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2 रिपॉजिटरी

Awesome GitHub RepositoriesData Layout Optimizations

Optimizing the physical arrangement of tensors in memory to maximize compute efficiency.

Distinct from Computational Graph Optimizers: Specifically targets memory layout and tensor alignment within a compute graph, rather than general graph rewriting.

Explore 2 awesome GitHub repositories matching software engineering & architecture · Data Layout Optimizations. Refine with filters or upvote what's useful.

Awesome Data Layout Optimizations GitHub Repositories

AI के साथ बेहतरीन रिपॉजिटरी खोजें।हम AI का उपयोग करके सबसे सटीक रिपॉजिटरी खोजेंगे।
  • infrasys-ai/aisystemInfrasys-AI का अवतार

    Infrasys-AI/AISystem

    17,017GitHub पर देखें↗

    AISystem is a comprehensive AI full-stack infrastructure project covering the entire pipeline from AI chip architecture to high-level training frameworks. It encompasses the development of AI compiler frameworks, inference engines, and distributed training orchestrators designed to coordinate workloads across a heterogeneous compute stack of CPUs, GPUs, and NPUs. The project focuses on the deep integration of software and hardware, employing software-hardware co-design to align tensor layouts with physical memory structures. It provides specialized capabilities for accelerating Transformer mo

    Analyzes compute graphs to determine and insert efficient data layouts for optimized hardware performance.

    Jupyter Notebookaiaiinfraaisys
    GitHub पर देखें↗17,017
  • iree-org/ireeiree-org का अवतार

    iree-org/iree

    3,819GitHub पर देखें↗

    IREE is an MLIR-based compiler toolchain and runtime designed to translate machine learning models from various frameworks into optimized binaries for execution across diverse hardware targets. It provides a unified pipeline to ingest models from PyTorch, TensorFlow, JAX, and ONNX, lowering them into a common intermediate representation for deployment on CPUs, GPUs, and bare-metal embedded systems. The project distinguishes itself through a bytecode virtual machine and a hardware abstraction layer that decouple high-level model logic from specific hardware instruction sets. It supports sophis

    Reorganizes operand layouts across a workload to improve memory locality and minimize transformation overhead.

    C++compilercudajax
    GitHub पर देखें↗3,819
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  2. Software Engineering & Architecture
  3. Performance and Reliability
  4. Performance Optimization
  5. Computational Efficiency
  6. Computational Graph Optimizers
  7. Data Layout Optimizations