GPU ACCELERATED TOTAL FOCUSING METHOD IN CIVA

Transcription

1 OPARUS GPU ACCELERATED TOTAL FOCUSING METHOD IN CIVA Authors: Gilles ROUGERON, Jason LAMBERT, Ekaterina IAKOVLEVA, L. LACASSAGNE Presenter: Nicolas DOMINGUEZ QNDE 2013 Baltimore, Md, USA, 24/07/2013 CEA 10 AVRIL 2012 PAGE 1

2 APPLICATION CONTEXT Phased Array Inspection in Industry: Examples AIRCRAFT INDUSTRY PIPE INDUSTRY NUCLEAR INDUSTRY Faster acquisition / Finer resolution / Limited Access Compensation QNDE July 2013 PAGE 2

3 APPLICATION CONTEXT Trade-off elements for practical implementation of phased array inspections NDT PERFORMANCE Good detection Law false alarms Sizing capability (resolution) INDUSTRIAL THROUGHPUT Amount (and type) of acquired data Scanning speed Processing speed Analysis speed (processing + diagnosis) Versatility (ability to cover areas) THE CHOICE OF A PHASED ARRAY SOLUTION IS A COMBINATION OF Device Acquisition settings Processing TFM QNDE July 2013 PAGE 3

4 APPLICATION CONTEXT Phased Array UT & Reconstructions Application of reconstruction algorithms can improve Contrast (Signal to Noise Ratio) Spatial resolution Limited Access Compensation But penalizes inspection speeds Acquisition is usually heavier more shots, more data to upload (ex. FMC) Processing needs to be applied. Work on processing acceleration QNDE July 2013 PAGE 4

5 APPLICATION CONTEXT Challenges with the Total Focusing Method performances TFM is preferentially processed from FMC acquisitions or a subset (SMC) Data acquisition Uploading data flow Data management / Memories Hardware/ Firmware Computation cost: Ultra-fast analysis (post-processing) Real-time reconstruction (with data flow) Firmware/ Software QNDE July 2013 PAGE 5

6 GPU ACCELERATED TFM IN CIVA Synthetic Focusing & Total Focusing Method QNDE July 2013 PAGE 6

7 COMPUTATION TASKS A 2 STEPS PROCESS 1. Time of flight computation 2. Amplitudes summation QNDE July 2013 PAGE 7

8 GPU ACCELERATED TFM IN CIVA Optimization approach Algorithmic optimizations Time of flights in emission and reception are equal => computed only once Time of flights are computed on a coarser grid of the reconstruction area and then interpolated on the refined grid. Time of flights computation Coarser grid = tiles on GPU Pixels Implementation optimization Parallelization Use of GPU devices Comparison GPU (CUDA / OpenCL) & CPU-multi-cores (OpenMP) QNDE July 2013 PAGE 8

9 TIME OF FLIGHT COMPUTATION (x i x i ) 2 +z 2 i Minimizing time of flight T Eip= + (x i x) 2 +z 2 c 1 Requires to solve x i x i c1 x i x i 2 +z i 2 Solved as a 4 th degree polynomial (flat surface). = c 2 x i x c 2 x i x 2 +z 2 (Snell-Law). Curved surfaces implies solving higher degree polynomials depending on the surface (6 th, 10 th or 16 th in the most general case). Laguerre s Method is used: iterative method, finds a real root or two complex conjugate roots at a time, requires deflation. QNDE July 2013 PAGE 9

10 PARALLEL STRATEGY AND MEMORY ACCESS GPU (Cuda/OpenCL) Blocks of threads correspond to tiles of pixels. Step 1 Time of flight at each 4 tile corners are computed by multiple threads. Results are stored in shared memory. Step 2 Simultaneous ToF interpolation + amplitude summation for same couple of E i R j elements for multiple pixels (one thread per pixel). Due to pixel locality and slowly spatially varying time of flights, memory access are enhanced (use of cache). Parallel CPU (OpenMP) Step 1 Parallel computation of time of flights for coarser grid (one CPU thread per grid point). Step 2 Parallel ToF interpolation + amplitude summation (one CPU thread per pixel) QNDE July 2013 PAGE 10

