Faculté Polytechnique
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1 Faculté Polytechnique CHAPTER 6 : GPU PROGRAMMING APPLICATION : MULTI-CPU-GPU BASED IMAGE AND VIDEO PROCESSING Sidi Ahmed Mahmoudi sidi.mahmoudi@umons.ac.be 11 Mars 2015
2 PLAN Introduction I. GPU Presentation II. III. IV. GPU Programming Exploitation of Multi-CPU-GPU Architectures Application for Multimedia processing 1. Multi-CPU/Multi-GPU based image processing 2. Multi-GPU based video processing in Real Time V. Multi-CPU/Multi-GPU based framework for HD image and video processing VI. Experimental Results Conclusion Université de Mons Sidi Ahmed Mahmoudi Computer Science Department. Faculté polytechnique 2
3 PLAN Introduction I. GPU Presentation II. III. IV. GPU Programming Exploitation of Multi-CPU-GPU Architectures Application for Multimedia processing 1. Multi-CPU/Multi-GPU based image processing 2. Multi-GPU based video processing in Real Time V. Multi-CPU/Multi-GPU based framework for HD image and video processing VI. Experimental Results Conclusion Université de Mons Sidi Ahmed Mahmoudi Computer Science Department. Faculté polytechnique 3
4 INTRODUCTION GPU PRESENTATION GPU PROGRAMMING MULTI-CPU-GPU PROGRAMMING MULTIMEDIA PROCESSING APPLICATIONS EXPERIMENTATIONS CONCLUSION Introduction Moore s Law : 1968 The number of transistors in an integrated circuit doubles approximately every two years Law well verified :the CPU power has doubled every 18 months CPU power capped to 4 GHZ : the law is no more verified Computer Science Department. Faculté polytechnique 4
5 INTRODUCTION GPU PRESENTATION GPU PROGRAMMING MULTI-CPU-GPU PROGRAMMING MULTIMEDIA PROCESSING APPLICATIONS EXPERIMENTATIONS CONCLUSION Introduction Multiplication of computing units in CPU : Multi-CPU, Multi-core, GPU GPU : initially used in 3D and video games. CPU Intel Core i7 3770K (2012) GPU GeForce GTX 580 (2011) 4 cores/8 threads 320 Euros 512 CUDA cores 350 Euros Birth of GPGPU : General Purpose Graphic Processing Unit Université de Mons Sidi Ahmed Mahmoudi Computer Science Department. Faculté polytechnique 5
6 PLAN Introduction I. GPU Presentation II. III. IV. GPU Programming Exploitation of Multi-CPU-GPU Architectures Application for Multimedia processing 1. Multi-CPU/Multi-GPU based image processing 2. Multi-GPU based video processing in Real Time V. Multi-CPU/Multi-GPU based framework for HD image and video processing VI. Experimental Results Conclusion Université de Mons Sidi Ahmed Mahmoudi Computer Science Department. Faculté polytechnique 6
7 INTRODUCTION GPU PRESENTATION GPU PROGRAMMING MULTI-CPU-GPU PROGRAMMING MULTIMEDIA PROCESSING APPLICATIONS EXPERIMENTATIONS CONCLUSION Graphic processing units GPU Presentation NVIDIA ATI Integrated within graphic cards GeForce GTX 580 Radeon HD4870 Initially used for 3D rendering and video games Computer Science Department. Faculté polytechnique 7
8 INTRODUCTION GPU PRESENTATION GPU PROGRAMMING MULTI-CPU-GPU PROGRAMMING MULTIMEDIA PROCESSING APPLICATIONS EXPERIMENTATIONS CONCLUSION GPU Presentation GPU specialized for massively parallel treatments More transistors dedicated for computing Less number of control and cache units. GPU: Multiplication of computing units Université de Mons Sidi Ahmed Mahmoudi Computer Science Department. Faculté polytechnique 8
9 INTRODUCTION GPU PRESENTATION GPU PROGRAMMING MULTI-CPU-GPU PROGRAMMING MULTIMEDIA PROCESSING APPLICATIONS EXPERIMENTATIONS CONCLUSION GPU Presentation Computer Science Department. Faculté polytechnique 9
