Search Results for "gpu benchmark"
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Showing 14 open source projects for "gpu benchmark"
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XMRig
RandomX, KawPow, CryptoNight, AstroBWT and GhostRider unified miner
High performance, open-source, cross-platform RandomX, KawPow, CryptoNight, and AstroBWT CPU/GPU miner, RandomX benchmark, and stratum proxy. XMRig is a high-performance, open-source, cross-platform RandomX, KawPow, CryptoNight, and AstroBWT unified CPU/GPU miner and RandomX benchmark. Official binaries are available for Windows, Linux, macOS, and FreeBSD. The preferred way to configure the miner is the JSON config file as it is more flexible and human-friendly. The command-line interface does... -
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AlphaZero.jl
A generic, simple and fast implementation of Deepmind's AlphaZero
...++) and optimized for highly distributed computing environments. This makes them hardly accessible for students, researchers and hackers. Many simple Python implementations can be found on Github, but none of them is able to beat a reasonable baseline on games such as Othello or Connect Four. As an illustration, the benchmark in the README of the most popular of them only features a random baseline, along with a greedy baseline that does not appear to be significantly stronger. -
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SSD in PyTorch 1.0
High quality, fast, modular reference implementation of SSD in PyTorch
This repository implements SSD (Single Shot MultiBox Detector). The implementation is heavily influenced by the projects ssd.pytorch, pytorch-ssd and maskrcnn-benchmark. This repository aims to be the code base for research based on SSD. Multi-GPU training and inference: We use DistributedDataParallel, you can train or test with arbitrary GPU(s), the training schema will change accordingly. Add your own modules without pain. We abstract backbone, Detector, BoxHead, BoxPredictor, etc. You can... -
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PyTorch Geometric
Geometric deep learning extension library for PyTorch
It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of an easy-to-use mini-batch loader for many small and single giant graphs, a large number of common benchmark datasets (based on simple interfaces to create your own), and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds. We have outsourced a lot... -
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PyG
Graph Neural Network Library for PyTorch
PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data. It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of easy-to-use mini-batch loaders for operating on many small and single giant graphs, multi GPU-support, DataPipe support, distributed... -
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BCI
BCI: Breast Cancer Immunohistochemical Image Generation
...) to B(IHC images). The evaluation of human epidermal growth factor receptor 2 (HER2) expression is essential to formulate a precise treatment for breast cancer. The routine evaluation of HER2 is conducted with immunohistochemical techniques (IHC), which is very expensive. Therefore, for the first time, we propose a breast cancer immunohistochemical (BCI) benchmark attempting to synthesize IHC data directly with the paired hematoxylin and eosin (HE) stained images. -
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PyTorch Geometric Temporal
Spatiotemporal Signal Processing with Neural Machine Learning Models
The library consists of various dynamic and temporal geometric deep learning, embedding, and Spatio-temporal regression methods from a variety of published research papers. Moreover, it comes with an easy-to-use dataset loader, train-test splitter and temporal snaphot iterator for dynamic and temporal graphs. The framework naturally provides GPU support. It also comes with a number of benchmark datasets from the epidemiological forecasting, sharing economy, energy production and web traffic... -
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Detectron2
Next-generation platform for object detection and segmentation
Detectron2 is Facebook AI Research's next generation software system that implements state-of-the-art object detection algorithms. It is a ground-up rewrite of the previous version, Detectron, and it originates from maskrcnn-benchmark. It is powered by the PyTorch deep learning framework. Includes more features such as panoptic segmentation, Densepose, Cascade R-CNN, rotated bounding boxes, PointRend, DeepLab, etc. Can be used as a library to support different projects on top of it. We'll open... -
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PictorTools (Cloud3D Bench)
An interactive 3D benchmark suite for cloud graphics rendering
Cloud3D Bench is expected to be a standard and free benchmark suite for interactive graphics rendering research in the cloud. Cloud3D Bench 0.1 is our first attempt. To keep pace with the fast evolution of modern 3D workloads, we will keep enriching and updating this benchmark suite with more representative 3D applications on the Linux platform. Application Area | BenchmarkGame ---------------------------------------------------- Game: Racing -
Computing infrastructure in predefined or custom machine sizes to accelerate your cloud transformation. General purpose (E2, N1, N2, N2D) machines provide a good balance of price and performance. Compute optimized (C2) machines offer high-end vCPU performance for compute-intensive workloads. Memory optimized (M2) machines offer the highest memory and are great for in-memory databases. Accelerator optimized (A2) machines are based on the A100 GPU, for very demanding applications.
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Dopamine
Framework for prototyping of reinforcement learning algorithms
Dopamine is a research framework for fast prototyping of reinforcement learning algorithms. It aims to fill the need for a small, easily grokked codebase in which users can freely experiment with wild ideas (speculative research). This first version focuses on supporting the state-of-the-art, single-GPU Rainbow agent (Hessel et al., 2018) applied to Atari 2600 game-playing (Bellemare et al., 2013). Specifically, our Rainbow agent implements the three components identified as most important... -
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AutoBench
This program is a benchmark site data extraction util program
This program is a program that extracts the latest CPU, GPU, Drive and RAM performance scores and rankings from benchmark sites. The Output Data is saved as a csv, xlsx and xls file. CPU information is written by model name and score. GPU information is written by model name and score. Drive information is written by model name and score. RAM information is written by model name and score. -
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CUDA-Quicksort
CUDA-Quicksort: A GPU-based implementation of the quicksort algorithm
... of the quicksort algorithm. "Experiments performed on six sorting benchmark distributions show that CUDA-quicksort is up to four times faster than GPU-quicksort and up to three times faster than CDP-quicksort."[*]. *Copyright © 2015 John Wiley & Sons, Ltd. Concurrency Computat.: Pract. Exper. (2015) DOI: 10.1002/cpe.3611 For further information, please see the corresponding publication: http://onlinelibrary.wiley.com/doi/10.1002/cpe.3611/abstract -
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GPUBench2 is a cross platform suite to analyze the performance of GPU( Graphics Processing Unit). GPUBench2 will gather of state-of-the-art parameters from different interfaces ( OpenGL, Cg, CUDA) available.
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FFFF is the fastest Win32/OSX/Linux/IRIX Mandelbrot generator. Features OpenGL, realtime zoom, SSE/AltiVec QuadPixel, SSE2/3DNow! DualPixel calc, FPU per pixel calc, GPU asm (Fragment/Vertex) calc, multiprocessor support, and benchmarking. Opt asm code!
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