Large Scale Flocking and Obstacle Avoidance Simulation Using CUDA
Categories: NVIDIA CUDA Tags: cuda, flocking, gpu, Instinct Technology, NVIDIA, simulation
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ATI and NVIDIA GDC 2009 Presentations Available!
Categories: ATI Stream, Game Development, Microsoft DirectX, NVIDIA CUDA, NVIDIA PhysX, OpenGL Tags: apex, ATI, ati stream, direct3d, directx 10, directx 11, game development, GDC 2009, NVIDIA, physx, presentation, Programming, shader model 5, tesselation
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Awesome: Real Time 3D Fluid and Particle Simulation and Rendering
Categories: GPU, NVIDIA CUDA, NVIDIA PhysX Tags: apex, cuda, fluid, gpu, NVIDIA, particle, physics, simulation
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CUDA Voxel Rendering Engine
Categories: NVIDIA CUDA Tags: cuda, Programming, ray casting, rendering engine, voxel, voxlap
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GPU RayCaster Demo Using NVIDIA CUDA
Categories: Game Development, NVIDIA CUDA, Programming Tags: Computing Capability, cuda, demo, game development, gpu, kd-tree, Programming, ray casting, ray tracing
Demo with post-processing effect
Demo without post-processing effect
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SIGGRAPH 2008 Asia: A Stack of PDF Available on GPGPU Programming
Categories: ATI Stream, NVIDIA CUDA, OpenCL, Programming Tags: ati stream, cuda, directx11 compute shader, gpgpu, gpu, opencl, Siggraph 2008
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How to Solve Black-Scholes and Monte Carlo Equations With CUDA
Categories: NVIDIA CUDA Tags: Black-Scholes, cuda, financial equation, Monte Carlo, Programming
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Bullet 2.73 Physics Engine Uses CUDA
Categories: Game Development, NVIDIA CUDA, SDK Tags: bullet, cuda, engine, game development, physics, Programming, sdk
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Interactive Ray Tracing with CUDA and OpenGL
Categories: Game Development, NVIDIA CUDA, OpenGL Tags: cuda, game development, hybrid, opengl, Programming, ray tracing
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Programming a Matrix Multiplication for GPUs with CUDA
Categories: NVIDIA CUDA, Programming Tags: cuda, cudaFree, cudaMalloc, cudaMemcpy, gpu, NVIDIA, Programming
CUDA makes it possible to program the GPU with the language C. This article will show you the steps to code a matrix multiplication routine in CUDA:
- allocate memory on the GPU with cudaMalloc or cudaMallocPitch (for aligned memory allocation)
- move data to the GPU with cudaMemcpy2D
- select the kernel domain, write the kernel and run it
- move results back from the GPU to the host with cudaMemcpy2D
- free resources with cudaFree
NVIDIA GeForce Graphics Cards Used to Break WIFI Passwords
Categories: Graphics Cards, NVIDIA CUDA Tags: crack, cuda, geforce, gpgpu, graphics card, NVIDIA, wifi, wpa, wpa2
With graphics cards such the GeForce GTX 280 and parallel brute force based algorithms coded in CUDA (GPGPU techniques), WiFi’s WPA (RC4 based) and WPA2 (AES based) encryption systems are now crackable and the time to crack them compared to CPU based algorithms is reduced by a factor of 100 (or 10,000 %).
[source]
Update (2008-10-16)
SIGGRAPH 2008 Presentations: Programming with CUDA
Categories: NVIDIA CUDA, Programming Tags: cuda, image filtering, image processing, nvcuvid, NVIDIA, parallel computing, Programming, stream computing, video
NVIDIA has released two SIGGRAPH 2008 presentations about CUDA. The first one talks about image processing and video algorithms with CUDA and shows some CUDA applications such as image filtering (sobel filter with code sample). This presentation talks also about NVCUVID, the video extension for CUDA. NVCUVID is similar to DXVA API, but is platform OS independent.
The second presentation is more general about CUDA programming and shows how to create high performance code to run on the millions of CUDA-capable GPUs already in use.
Links:
NVIDIA’s GT200: Inside a Parallel Processor
Categories: Graphics Cards, NVIDIA CUDA Tags: architecture, cuda, die, gpu, graphics card, gt200, NVIDIA, parallel, processor
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CUDA 2.0 Available
Categories: NVIDIA CUDA, Programming Tags: cuda, cuda 2.0, geforce, gpu, gpu computing, NVIDIA, Programming
CUDA 2.0 is available here: CUDA Zone. To take advantage of CUDA, you need Forceware 177.84 or better (177.89). Samples work with GeForce 8/9 series.
NVIDIA CUDA technology is the world’s only C language environment that enables programmers and developers to write software to solve complex computational problems in a fraction of the time by tapping into the many-core parallel processing power of GPUs.
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