10 INTRODUCTION GPU PRESENTATION GPU PROGRAMMING MULTI-CPU-GPU PROGRAMMING MULTIMEDIA PROCESSING APPLICATIONS EXPERIMENTATIONS CONCLUSION GPU Presentation Power evolution of CPU Intel vs. GPU Nvidia Computer Science Department. Faculté polytechnique 10
11 INTRODUCTION GPU PRESENTATION GPU PROGRAMMING MULTI-CPU-GPU PROGRAMMING MULTIMEDIA PROCESSING APPLICATIONS EXPERIMENTATIONS CONCLUSION GPU Presentation Bandwidth evolution of CPU Intel vs. GPU Nvidia Computer Science Department. Faculté polytechnique 11
12 INTRODUCTION GPU PRESENTATION GPU PROGRAMMING MULTI-CPU-GPU PROGRAMMING MULTIMEDIA PROCESSING APPLICATIONS EXPERIMENTATIONS CONCLUSION GPU Presentation TOP 500. Current List, June Computer Science Department. Faculté polytechnique 12
13 PLAN Introduction I. GPU Presentation II. III. IV. GPU Programming Exploitation of Multi-CPU-GPU Architectures Application for Multimedia processing 1. Multi-CPU/Multi-GPU based image processing 2. Multi-GPU based video processing in Real Time V. Multi-CPU/Multi-GPU based framework for HD image and video processing VI. Experimental Results Conclusion Université de Mons Sidi Ahmed Mahmoudi Computer Science Department. Faculté polytechnique 13
14 GPU Programming Brook GPU : since 2004 ATI Stream : for ATI graphic cards DirectX 11, OpenGL : GPGPU shaders OpenCL : compatible with both NVIDIA and ATI CUDA : for NVIDIA cards only Computer Science Department. Faculté polytechnique 14
15 OpenCL Launched by Apple in order to unify the use of multi-cores and GPUs Joint by AMD/ATI and NVIDIA First version in 2009 Compatible with both NVIDIA and Ati cards Newer than CUDA : less powerful!! New GPU data types (vector, image, etc.). Computer Science Department. Faculté polytechnique 15
16 CUDA Compatible with GPU NVIDIA. Syntax similar to C. Grid: set of blocs. Bloc: set of threads. Threads synchronized within shared memory Management of different GPU memories Computer Science Department. Faculté polytechnique 16
17 CUDA Example of vectors addition : global void vecadd(float* A, float* B, float* C) { int i = threadidx.x; C[i] = A[i] + B[i]; } Kernel int main() { // Kernel invocation vecadd<<<1, N>>>(A, B, C); } Call of Kernel Computer Science Department. Faculté polytechnique 17
18 CUDA vecadd<<<1, N>>>(A, B, C); vecadd<<<2, N/2>>>(A, B, C); vecadd<<<100, N/100>>>(A, B, C); 1 Bloc of N threads 2 Blocs of N/2 threads 100 Blocs of N/100 threads Computer Science Department. Faculté polytechnique 18
19 CUDA A CUDA program consists of five steps : 1. Memory allocation on GPU 2. Data transfer from CPU to GPU memory 3. Definition of the number of CUDA threads 4. Launching of CUDA functions in parallel 5. Data (result) transfer from GPU to CPU memory Computer Science Department. Faculté polytechnique 19
20 CUDA Example of matrix addition: Memory allocation Memory allocation on GPU The allocated memory will receive data from central (CPU) memory. float *Ad, *Bd, *Cd; const int size = N*N*sizeof(float); cudamalloc((void **)&A_d, size); cudamalloc((void **)&B_d, size); cudamalloc((void **)&C_d, size); // matrix size // Allocate matrix A_d // Allocate matrix B_d // Allocate matrix C_d Computer Science Department. Faculté polytechnique 20
21 CUDA Example of matrix addition : data transfer (CPU Mem to GPU Mem) Data transfer from CPU memory to GPU memory Matrix initialized on central memory Precise the type of transfer : HostToDevice cudamemcpy(a_d, A, size, cudamemcpyhosttodevice); cudamemcpy(b_d, B, size, cudamemcpyhosttodevice); // A and B represent the matrix initialized on CPU memory Computer Science Department. Faculté polytechnique 21
