#计算机科学#A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computa...
翻译 - 快速,可扩展,高性能的“决策树加速梯度”库,用于对Python,R,Java,C ++进行排名,分类,回归和其他机器学习任务。支持在CPU和GPU上进行计算。
gyroflow 是一个可以通过陀螺仪和加速传感器,稳定视频防抖动的应用程序
#算法刷题#[ARCHIVED] The C++ parallel algorithms library. See https://github.com/NVIDIA/cccl
翻译 - Thrust是一个类似于C ++标准库的C ++并行编程库。
High-performance TensorFlow library for quantitative finance.
翻译 - 高性能TensorFlow库,用于量化金融。
The fastest and most memory efficient lattice Boltzmann CFD software, running on all GPUs and CPUs via OpenCL. Free for non-commercial use.
#计算机科学#Resource scheduling and cluster management for AI
翻译 - AI的资源调度和集群管理
#计算机科学#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 ad...
GPGPU microprocessor architecture
翻译 - GPGPU微处理器架构
CUDA integration for Python, plus shiny features
Parallel Computing and Scientific Machine Learning (SciML): Methods and Applications (MIT 18.337J/6.338J)
翻译 - 18.337-并行计算与科学机器学习
#计算机科学#Deep learning in Rust, with shape checked tensors and neural networks
The write-once-run-anywhere GPGPU library for Rust
翻译 - mu是用于在Rust中开发安全,强大的GPU加速应用程序的框架。
#Awesome#😎 Curated list of awesome things around WebGPU ecosystem.
Implementation of SYCL and C++ standard parallelism for CPUs and GPUs from all vendors: The independent, community-driven compiler for C++-based heterogeneous programming models. Lets applications ada...
#计算机科学#Simulation of spiking neural networks (SNNs) using PyTorch.
#计算机科学#A fast, ergonomic and portable tensor library in Nim with a deep learning focus for CPU, GPU and embedded devices via OpenMP, Cuda and OpenCL backends
An efficient C++17 GPU numerical computing library with Python-like syntax
翻译 - 一个高效的 C++17 GPU 数值计算库,具有类似 Python 的语法
TornadoVM: A practical and efficient heterogeneous programming framework for managed languages