Fast Interactive Sand Simulation for Gesture Tracking systems Shrenik Lad
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1 Fast Interactive Sand Simulation for Gesture Tracking systems Shrenik Lad Project Guide : Vivek Mehta, Anup Tapadia TouchMagix media labs
2 TouchMagix Interactive display solutions Interactive Wall, Floor etc Gesture and Touch based technologies Advertising, branding, gaming, events
3 Particle Systems Used for simulating physical systems Fluid simulation, smoke and fire for games, molecular dynamics etc Main disadvantage :- requires LARGE NUMBER OF PARTICLES
4 Some Examples
5 Interaction Particles move according to the interaction Interaction can be through keyboard, mouse, touch or even gestures!! Points of Interaction continuously given to the application.
6 Multi Point Interaction When MULTI-POINT interaction, for ex gestures, lots of computations to be done per frame. Result :- Realistic effects not obtained
7 Normal CPUs not capable of doing such computations. Fps obtained :- 4-5 fps on a dual core machine at 2 lac particles TOO SLOW!!
8 Multithreading on CPU Multi-threading on CPU would execute at most 4-5 threads in parallel But we have lacs of particles Instead, do massive parallel computations on GPU
9 Solution Use GPU for general purpose computations (GPGPU) AIM :- Achieve good fps on regular Graphics cards Reduce CPU usage to minimum Performance dependent on GPU and not CPU
10 GPU representation
11 Technologies OpenCL for GPGPU (works on both Nvidia and AMD platform) We use Nvidia platform (CUDA architechture) OpenGL GPU used:- Nvidia GeForce 210, 16 CUDA cores
12 Functionalities 2 functionalities to be implemented Find position for each particle in space Scatter particles which are near the points of interaction Both are embarrassingly parallel!!
13 Approach Transfer all data to device (in our case, GPU) memory Do parallel computations for the 2 functionalities Copy results back to CPU memory
14 GPGPU approach
15 Result Very poor!! 2-3 fps Fps less than the counterpart CPU version Reason??
16 Reason Swapping of memory between host and device taking too much time. Time saved in computations has no effect on performance What about CPU and GPU usage??
17 CPU and GPU usage CPU usage comparatively less GPU usage around 30-40% Optimizations need to be done to improve performance
18 Optimizations
19 Optimization 1 Transfer only those memory which is needed No unnecessary memory transfer Result : fps
20 Good Increase in fps but minimum fps required for human eyes 30 fps
21 Drawing Particles
22 Traditional drawing
23 Drawing Particles drawn by GL_QUADS and GL_VERTEX function calls If 4 lac particles, 16 lac GL_VERTEX function calls Too many function calls affecting the performance
24 Optimization 2 Use Vertex Arrays
25 Use Vertex Arrays Just one Function call for drawing all particles Give all particles data in one shot!! Very efficient when large number of particles
26 Result Fps around 20, almost DOUBLED CPU usage decreases further But Fps still needs to reach 30
27 Optimization 3 A very important one!!
28 No Swapping of memory
29 Shared Memory Use shared memory between GL and CL VBOs (vertex buffer objects), stored in video card memory Both Host(CPU) and Device(GPU) would be using the same memory, no transfer
30 Last Optimization Use Point Sprites
31 Point Sprites Instead of 1 Quadrilateral for each particle, use a POINT Point Sprites are hardware accelerated Can be textured Ideal for creating high performance particle systems
32 Final Result 4 lac particles, 30 fps on a 16 CUDA core GPU 15-20x faster than the CPU version GPU usage reduces to minimum, CPU usage increases (no idea why.) Physical Memory constant
33 Performance scaling Things become simple now Replace the GPU with a better one, increase the number of particles and the performance is still better!! On a 32 CUDA core GPU, fps of 50 at 4 lac particles. CPU version hangs when more than 2 lac particles.
34 FPS Particles Avg FPS on devices
35 Conclusion
36 Conclusion Large particle systems can be simulated at interactive rates. CPU free for other intensive tasks, can use it for other independent computations
37 Thank You
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