22 CUDA Example of matrix addition : define the number of threads Define the number of GPU threads : Define the grid size Define the bloc size The threads number depends from the size of data to treat dim3 dimblock( blocksize, blocksize ); dim3 dimgrid( N/dimBlock.x, N/dimBlock.y); // Bloc size // Grid size Computer Science Department. Faculté polytechnique 22
23 CUDA Example of matrix addition : launching of CUDA kernels Define the CUDA function (kernel) Call and execute the CUDA kernel Specify the threads number (already defined) global void add matrix( float* a, float *b, float *c, int N ) { int i = blockidx.x * blockdim.x + threadidx.x; int j = blockidx.y * blockdim.y + threadidx.y; int index = i + j*n; if ( i < N && j < N ) c[index] = a[index] + b[index]; } add_matrix<<<dimgrid, dimblock>>>( A_d, B_d, C_d, N ); Computer Science Department. Faculté polytechnique 23
24 CUDA Example of matrix addition: Results transfer (GPU Mem to CPU Mem) Transfer of results from GPU to CPU memory Precise the type of transfer : DeviceToHost Release the allocated memory on GPU cudamemcpy( C, C_d, size, cudamemcpydevicetohost ); cudafree( A_d ); cudafree( B_d ); cudafree( C_d ); // Results wil be savec on CPU memory «C». Computer Science Department. Faculté polytechnique 24
25 PLAN Introduction I. GPU Presentation II. III. IV. GPU Programming Exploitation of Multi-CPU-GPU Architectures Application for Multimedia processing 1. Multi-CPU/Multi-GPU based image processing 2. Multi-GPU based video processing in Real Time V. Multi-CPU/Multi-GPU based framework for HD image and video processing VI. Experimental Results Conclusion Université de Mons Sidi Ahmed Mahmoudi Computer Science Department. Faculté polytechnique 25
26 Exploitation of Multi-CPU/Multi-GPU Architectures StarPU Developed in LABRI laboratory. Bordeaux, France Simultaneous exploitation of multiple CPUs and GPUs Exploit functions implemented with C, CUDA or OpenCL Efficient scheduling strategies Survey of StarPU [1] Computer Science Department. Faculté polytechnique 26
27 Exploitation of Multi-CPU/Multi-GPU Architectures StarSS Developed at the University of Catalonia (Barcelona) Flexible programming model for multicore Based essentially on : CellSs: programming Cell processors SPMSs: programming SMPs processors ClearSpeedSs: programming Clear Speed processors GPUSs: programming Multi-GPUs ClusterSs: programming of clusters GridSs: programming resources in grids Computer Science Department. Faculté polytechnique 27
28 Exploitation des architectures hétérogènes OpenACC API for high level programming Supported by CAPS enterprise, CRAY Inc, The Portland Group Inc (PGI) and NVIDIA A collection of compilation directives Takes into account the tasks of initialization, launching and stopping of accelerators Provides information for compilers Equivalent to Open MP for CPU programming Computer Science Department. Faculté polytechnique 28
29 PLAN Introduction I. GPU Presentation II. III. IV. GPU Programming Exploitation of Multi-CPU-GPU Architectures Application for Multimedia processing 1. Multi-CPU/Multi-GPU based image processing 2. Multi-GPU based video processing in Real Time V. Multi-CPU/Multi-GPU based framework for HD image and video processing VI. Experimental Results Conclusion Université de Mons Sidi Ahmed Mahmoudi Computer Science Department. Faculté polytechnique 29
30 Intensive treatments Large volumes HD, Full HD, 4K, etc. Multi-CPU Computing time? Real time? Cost? Image processing Video processing Medical imaging Multimedia processing GPU or Multi-GPU Acceleration Parallel computing Hybrid computing CPU or/and GPU? Computer Science Department. Faculté polytechnique 30
