Showing 2 open source projects for "gpu"

View related business solutions
  • Our Free Plans just got better! | Auth0 Icon
    Our Free Plans just got better! | Auth0

    With up to 25k MAUs and unlimited Okta connections, our Free Plan lets you focus on what you do best—building great apps.

    You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
    Try free now
  • Create and run cloud-based virtual machines. Icon
    Create and run cloud-based virtual machines.

    Secure and customizable compute service that lets you create and run virtual machines.

    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.
    Try for free
  • 1

    viewpix

    A Landsat 8 scene viewer

    ...A 30m resolution gray scale scene is generated by Viewpix and is presented as band 12. Each Landsat 8 scene is roughly 190 X 180 kilometers. Viewpix was originally written as a simple platform to test GPU software on low end computer systems. This is the "lite" version of Viewpix as it has no cuda filters installed. Viewpix is written in C. It is tested on Ubuntu and Centos. It should work with most current Linux distributions. Viewpix runs efficiently on computers with modest CPU's such as low power mini-ITX boards. It is known to work nicely on Intel NUC, AMD A6 SOC , and NVIDIA TX2. ...
    Downloads: 2 This Week
    Last Update:
    See Project
  • 2
    Particular filter CUDA

    Particular filter CUDA

    Improvements of positioning algorithms using CUDA

    Our project consist in porting positioning algorithms on a GPU. We will improve programs which are already working on CPU in order to make them compatible with the CUDA technology offered by Nvidia. The advantage of this technology is that it allows us to use massive multithreading and so make calculations go faster. Algorithms will be implemented in C++.
    Downloads: 0 This Week
    Last Update:
    See Project
  • Previous
  • You're on page 1
  • Next