as an analytical data processing engine that facilitates high-performance computation directly on memory-resident datasets. It distinguishes itself through a zero-copy architecture, which allows multiple

    Organizes data in contiguous memory blocks to maximize CPU cache efficiency and enable vectorized processing.

    C++arrowparquet
    GitHub पर देखें↗16,529
  • scipy/scipyscipy का अवतार

    scipy/scipy

    14,474GitHub पर देखें↗

    SciPy is a scientific computing library for Python that provides a comprehensive collection of mathematical algorithms and numerical tools for research and engineering. It functions as a high-performance numerical analysis framework, bridging high-level Python code with compiled C and Fortran routines to execute complex computations at hardware speeds. The library is built upon array-based data structures that utilize strided memory layouts to enable efficient data manipulation and slicing. By employing vectorized operation dispatch and linking to optimized hardware-specific linear algebra li

    Utilizes strided memory layouts to enable efficient slicing and manipulation of multidimensional data without copying.

    Pythonalgorithmsclosemberpython
    GitHub पर देखें↗14,474
  • rougier/numpy-100rougier का अवतार

    rougier/numpy-100

    13,812GitHub पर देखें↗

    This project is a curated collection of programming exercises designed to build proficiency in numerical computing and data manipulation. It provides a structured learning path for mastering multidimensional array operations, vectorized arithmetic, and statistical analysis. The repository focuses on developing practical expertise in array-based workflows, emphasizing techniques such as memory management, efficient data processing, and the replacement of explicit loops with vectorized operations. Users engage with hands-on challenges that cover the full lifecycle of numerical data, from initia

    Organizes data into user-defined fields within contiguous memory blocks to represent complex records.

    Pythonbinderexercisesnotebook
    GitHub पर देखें↗13,812
  • odin-lang/odinodin-lang का अवतार

    odin-lang/Odin

    9,806GitHub पर देखें↗

    Odin is a compiled, statically typed systems programming language designed for high-performance software development. It focuses on pragmatic low-level memory control, providing a toolset for manual memory management and precise control over hardware utilization. The language is distinguished by its flexible memory model, which includes custom allocators and precise data layout capabilities to optimize resource usage. It features a comprehensive foreign function interface for importing assembly files and linking with external libraries using configurable calling conventions. The type system

    Enables efficient data packing using bit fields and specific record layouts for cache-efficient processing.

    Odincompilerlanguageodin
    GitHub पर देखें↗9,806
  • rapidsai/cudfrapidsai का अवतार

    rapidsai/cudf

    9,672GitHub पर देखें↗

    cuDF is a GPU-accelerated dataframe library and data processing engine designed for manipulating and analyzing large tabular datasets. It provides a high-level API for executing filtering, joining, and aggregating operations directly on GPU hardware. The project integrates the Apache Arrow memory format to enable zero-copy data transfers and includes a just-in-time compiler for executing custom user-defined functions on the GPU. The library features specialized acceleration for existing workflows by redirecting standard Pandas dataframe calls and Polars query plans to a GPU backend. It also p

    Implements optimized columnar memory layouts to maximize GPU bandwidth and SIMD execution efficiency.

    C++
    GitHub पर देखें↗9,672
  • torch/torch7torch का अवतार

    torch/torch7

    9,127GitHub पर देखें↗

    Torch7 is a scientific computing environment and tensor computation library used for deep learning research and numerical analysis. It functions as a Lua-based framework for training neural networks and learning agents, providing a toolkit for implementing architectures and training through reinforcement learning algorithms. The project is distinguished by its tight integration with C, utilizing a binding layer to map high-level scripting to low-level C structures for direct memory access. It supports hardware-accelerated computation by offloading linear algebra and convolution operations to

    Implements strided memory layouts to manipulate tensor dimensions and shapes without duplicating data buffers.

    C
    GitHub पर देखें↗9,127
  • vaexio/vaexvaexio का अवतार

    vaexio/vaex

    8,506GitHub पर देखें↗

    Vaex is a high-performance Apache Arrow DataFrame library and out-of-core data processing engine designed to handle billion-row tabular datasets in Python. It functions as a lazy evaluation framework that defers computations and transformations until results are required, enabling the processing of datasets that exceed available system RAM by mapping files directly from disk. The project distinguishes itself as a tool for big data visualization and exploration, specifically integrated for use within interactive notebooks. It provides specialized capabilities for machine learning feature engin

    Implements an Apache Arrow columnar memory layout to enable high-speed data access and efficient interoperability.

    Python
    GitHub पर देखें↗8,506
  • path/fastimagecachepath का अवतार

    path/FastImageCache

    8,068GitHub पर देखें↗

    FastImageCache is an iOS image caching library that provides a persistent disk-based image store. It utilizes a persistent bitmap cache to store images in uncompressed formats and incorporates an image pre-processing pipeline to optimize assets before they are committed to storage. The library optimizes rendering performance by using memory-mapped image tables for constant-time retrieval and byte-aligned data layouts to prevent memory copies. It organizes images of identical dimensions into shared tables and manages disk space through a least-recently-used cache eviction system. The project

    Optimizes rendering performance by aligning image rows to memory boundaries to prevent expensive memory copies.

