Search Results for "gpu processing"

...It can process photographs captured by drones, balloons, kites, and other aerial platforms. Outputs include classified point clouds, textured 3D models, georeferenced orthophotos, and digital elevation models. The processing pipeline integrates specialized open-source projects for reconstruction, meshing, geospatial conversion, and point-cloud processing. ODM runs on Windows, macOS, and Linux and is commonly deployed through Docker. GPU acceleration is available for selected feature-extraction workloads. NodeODM can expose the processing engine over a network for integration with other applications.

Scalene is a high-performance CPU, GPU and memory profiler for Python that does a number of things that other Python profilers do not and cannot do. It runs orders of magnitude faster than other profilers while delivering far more detailed information. Once Scalene has profiled your program, it will launch a web browser with an interactive user interface (all processing is done locally).

DistributedCL is a middleware that provides location-transparent GPU processing to applications developed using the OpenCL API. Moved to GitHub. https://github.com/andrelrt/distributedcl

Documentation, simulator, compiler, and Verilog implementation of a completely open-architecture graphics processing unit. This design is intended for academic and commercial purposes. The first step is to develop a detailed GPU simulator and compiler. The second step is to implement the GPU in synthesizable Verilog. The third step is to develop a feedback loop between the simulator and implementation, allowing power, performance, and reliability aspects of the hardware to feed back into ever more detailed and accurate simulations of a complete GPU. ...

The GWO library is numerical calculation library for the diffraction integrals using a GPU (Graphics Processing Unit). If optics engineers and researchers have no knowledge of GPU, the GWO library provides them with the GPU calculation power easily.

This project uses massively parallel Graphics Processing Units(GPU) for neural network(Backpropagation) purposes.