Image and Video Processing on Parallel (GPU) and Heterogeneous Architectures (Multi-CPU/Multi-GPU)

Transcription

1 Faculté Polytechnique 2nd Workshop of COST 0805 Open Network for High-Performance Computing on Complex Environments Image and Video Processing on Parallel (GPU) and Heterogeneous Architectures (Multi-CPU/Multi-GPU) 26 Jan 2012 Sidi Ahmed Mahmoudi, Pierre Manneback Computer Science Department, Faculty of Engineering. UMONS

2 Agenda 1. Introduction 2. Context 3. Image Processing on Parallel (GPU) and Heterogeneous Architectures 4. Video Processing on GPU 5. Proposed Framework for Multimedia Processing on Heterogeneous Architectures 6. Experimental Results 7. Conclusion Université de Mons 2

3 Introduction «The number of transistors that can be placed on an integrated circuit would double every two years». Moore s law. Effectively, the CPU power has doubled every 18 months till 2008 This law has no more been respected in recent years for thermal reasons: CPU power capped at 4 GHz. Solution: multiplication of computing units in CPU (many-cores) Large number on processing units on GPU: initially used in 3D and video games. Birth of GPGPU: General Purpose Graphic Processing Unit. The use of GPU to perform tasks habitually performed by the CPU. Université de Mons 3

4 CPU INTRODUCTION CONTEXT IMAGE PROCESSING ON HETEROGENEOUS ARCHITECTURES VIDEO PROCESSING ON GPU PROPOSED FRAMEWORK EXPERIMENTATIONS CONCLUSION Control DRAM ALU ALU Cache ALU ALU Context Hardware High computing power of GPUs. GPU Heterogeneous architectures: Multi-CPU/Multi-GPU. DRAM Applications Intensive processing of multimedia objects (images, videos, etc.). Platform Multi -CPU-GPU High intensity : Large volumes of multimedia objects (HD, Full HD, etc.). Constraints Transfer time between CPU and GPU memories. Adapted selection of the computing units (CPU or/and GPU) for processing. Complex management of heterogeneous architectures. Objectives Efficient multimedia processing on heterogeneous architectures (Multi-CPU/Multi-GPU). Efficient Selection of the computing units depending on the type of media to process. Université de Mons 4

5 GPU Programming Brook GPU : Since ATI Stream : for ATI cards. DirectX 11, OpenGL : GPGPU shaders. ATI Radeon-HD-4770 OpenCL : all types of GPUs. CUDA : for nvidia cards. Université de Mons 5 NVIDIA-GTX-590

6 GPU Programming: Runtimes for heterogeneous platforms 1. StarPU 2. StarSs Developed in LABRI laboratory. Bordeaux. France. Exploitation of the full computing power of machines (multi-cpu-gpu). Efficient Scheduling strategies. Developed in the university of Cataluña. Spain. Flexible programming model for multicores. Based mainly on: CPUSS: for multicore programming. GPUSS: for multi-gpu programming. 3. Grand Central Dispatch Developed by Apple, released for Mac systems. Optimize application support for systems with multi-core processors Université de Mons 6

7 Image Processing on GPU Image processing fits naturally for data parallel processing - pixels can be mapped directly to threads - lots of data are shared between pixels - high resolution images require intensive computing Advantage of CUDA and pixel shader for based image processing CUDA supports sharing images data with OpenGL and Direct3D applications Université de Mons 7

8 Image Processing on GPU Case 1: Single Image : OpenGL Visualization (without transfer CPU-GPU). Case 2: Set of images: Storing results on CPU memory ( with transfer CPU-GPU). Université de Mons 8

9 Image Processing on GPU 1. Classic image processing methods: Geometrical transformations(rotation, translation, etc.) Parallel processing between image pixels GPU Acceleration ranging from 10x to 40x compared to CPU Input Image 2. Corner Detection on GPU: Preliminary step for many algorithms of computer vision GPU implementation based on Harris and Bouguet technics Efficiency: invariance to rotation, brightness, scale, etc. Detected Contours on GPU 3. Edge Detection contours on GPU: GPU Implementation based on Deriche-Canny method Efficiency: robustness to noise, reduced number of operations. Good quality detected contours. Université de Mons 9 Detected Corners on GPU

10 Image Processing on GPU Case 1: Single Image (OpenGL Visualization) Image Resolution Corner Detection (CPU) Corner Detection (GPU) Speedup 512* ms ms * ms ms * ms ms * ms ms Corner Detection using Harris Detector Image Resolution Edge Detection (CPU) Edge Detection (GPU) Speedup 512* ms ms * ms ms * ms ms * ms ms Edge Detection using Deriche-Canny Method Case 2: Set of Image (Resolution: 1476*1680) Images Number Corner Detection (CPU) Corner Detection (GPU) Speedup s 0.48 s s 1.35 s s 2.60 s s 4.29 s 6.11 Corner Detection using Harris Detector Images Number Edge Detection (CPU) Edge Detection (GPU) Speedup s 0.40 s s 0.98 s s 1.80 s s 3.43 s 8.51 Edge Detection using Deriche-Canny Method Université de Mons 10

11 Multiple Image Processing on Heterogeneous Platforms (Multi-CPU/Multi-GPU) Optimization : - GPU streaming Technic: overlap kernel execution with device and host memory copies. - Streaming within multiple GPUs allowed improving performances about 25% in case of data-base medical image processing solution (Experimental results). Université de Mons 11

12 Video Processing on GPU in Real Time CPU GPU Video (Real Time) Imagei (i <=N) on GPU Imagei (i <=N) on CPU CUDA Parallel Processing OpenGL Visualisation i = i +1 Video End NO END YES Université de Mons 12