    Objective-C
    GitHub पर देखें↗8,068
  • dominikh/go-toolsdominikh का अवतार

    dominikh/go-tools

    6,818GitHub पर देखें↗

    go-tools is a collection of utilities for Go static analysis and memory layout optimization. It provides a toolset designed to analyze source code to detect bugs and dead code, alongside specialized tools for optimizing how structs are arranged in memory. The project includes a memory alignment visualizer to display physical memory layouts and padding, as well as a struct layout optimizer that reorders fields to minimize memory padding. Additionally, it provides a boilerplate generator to automate the creation of registration and test files required for developing custom Go analyzers. The to

    Provides a utility for inspecting memory alignment, field sizes, and padding of Go structs.

    Go
    GitHub पर देखें↗6,818
  • halide/halidehalide का अवतार

    halide/Halide

    6,572GitHub पर देखें↗

    Provides native handling of interleaved, planar, and custom memory layouts without data copying.

    C++compilerdslgpu
    GitHub पर देखें↗6,572
  • mandliya/algorithms_and_data_structuresmandliya का अवतार

    mandliya/algorithms_and_data_structures

    6,145GitHub पर देखें↗

    This project is a comprehensive collection of C++ libraries and toolkits providing reference implementations for data structures, graph algorithms, and bitwise logic. It serves as a C++ algorithm reference containing over 180 solved coding problems and a specialized toolkit for competitive programming. The repository distinguishes itself through extensive low-level bit manipulation libraries for parity checks, endianness detection, and XOR-based logic. It also provides a wide array of reference solutions for complex algorithmic challenges involving backtracking, graph theory, and dynamic prog

    Implements byte order reversal to convert data between big-endian and little-endian representations.

    C++algorithmbit-manipulationc
    GitHub पर देखें↗6,145
  • mosra/magnummosra का अवतार

    mosra/magnum

    5,169GitHub पर देखें↗

    Magnum is a C++ middleware suite for cross-platform graphics development and real-time data visualization. It provides a hardware-agnostic rendering layer that translates graphics commands into platform-specific calls, ensuring consistent behavior across different GPU drivers and APIs such as Vulkan. The project focuses on decoupling application logic from underlying hardware through abstract graphics and system utilities. It features a plugin-based resource importer for 3D assets and audio, a hierarchical scene graph for spatial transformations, and a high-performance signal-based event syst

    Organizes interleaved data into contiguous blocks and strided views to enhance data locality and SIMD efficiency.

    C++
    GitHub पर देखें↗5,169
  • c3lang/c3cc3lang का अवतार

    c3lang/c3c

    5,147GitHub पर देखें↗

    c3c is the compiler for the C3 programming language, transforming source code into executable binaries, static libraries, or dynamic libraries using an LLVM backend. It implements a system based on result-based error handling, scoped memory pooling, and a semantic macro system. The compiler provides first-class support for hardware-backed SIMD vectors that map directly to processor instructions and enables runtime polymorphism through interface-based dynamic dispatch. The project covers a broad set of low-level capabilities, including manual and pooled memory management, inline assembly inte

    Controls bit-level data layout for big-endian and little-endian storage and overlapping ranges.

    C3c3compilerlanguage
    GitHub पर देखें↗5,147
  • dimforge/nalgebradimforge का अवतार

    dimforge/nalgebra

    4,745GitHub पर देखें↗

    nalgebra Rust के लिए एक लीनियर अलजेब्रा लाइब्रेरी है जो कंपाइल-टाइम और रनटाइम आयामों दोनों के समर्थन के साथ मैट्रिक्स और वेक्टर ऑपरेशंस प्रदान करती है। यह एक न्यूमेरिकल एनालिसिस लाइब्रेरी और एक स्पार्स मैट्रिक्स लाइब्रेरी के रूप में कार्य करती है, जो एम्बेडेड वातावरण और WebAssembly में Rust स्टैंडर्ड लाइब्रेरी की आवश्यकता के बिना चलने में सक्षम एक गणितीय फ्रेमवर्क प्रदान करती है। यह प्रोजेक्ट एक ज्यामितीय ट्रांसफॉर्मेशन लाइब्रेरी के रूप में अलग है, जो 3D रोटेशन, ट्रांसलेशन और प्रोजेक्शन को संभालने के लिए होमोजेनियस कोऑर्डिनेट्स, क्वाटरनियंस और आइसोमेट्रीज़ का उपयोग करती है। यह लीनियर सिस्टम को हल करने और मैट्रिसेस का विश्लेषण करने के लिए LU, QR, Cholesky, SVD और आइगेनडिकंपोज़िशन सहित विभिन्न मैट्रिक्स डिकंपोज़िशन को लागू करती है। यह लाइब्रेरी स्थानिक ट्रांसफॉर्मेशन के लिए ज्यामितीय कंप्यूटिंग, प्रोजेक्शन मैट्रिक्स कंपोज़िशन और शेडर डेटा एक्सपोर्ट के लिए कंप्यूटर ग्राफिक्स यूटिलिटीज, और कंप्रेस्ड रो और कॉलम स्टोरेज का उपयोग करके विशेष स्पार्स मैट्रिक्स मैनेजमेंट सहित व्यापक क्षमता क्षेत्रों को कवर करती है। यह मैट्रिक्स इनिशियलाइज़ेशन, रीसाइज़िंग और Matrix Market फाइलों की पार्सिंग के लिए डेटा मैनेजमेंट टूल्स भी प्रदान करती है।