31 Application for Multimedia processing Hardware High computing power of GPUs. Heterogeneous architectures (Multi-CPU/Multi-GPU) GPU Université de Mons Sidi Ahmed Mahmoudi Computer Science Department. Faculté polytechnique 31
32 Application for Multimedia processing Hardware High computing power of GPUs. Heterogeneous architectures (Multi-CPU/Multi-GPU) Applications Intensive processing of images and videos intensity : HD, Full HD or 4K of images videos (real time) 200 images 05 minutes Vertebra detection [Lecron2011] Université de Mons Sidi Ahmed Mahmoudi Computer Science Department. Faculté polytechnique 32
33 Application for Multimedia processing Hardware High computing power of GPUs. Heterogeneous architectures (Multi-CPU/Multi-GPU) Applications Intensive processing of videos: motion tracking and recognition. intensity : HD, Full HD or 4K videos to treat in real time 2000 images 10 minutes Images indexation :MediaCycle [Siebert2009] Université de Mons Sidi Ahmed Mahmoudi Computer Science Department. Faculté polytechnique 33
34 Application for Multimedia processing Hardware High computing power of GPUs. Heterogeneous architectures (Multi-CPU/Multi-GPU) Applications Intensive processing of videos: motion tracking and recognition. intensity : HD, Full HD or 4K videos to treat in real time Vidéo : 720x FPS Vidéo : 640x FPS Vidéo : 720x640 8 FPS Computer Science Department. Faculté polytechnique 34
35 Application for Multimedia processing Hardware High computing power of GPUs. Heterogeneous architectures (Multi-CPU/Multi-GPU) Applications Intensive processing of videos: motion tracking and recognition. intensity : HD, Full HD or 4K videos to treat in real time Constraints Transfer times between CPU and GPU memories. Efficient distribution of tasks between multiple GPUs Objectives Efficient management of memories: significant reduction of transfer times Efficient image and video processing on GPU and Multi-GPU platforms Université de Mons Sidi Ahmed Mahmoudi Computer Science Department. Faculté polytechnique 35
36 PLAN Introduction I. GPU Presentation II. III. IV. GPU Programming Exploitation of Multi-CPU-GPU Architectures Application for Multimedia processing 1. Multi-CPU/Multi-GPU based image processing 2. Multi-GPU based video processing in Real Time V. Multi-CPU/Multi-GPU based framework for HD image and video processing VI. Experimental Results Conclusion Université de Mons Sidi Ahmed Mahmoudi Computer Science Department. Faculté polytechnique 36
37 YES GPU i = i +1 CPU INTRODUCTION GPU PRESENTATION GPU PROGRAMMING MULTI-CPU-GPU PROGRAMMING MULTIMEDIA PROCESSING APPLICATIONS EXPERIMENTATIONS CONCLUSION Scheme development : image processing Output Images 3 Input image N Multiple Images Image Unique Image i (i<=n) on GPU 1 1 Image on GPU CUDA Parallel Treatment 2 2 CUDA Parallel Treatment NO I = N 3 FIN OpenGL Visualization Computer Science Department. Faculté polytechnique 37
38 GPU optimizations Single image processing Texture Memory: fast access to pixel values Shared Memory : fast access to neighbors' values Multiple image processing Texture et shared memories Overlapping of data transfers by kernels executions on GPU : CUDA streams Computer Science Department. Faculté polytechnique 38