13 Video Processing on GPU in Real Time Background Subtraction on GPU Point of Interest Detection on GPU Performance: Background Subtraction on GPU Performance :Corner Detection on GPU Université de Mons 13

14 Video Processing on GPU in Real Time CPU Dual Core GPU GTX 280 GPU Tesla C2070(Fermi) 80 FPS x x x x1080 Video Resolution Performance: Optical flow Computation on GPU Université de Mons 14

15 Proposed Framework for Heterogeneous Multimedia Processing In case of heterogeneous computing, we use a scheduling strategy which gives priority to GPU for high intensive tasks and to CPU for less intensive tasks. Université de Mons 15

16 Use Case 1: Vertebra Segmentation Extraction of mean shape models of vertebrae Set of medical images Histogram Equalization (Improve contrast) Edge Detection Corner Detection Selection of vertebrae corners Extraction of real vertebrae (ASM) CPU treatments Hybrid Processing Multi-CPU/Multi-GPU CPU treatments Heterogeneous Computing for Vertebra Segmentation Université de Mons 16

17 Use Case 2: Video Indexation VideoCycle: Indexation of video sequences based on features extraction: Silhouette. Areas mouvements. Contours. Hu Moments. Hybrid detection of contours Hu moments extracted from edges Université de Mons 17

18 EXPERIMENTAL RESULTS: PERFORMANCE Speedup GPU 1GPU-2CPU 2-GPU 2GPU-4CPU 4GPU 4GPU-8CPU 0 512x x x x3936 Image Resolution Performance of edge + corner detection on heterogeneous architectures Note: The use of GPU Streaming technic allowed improving performances about 25%. Université de Mons 18

19 CONCLUSION The proposed framework allows parallel treatments of images at two levels: Low level: parallel processing on GPU between pixels in image (intra-image parallel processing) High Level: Simultaneous exploitation of both CPUs and GPUs cores (inter-image parallel processing) GPU treatments for High Definition video processing on real time : CUDA treatments and OpenGL visualization. Use of CUDA streaming technic in order to overlap transfers with computations. Current works : Computation of the intensity factor of each algorithm based on different parameter (number of operations, number of memory access, dependency factor, etc.) Efficient Selection of resources (CPU or/ and GPU) for a full exploitation of heterogeneous architectures. Université de Mons 19

20 Future Works Future Works : A general framework enabling an automatic selection of resources (CPU or/and GPU) depending of the intensity of image (single or multiple) and video processing applications. Exploitation of SDI capture (Input/Output) cards for real time video processing exploiting multiple outputs simultaneously. Quadro SDI Capture Card Full integrated GPU-based solution for real-time video processing Université de Mons 20

21 Regular Papers in Journals PUBLICATIONS F. Lecron, S. A. Mahmoudi, M. Benjelloun, S. Mahmoudi and P. Manneback "Heerogenous Computing for Vertebra Detection and Segmentation in X-Ray Images", International Journal of Biomedical Imaging : Parallel Computation in Medical Imaging Applications. Juin S. A. Mahmoudi, P. Manneback, C. Augonnet, S. Thibault «Traitement d Images sur Architectures Parallèles et Hétérogènes», Revue des sciences et technologies de l'information. In submission ( Submitted on 16/09/2011). International Conferences and Workshops S. A. Mahmoudi, P. Manneback, C. Augonnet, S. Thibault "Détection optimale des coins et contours dans des bases d'images volumineuses sur architectures multi-cœurs hétérogènes", 20eme Rencontres Francophones du Parallélisme, RenPar'20, Saint-Malo, France. Mai S. A. Mahmoudi, S. Frémal, M. Bagein, P. Manneback, "Calcul intensif sur GPU : exemples en traitement d'images, en bio-informatique et en télécommunication", CIAE 2011 : Colloque d'informatique, Automatique et Electronique, Casablanca, Maroc. Mars S. A. Mahmoudi, F. Lecron, P. Manneback, M. Benjelloun, S. Mahmoudi, "GPU-Based Segmentation of Cervical Vertebra in X-Ray Images", Workshop HPCCE. IEEE International Conference on Cluster Computing, Crete, Greece. Septembre S. A. Mahmoudi, P. Manneback, "Parallel Image Processing with CUDA and OpenGL", Network for High-Performance Computing on Complex Environments. Lisbon, Portugal. COST ACTION IC 805, WG Meeting. October S. A. Mahmoudi, P. Manneback, "Traitements d'images sur GPU sous CUDA et OpenGL : application a l'imagerie médicale", Journées CIGIL : Calcul Intensif et Grilles Informatiques a Lille. Lille, France. December S. A. Mahmoudi, Pierre Manneback, «Traitement d'objets multimédias sur gpu", Seconde journée scientifique du pôle hainuyer. Belgique, Mai Technical Reports: S. Dupont, C. Frisson, S. A. Mahmoudi, X. Siebert, J. Urbain, T. Ravet, "MediaBlender : Interactive Multimedia Segmentation and Annotation", QPSR of the numediart research program, volume 3, December M. Mancas, R. B. Madkhour, S. A. Mahmoudi, T. Ravet, "VirTrack: Tracking for Virtual Studios", QPSR of the numediart research program, volume 3, N 1, pp. 1-4, March M. Mancas, J. Tilmanne, R. Chessini, S. Hidot, C. Machy, S. A. Mahmoudi, T. Ravet, "MATRIX : Natural Interaction Between Real and Virtual Worlds", QPRS of the numediart research program, vol. 1, N 5, January M. Mancas, M. Bagein, N. Guichard, S. Hidot, C. Machy, S. A. Mahmoudi, X. Siebert, "AVS : Augmented Virtual Studio", QPSR of the numediart research program, Vol. 1, No. 4, December Université de Mons 21

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