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blog-posts

This repository hosts all the blog posts on machine learning, deep learning and statistical modeling.

ceanfft-simulation-with-cuda-based-programming-language-

Sample: particles Minimum spec: SM 2.0 This sample uses CUDA to simulate and visualize a large set of particles and their physical interaction. Adding "-particles=<N>" to the command line will allow users to set # of particles for simulation. This example implements a uniform grid data structure using either atomic operations or a fast radix sort from the Thrust library Key concepts: Graphics Interop Data Parallel Algorithms Physically-Based Simulation Performance Strategies

ctakes-xml-parsing

fluidsd3d9-simulation-with-cuda-based-programming-language-

Sample: fluidsD3D9 Minimum spec: SM 2.0 An example of fluid simulation using CUDA and CUFFT, with Direct3D 9 rendering. A Direct3D Capable device is required. Key concepts: Graphics Interop CUFFT Library Physically-Based Simulation

fluidsgl-simulation-with-cuda-based-programming-language-

Sample: fluidsGL Minimum spec: SM 2.0 An example of fluid simulation using CUDA and CUFFT, with OpenGL rendering. Key concepts: Graphics Interop CUFFT Library Physically-Based Simulation

functional-programming-principles-in-scala

Coursera course for learning scala

javafx-examples

JavaFX Example Projects

jira_automation

jira automation

n-body-simulation-with-cuda-based-programming-language-

nbody

Sample: nbody Minimum spec: SM 2.0 This sample demonstrates efficient all-pairs simulation of a gravitational n-body simulation in CUDA. This sample accompanies the GPU Gems 3 chapter "Fast N-Body Simulation with CUDA". With CUDA 5.5, performance on Tesla K20c has increased to over 1.8TFLOP/s single precision. Double Performance has also improved on all Kepler and Fermi GPU architectures as well. Starting in CUDA 4.0, the nBody sample has been updated to take advantage of new features to easily scale the n-body simulation across multiple GPUs in a single PC. Adding "-numbodies=<bodies>" to the command line will allow users to set # of bodies for simulation. Adding “-numdevices=<N>” to the command line option will cause the sample to use N devices (if available) for simulation. In this mode, the position and velocity data for all bodies are read from system memory using “zero copy” rather than from device memory. For a small number of devices (4 or fewer) and a large enough number of bodies, bandwidth is not a bottleneck so we can achieve strong scaling across these devices. Key concepts: Graphics Interop Data Parallel Algorithms

oceanfft-simulation-with-cuda-based-programming-language-

Sample: oceanFFT Minimum spec: SM 2.0 This sample simulates an Ocean height field using CUFFT Library and renders the result using OpenGL. Key concepts: Graphics Interop Image Processing CUFFT Library

particles-simulation-with-cuda-based-programming-language-

This sample uses CUDA to simulate and visualize a large set of particles and their physical interaction. Adding "-particles=<N>" to the command line will allow users to set # of particles for simulation. This example implements a uniform grid data structure using either atomic operations or a fast radix sort from the Thrust library

simulations-using-cuda-based-programming-language

smokeparticles-simulation-with-cuda-based-programming-language-

Sample: smokeParticles Minimum spec: SM 2.0 Smoke simulation with volumetric shadows using half-angle slicing technique. Uses CUDA for procedural simulation, Thrust Library for sorting algorithms, and OpenGL for graphics rendering. Key concepts: Graphics Interop Data Parallel Algorithms Physically-Based Simulation

swot

Identify email addresses or domains names that belong to colleges or universities. Help automate the process of approving or rejecting academic discounts.

vflockingd3d10-simulation-with-cuda-based-programming-language-

Sample: VFlockingD3D10 Minimum spec: SM 2.0 The sample models formation of V-shaped flocks by big birds, such as geese and cranes. The algorithms of such flocking are borrowed from the paper "V-like formations in flocks of artificial birds" from Artificial Life, Vol. 14, No. 2, 2008. The sample has CPU- and GPU-based implementations. Press 'g' to toggle between them. The GPU-based simulation works many times faster than the CPU-based one. The printout in the console window reports the simulation time per step. Press 'r' to reset the initial distribution of birds. Key concepts: Graphics Interop Data Parallel Algorithms Physically-Based Simulation Performance Strategies

winutils

Windows binaries for Hadoop versions (built from the git commit ID used for the ASF relase)