39 Implemented algorithms on GPU Classic image processing algorithms : Geometrical transformations: o rotation, translation, homography Parallel treatments between image pixels GPU acceleration : from 10x to 100x Homography on GPU Points of interest detection on GPU: Preliminary step of several computer vision methods GPU implementation based on Harris technic Efficiency: invariant to rotation, brightness, etc. Corners detection on GPU Contours detection on GPU: GPU implementation based on Deriche-Canny approach Efficiency: noise robustness, reduced number of operations Good quality of detected contours Contours detection on GPU Computer Science Department. Faculté polytechnique 39
40 Results : Single image processing on GPU Edges and corners detection on GPU : Global Memory Computer Science Department. Faculté polytechnique 40
41 Results : Single image processing on GPU Edges and corners detection on GPU : OpenGL Visualization Computer Science Department. Faculté polytechnique 41
42 Results : Single image processing on GPU Edges and corners detection on GPU : Texture & Shared memories Computer Science Department. Faculté polytechnique 42
43 Results : Multiple images processing on GPU Images resolution : 2048x2048 Edges and corners detection on GPU : Texture & Shared memories Computer Science Department. Faculté polytechnique 43
44 Results : Multiple images processing on GPU Images resolution : 2048x2048 Low accelerations: costs of data transfers between CPU and GPU memories Full exploitation of computing power within heterogeneous (Multi-CPU-GPU) platforms Edges and corners detection on GPU : CUDA streaming Computer Science Department. Faculté polytechnique 44
45 Multiple images processing on heterogeneous platforms StarPU: runtime for exploiting Heterogeneous platforms. Multiple images StarPU buffers Dynamic scheduling GPU N 1... GPU N M CPU N 1... CPU N N CPU Treatment CPU Treatment copy copy copy copy copy copy 4 CUDA streams copy copy Copy to CPU Memory Computer Science Department. Faculté polytechnique 45
46 Heterogeneous treatment : results Résolution d images : 512x512, 1024x1024, 2048x2048, 3936x3936 Edge and corners detection on heterogeneous platforms Computer Science Department. Faculté polytechnique 46
47 Heterogeneous treatment : results Résolution d images : 512x512, 1024x1024, 2048x2048, 3936x3936 Edge and corners detection on heterogeneous platforms Dynamic scheduling Computer Science Department. Faculté polytechnique 47
48 Heterogeneous treatment : results Résolution d images : 512x512, 1024x1024, 2048x2048, 3936x3936 Edge and corners detection on heterogeneous platforms Dynamic scheduling + CUDA streaming Computer Science Department. Faculté polytechnique 48
49 PLAN Introduction I. GPU Presentation II. III. IV. GPU Programming Exploitation of Multi-CPU-GPU Architectures Application for Multimedia processing 1. Multi-CPU/Multi-GPU based image processing 2. Multi-GPU based video processing in Real Time V. Multi-CPU/Multi-GPU based framework for HD image and video processing VI. Experimental Results Conclusion Université de Mons Sidi Ahmed Mahmoudi Computer Science Department. Faculté polytechnique 49
50 Video Processing on GPU : scheme development Université de Mons Sidi Ahmed Mahmoudi Computer Science Department. Faculté polytechnique 50
51 GPU based motion tracking CPU GPU Video OpenCV decoding frames on GPU Corners detection Optical flow computation Noise elimination OpenGL Visualization Université de Mons Sidi Ahmed Mahmoudi Computer Science Department. Faculté polytechnique 51
52 CPU Multi-GPU based motion tracking GPU Video OpenCV decoding frames on GPU GPU 1 GPU 2 GPU 3 GPU 4 Sub-frame N 1 Sub-frame N 2 Sub-frame N 3 Sub-frame N 4 Corners detection Corners detection Corners detection Corners detection Optical flow computing Optical flow computing Optical flow computing Optical flow computing Noise elimination Noise elimination Noise elimination Noise elimination GPU 1 (output video) OpenGL Visualization Université de Mons Sidi Ahmed Mahmoudi Computer Science Department. Faculté polytechnique 52
