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KomputeProject avatar

KomputeProject/kompute

0
View on GitHub↗
2,519 stars·194 forks·C++·Apache-2.0·12 viewskompute.cc↗

Kompute

General purpose GPU compute framework built on Vulkan to support 1000s of cross vendor graphics cards (AMD, Qualcomm, NVIDIA & friends). Blazing fast, mobile-enabled, asynchronous and optimized for advanced GPU data processing usecases. Backed by the Linux Foundation.

Features

  • Computation and Optimization - Framework for developing applications on neuromorphic hardware.
  • Parallel Programming Frameworks - Cross-vendor GPU compute framework for graphics cards.

Star history

Star history chart for komputeproject/komputeStar history chart for komputeproject/kompute

How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.

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Frequently asked questions

What does komputeproject/kompute do?

General purpose GPU compute framework built on Vulkan to support 1000s of cross vendor graphics cards (AMD, Qualcomm, NVIDIA & friends). Blazing fast, mobile-enabled, asynchronous and optimized for advanced GPU data processing usecases. Backed by the Linux Foundation.

What are the main features of komputeproject/kompute?

The main features of komputeproject/kompute are: Computation and Optimization, Parallel Programming Frameworks.

What are some open-source alternatives to komputeproject/kompute?

Open-source alternatives to komputeproject/kompute include: horovod/horovod — Horovod is a distributed deep learning framework and gradient synchronizer designed to scale model training across… alpaka-group/alpaka — alpaka - Abstraction Library for Parallel Kernel Acceleration. arogozhnikov/einops — Einops is a tensor manipulation library that provides a framework-agnostic interface for reshaping, Einstein… bitsandbytes-foundation/bitsandbytes — bitsandbytes is a deep learning quantization tool and library designed to reduce the memory footprint of large… bloomen/transwarp — A header-only C++ library for task concurrency. adapter-hub/adapters — A Unified Library for Parameter-Efficient and Modular Transfer Learning.

Open-source alternatives to Kompute

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    Horovod is a distributed deep learning framework and gradient synchronizer designed to scale model training across multiple GPUs and compute nodes. It functions as a distributed training orchestrator and an elastic training engine, utilizing an MPI collective communication library to synchronize weights and gradients across TensorFlow, PyTorch, Keras, and MXNet models. The system distinguishes itself through dynamic elastic scaling, which allows it to adjust the number of active workers at runtime and recover from node failures. It optimizes communication efficiency using tensor fusion batchi

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    alpaka - Abstraction Library for Parallel Kernel Acceleration

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  • arogozhnikov/einopsarogozhnikov avatar

    arogozhnikov/einops

    9,398View on GitHub↗

    Einops is a tensor manipulation library that provides a framework-agnostic interface for reshaping, Einstein summation, and multi-dimensional array operations. It serves as an abstraction layer that works across NumPy, PyTorch, TensorFlow, and JAX, allowing for tensor transformations without changing the API. The library distinguishes itself through a declarative notation system that uses readable string patterns to describe tensor rearrangements and reductions. This approach includes an extended Einstein summation interface that supports multi-letter axis names and a named dimension mapping

    Pythoncupydeep-learningeinops
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  • adapter-hub/adaptersadapter-hub avatar

    adapter-hub/adapters

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