920 Open source projects that are alternatives of or similar to Senet

GGNN: State of the Art Graph-based GPU Nearest Neighbor Search

Robotics with GPU computing

PyGraphistry is a Python library to quickly load, shape, embed, and explore big graphs with the GPU-accelerated Graphistry visual graph analyzer

SDK for GPU accelerated genome assembly and analysis

JIT Compilation for Multiple Architectures: C++, OpenMP, CUDA, HIP, OpenCL, Metal

CUDA integration for Python, plus shiny features

ThunderSVM: A Fast SVM Library on GPUs and CPUs

Deep Learning library using GPU(CUDA/cuBLAS)

Modified nVidia .inf files to run drivers on all video cards, research & telemetry free drivers

A Neural Network Toolkit.

Simple physically based path tracer based on Nvidia's Optix Ray Tracing Engine

Set up CUDA for machine learning (and gaming) on macOS using a NVIDIA eGPU

Tests and benchmarks for cudnn (and in the future, other nvidia libraries)

OptiX Path Tracer

Qualia is a deep learning framework deeply integrated with automatic differentiation and dynamic graphing with CUDA acceleration. Qualia was built from scratch.

Reference implementation for "Massively Parallel Rendering of Complex Closed-Form Implicit Surfaces" (SIGGRAPH 2020)

A community run, 5-day PyTorch Deep Learning Bootcamp

Deep.Net machine learning framework for F#

Gradient Boosting powered by GPU(NVIDIA CUDA)

Molecular dynamics and Monte Carlo soft matter simulation on GPUs.

Single C file, Realtime CPU/GPU Profiler with Remote Web Viewer

An open-source library of algorithms to analyse time series in GPU and CPU.

CUDA Matrix Factorization Library with Alternating Least Square (ALS)

cuML - RAPIDS Machine Learning Library

Examples of C# code compiled to GPU by hybridizer

Experiments with CUDA and Rust

Deep Learning API and Server in C++14 support for Caffe, Caffe2, PyTorch,TensorRT, Dlib, NCNN, Tensorflow, XGBoost and TSNE

Cooperative primitives for CUDA C++.

Paperspace API for python

GraphVite: A General and High-performance Graph Embedding System

Concurrent CPU-GPU Programming using Task Models

CARLsim is an efficient, easy-to-use, GPU-accelerated software framework for simulating large-scale spiking neural network (SNN) models with a high degree of biological detail.

GPU Accelerated t-SNE for CUDA with Python bindings

Fast Clojure Matrix Library

Some CUDA design patterns and a bit of template magic for CUDA

Julia wrapper for CUDA runtime API

Automatic parallelization of Python/NumPy, C, and C++ codes on Linux and MacOSX

An extensible HPC framework for CUDA, OpenCL and native CPU.

A Python wrapper of NVIDIA Video Loader (NVVL) with CuPy for fast video loading with Python

Numeric Erlang - vector and matrix operations with CUDA. Heavily inspired by Pteracuda - https://github.com/kevsmith/pteracuda

💥💻💥 A data-parallel functional programming language

Performance-optimized wheels for TensorFlow (SSE, AVX, FMA, XLA, MPI)

A library for high performance deep learning inference on NVIDIA GPUs.

The C++ Standard Library for your entire system.

A real time optical flow algorithm implemented on GPU

A GPU benchmark tool for evaluating GPUs on mixed operational intensity kernels (CUDA, OpenCL, HIP, SYCL)

🐜 A CUDA based miner for Monero

A structured light scanner

JCuda - Java bindings for CUDA

oneAPI Math Kernel Library (oneMKL) Interfaces

Build userspace NVMe drivers and storage applications with CUDA support

gmonitor is a GPU monitor (Nvidia only at the moment)

Build and run Docker containers leveraging NVIDIA GPUs

Burstcoin C++ CPU and GPU Miner

OpenCL integration for Python, plus shiny features

Python interface to GPU-powered libraries

The purpose of this tutorial is to learn how to install and prepare TensorFlow framework to train your own convolutional neural network object detection classifier for multiple objects, starting from scratch

QUDA is a library for performing calculations in lattice QCD on GPUs.

Collective Knowledge workflow for Caffe to automate installation across diverse platforms and to collaboratively evaluate and optimize Caffe-based workloads across diverse hardware, software and data sets (compilers, libraries, tools, models, inputs):

A simple GPU hash table implemented in CUDA using lock free techniques