53 OpenCV CPU 10 FPS INTRODUCTION GPU PRESENTATION GPU PROGRAMMING MULTI-CPU-GPU PROGRAMMING MULTIMEDIA PROCESSING APPLICATIONS EXPERIMENTATIONS CONCLUSION CPU based motion tracking Noise Noise Noise Noise CPU based motion tracking in Full HD videos (1920x1080) Université de Mons Sidi Ahmed Mahmoudi Computer Science Department. Faculté polytechnique 53
54 OpenCV GPU 19 FPS INTRODUCTION GPU PRESENTATION GPU PROGRAMMING MULTI-CPU-GPU PROGRAMMING MULTIMEDIA PROCESSING APPLICATIONS EXPERIMENTATIONS CONCLUSION GPUCV based motion tracking Noise Noise Noise Noise GPUCV based motion tracking in Full HD videos (1920x1080) Université de Mons Sidi Ahmed Mahmoudi Computer Science Department. Faculté polytechnique 54
55 GPU 62 FPS INTRODUCTION GPU PRESENTATION GPU PROGRAMMING MULTI-CPU-GPU PROGRAMMING MULTIMEDIA PROCESSING APPLICATIONS EXPERIMENTATIONS CONCLUSION GPU based motion tracking Noise elim Noise elim Noise elim Noise elim GPU based motion tracking in Full HD videos (1920x1080) Université de Mons Sidi Ahmed Mahmoudi Computer Science Department. Faculté polytechnique 55
56 PLAN Introduction 1. GPU Presentation 2. GPU Programming 3. Exploitation of Multi-CPU-GPU Architectures 4. Application for Multimedia processing 1. Multi-CPU/Multi-GPU based image processing 2. Multi-GPU based video processing in Real Time 1. Multi-CPU/Multi-GPU based framework for HD image and video processing 5. Experimental Results Conclusion Université de Mons Sidi Ahmed Mahmoudi Computer Science Department. Faculté polytechnique 56
57 INTRODUCTION GPU PRESENTATION GPU PROGRAMMING MULTI-CPU-GPU PROGRAMMING MULTIMEDIA PROCESSING APPLICATIONS EXPERIMENTATIONS CONCLUSIO Multi-CPU-GPU based framework Computer Science Department. Faculté polytechnique 57
58 1 st use case : Vertebra segmentation Computer Science Department. Faculté polytechnique 58
59 1 st use case : Vertebra segmentation Performances of heterogeneous vertebra detection using 200 images (1476x1680) Computer Science Department. Faculté polytechnique 59
60 2 nd use case : Movement detection within mobile camera Computer Science Department. Faculté polytechnique 60
61 2 nd use case : Movement detection within mobile camera GPU performance : movement detection within mobile camera Computer Science Department. Faculté polytechnique 61
62 3 rd use case: real time event detection Computer Science Department. Faculté polytechnique 62
63 4 th use case: eyes, nose and frontal face detection Computer Science Department. Faculté polytechnique 63
64 4 th use case: eyes, nose and frontal face tracking Computer Science Department. Faculté polytechnique 64
65 Conclusion Parallel processing between image pixels Parallel and heterogeneous processing between images Efficient exploitation of GPU memories Multi-GPU treatments for HD/Full HD videos Efficient selection of resources (CPUs or/and GPUs) Computer Science Department. Faculté polytechnique 65
66 Future Works Apply parallel and heterogeneous treatments on 3D images Integrate GPU based 3D image processing in our framework 3D medical imaging use cases Select the number of units (CPU or/and GPU) that offers a reduced energy consumption Integrate OpenCL implementations within our framework Computer Science Department. Faculté polytechnique 66
67 THANK YOU Université de Mons Sidi Ahmed Mahmoudi Computer Science Department. Faculté polytechnique 67
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