    Provides non-owning references to matrix sub-sections using memory offsets to avoid data copying.

    Rustalgebralinear-algebramatrix
    GitHub पर देखें↗4,745
  • rust-ndarray/ndarrayrust-ndarray का अवतार

    rust-ndarray/ndarray

    4,290GitHub पर देखें↗

    ndarray Rust के लिए एक मल्टीडायमेंशनल ऐरे लाइब्रेरी है जो एक लीनियर अलजेब्रा फ्रेमवर्क और वैज्ञानिक कंप्यूटिंग टूल के रूप में कार्य करती है। यह n-डायमेंशनल ऐरे बनाने और हेरफेर करने के लिए मुख्य इंफ्रास्ट्रक्चर प्रदान करती है, जो एक समानांतर ऐरे प्रोसेसर और संख्यात्मक डेटा विश्लेषण के लिए एक टूलकिट दोनों के रूप में कार्य करती है। यह लाइब्रेरी कुशल स्लाइसिंग और मेमोरी व्यू प्रदान करके खुद को अलग करती है, जो कॉपी किए बिना डेटा साझा करने की अनुमति देती है। यह उच्च-गति मैट्रिक्स गुणन के लिए अनुकूलित बैकएंड गणित लाइब्रेरीज़ का लाभ उठाती है और प्रसंस्करण में तेजी लाने के लिए कई CPU थ्रेड्स में भारी गणितीय पुनरावृत्तियों को वितरित करती है। यह प्रोजेक्ट गणितीय कार्यों की एक विस्तृत श्रृंखला को कवर करता है, जिसमें एलिमेंट-वाइज अंकगणित, अक्ष-आधारित डेटा एकत्रीकरण, और डॉट प्रोडक्ट गणना शामिल है। इसमें ऐरे हेरफेर के लिए व्यापक उपयोगिताएँ भी शामिल हैं जैसे कि रीशेपिंग, फ़्लैटनिंग, स्टैकिंग, और कोऑर्डिनेट ग्रिड निर्माण, साथ ही यादृच्छिक ऐरे निर्माण और सीरियलाइज़ेशन के लिए समर्थन।

    Maps multidimensional indices to flat memory buffers using axis-specific step sizes for efficient zero-copy slicing.

    Rust
    GitHub पर देखें↗4,290
  • dpilger26/numcppdpilger26 का अवतार

    dpilger26/NumCpp

    3,963GitHub पर देखें↗

    NumCpp is a C++ framework and numerical computing library that provides a toolkit for multi-dimensional array management and mathematical routines. It functions as a C++ implementation of the NumPy ecosystem, offering a scientific computing framework for managing tensors and performing complex algebraic equations. The project enables high-performance array manipulation within a C++ environment without relying on a Python runtime. It distinguishes itself by providing a NumPy-like interface for executing linear algebra, managing multi-dimensional data structures, and performing numerical proces

    Utilizes strided memory mapping to allow efficient array reshaping and slicing without copying data.

    C++
    GitHub पर देखें↗3,963
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  3. Memory Layouts

सब-टैग एक्सप्लोर करें

  • Endianness and Range ControlControl over big-endian and little-endian storage and the definition of overlapping bit ranges. **Distinct from Memory Layouts:** Distinct from general Memory Layouts by focusing on byte-ordering and range overlap for bit-level data.
  • Memory Layout Enforcers1 सब-टैगForces arrays into specific memory layouts like row-major to ensure alignment. **Distinct from Memory Layouts:** Distinct from general memory layouts: focuses on active enforcement and copying.
  • Non-Planar Layout HandlersProcesses images stored in interleaved, planar, or custom memory layouts without copying data. **Distinct from Memory Layouts:** Distinct from Memory Layouts: focuses on handling non-planar and interleaved image layouts specifically, not general memory organization.
  • Strided2 सब-टैग्सData structures using contiguous memory blocks with metadata strides for efficient slicing without copying. **Distinct from Memory Layouts:** Distinct from Memory Layouts: focuses on strided metadata for zero-copy slicing rather than general cache-efficient storage.