NeuralCloud Blog

We have built API's on a parallel bare metal cluster in seconds that you can use for Hadoop and other HPC apps.

Our focus & solutions are: Oil & Gas Exploration, 3D Imaging, 3D Analytics, digital forensics, cyber triage, cyber treats and malware Analytics.

IIf you understand behavior of an ANN (Artificial Neural Network) API app or whether you are a software programmer or algorithm developer with Accelerated & Multicore Simulations with experience using Matlab & Simulink, CUDA or OpenCL you maybe interested in running your application in our cloud. Please contact us at: info@neuralcloud.org

 

 

Blog Post

GPU Computing: The Revolution!!!

Solve a problem more quickly. Parallel processing would be faster, but the learning curve is steep – isn't it?
Not anymore. With CUDA, you can send C, C++ and Fortran code straight to GPU, no assembly language required.
Developers at companies such as Adobe, ANSYS, Autodesk, MathWorks and Wolfram Research are waking that sleeping giant – the GPU -- to do general-purpose scientific and engineering computing across a range of platforms.
Using high-level languages, GPU-accelerated applicatio…

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blog post

Find more complex relationships in your data
IBM® SPSS® Neural Networks software offers nonlinear data modeling procedures that enable you to discover more complex relationships in your data. The software lets you set the conditions under which the network learns. You can control the training stopping rules and network architecture, or let the procedure automatically choose the architecture for you.
With SPSS Neural Networks software, you can develop more accurate and effective predictive mo…

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Using Matlab to call Python std's

There is a simple strategy that should do: first, figure out how to call python from stand-alone C functions, and then use that code within a mex function. While tedious, at least this is straightforward. By using the not terribly complicated PyObject stuff we can create python objects in C, send them to python functions, and unpack whatever the python functions give us back. However, everything goes bad if we try to import numpy in our python code. We’ll get an error that looks like this:…

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GPU Computing: The Revolution!!!

Solve a problem more quickly. Parallel processing would be faster, but the learning curve is steep – isn't it?
Not anymore. With CUDA, you can send C, C++ and Fortran code straight to GPU, no assembly language required.
Developers at companies such as Adobe, ANSYS, Autodesk, MathWorks and Wolfram Research are waking that sleeping giant – the GPU -- to do general-purpose scientific and engineering computing across a range of platforms.
Using high-level languages, GPU-accelerated applications run the sequential part of their workload on the CPU – which is optimized for single-threaded performance – while accelerating parallel processing on the GPU. This is called "GPU computing."
GPU computing is possible because today's GPU does much more than render graphics: It sizzles with a teraflop of floating point performance and crunches application tasks designed for anything from finance to medicine.
CUDA is widely deployed through thousands of applications and published research papers and supported by an installed base of over 375 million CUDA-enabled GPUs in notebooks, workstations, compute clusters and supercomputers.
- See more at: http://www.nvidia.com/object/cuda_home_new.html#sthash.q3lhDItx.dpuf

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