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This project consists of neurodynamic simulation software, for large scale associative memories and self-organizing competetive nets. NVIDIA's CUDA library is used for acceleration. Written in C++ and meant for a Unix-like environment.
A data parallel scientific programming model. Compiles efficiently to different platforms like distributed memory (MPI), shared memory multi-processor (pthreads), Cell BE processor, Nvidia Cuda, SIMD vectorization (SSE, Altivec), and sequential C++ code.
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SIGGARA (Simple GPU Accelerated Realtime RAytracing Renderer) is a simple rendering engine implementing a bunch of raytracing algorithms. Later we will use CUDA to render directly on the Graphicscard GPU
this is a small project which provides several mathematical function useful for a chemist or somebody working with mass specs.
The goal is to provide several cuda and c based functions which can be easily accessed using java, groovy and python.
A low code unified framework for computer vision and deep learning
Monk is an open source low code programming environment to reduce the cognitive load faced by entry level programmers while catering to the needs of Expert Deep Learning engineers.
There are three libraries in this opensource set.
- Monk Classiciation- https://monkai.org. A Unified wrapper over major deep learning frameworks. Our core focus area is at the intersection of Computer Vision and Deep Learning algorithms.
- Monk Object Detection -...
Blit contains a group of highly efficient iterative sparse solvers that can handle multiple right-hand-sides (i.e. block solvers). We will implement BLQMR, BLGMRES and other block algorithms in MATLAB, FORTRAN 90, C/C++, CUDA and OpenCL.