11 PERFORMANCE BENCHMARK Planar specimen Cylindrical specimen Phased array elements: 128 Central frequency: 2 MHz Number of samples per signal: 1031 FMC data : 130 MB Reconstruction zone (mm) : Image definition (pixels) : Phased array elements: 128 Central frequency: 2 MHz Number of samples per signal: 2007 FMC data : 250 MB Reconstruction zone (mm) : Image definition (pixels) : Hardware CPU 2x Xeon X5690 (6 GPU Nvidia: GTX580 (1.5 GB), Tesla C2070 (6 GB), AMD: HD6970 (2 GB) Software CPU OpenMP / Visual C OpenCL / Intel SDK 1.5 & AMD APP SDK 2.7 GPU Cuda 4.2 / NVCC on NVIDIA GTX580, Tesla C2070 OpenCL / AMD APP SDK 2.7 & NVIDIA OpenCL QNDE July 2013 PAGE 11

12 PERFORMANCE BENCHMARK Time (s) CPU GPU OpenMP OpenCL CUDA OpenCL Mono Multi AMD Intel GTX580 C2070 GTX580 C2070 HD6970 Planar Cylindrical Best acceleration performances on GPU with Cuda Good acceleration performances on GPU with OpenCL Good acceleration with OpenMP on CPU OpenCL on CPU performs less well (algorithm optimised for GPU memory access) QNDE July 2013 PAGE 12

13 A CIVA PERSPECTIVE CIVA implementation strategy OpenCL: emerging standard for massively parallel architecture processors programming (CPU/MIC/GPU ). Portability: Same source code executable on different hardwares. Good/acceptable performances compared with native platforms languages/library : i.e. Cuda on Nvidia, OpenMP on CPU. OpenCL is improving on CPU (see latest Intel SDK 2013 with automatic parallelisation and code vectorization) TFM accelerated code in CIVA developed with OpenCL QNDE July 2013 PAGE 13

14 A CIVA PERSPECTIVE Features for TFM on GPU in CIVA Specimen: Flat Cylindrical CAD 2.5D with planar extrusion Isotropic materials Multiple probe positions 2D and 3D ROI Direct Modes with conversion QNDE July 2013 PAGE 14

15 A CIVA PERSPECTIVE Examples of TFM on GPU in CIVA Complex surface specimen 64 elements ROI (60x40mm / 300x200 pixels) Computation time : 0,54s on Nvidia Tesla C2070 Scanning 10 positions Computation time : 4.2s on Nvidia Tesla C2070 QNDE July 2013 PAGE 15

16 A CIVA PERSPECTIVE Examples of TFM on GPU in CIVA Flat surface specimen 128 elements ROI (30x30mm / 400x400 pixels) LL LT = TL TT Complex surface specimen 128 elements 3D ROI (30x30x30mm3 / 80x80x80 voxels) Computation time : 1.8 s on Nvidia Tesla C2070 QNDE July 2013 PAGE 16

17 A CIVA PERSPECTIVE TFM on GPU will be available in a CIVA 11.X Enlarge range of applications Specimen geometries: conical, torical surfaces (polynomial degree up to 16) TFM with skips (corner and indirect echoes) GPU memory limits => «Out of core» A solution when FMC data exceeds GPU memory Accelerate Specialization for optimization: use Newton method for simple surfaces (ex. planar) less generic but more efficient. See also: Friday Session 43 A fast ultrasonic simulation based on GPU QNDE July 2013 PAGE 17

18 Thank you for your attention. Questions? CIVA 11 the new MAJOR version of CIVA is released! Ask for your DVD CEA 10 AVRIL 2012 PAGE 18