Case Study: Accelerating Full Waveform Inversion via OpenCL on AMD GPUs Case Study: Accelerating Full Waveform Inversion via OpenCL™ on AMD GPUs ©2014 Acceleware Ltd. All rights reserved. Chris Mason, Acceleware Product Manager March 5, 2014

Case Study: Accelerating Full Waveform Inversion via OpenCL on AMD GPUs About Acceleware  Software and services company specializing in HPC product development, developer training and consulting services  OpenCL training for AMD GPUs –Progressive lectures and hands-on lab exercises –Experienced instructors –Delivered worldwide  High performance consulting –Feasibility studies –Porting and optimization –Code commercialization 1

Case Study: Accelerating Full Waveform Inversion via OpenCL on AMD GPUs Outline  What is Full Waveform Inversion?  The Project  OpenCL  Optimizations –Coalescing –Iterative kernel for stencil operations –Fusing kernels together to eliminate redundant memory accesses  Key Performance Results 2

Case Study: Accelerating Full Waveform Inversion via OpenCL on AMD GPUs What is Full Waveform Inversion?  Seismic inversion technique  Used to build Earth models from recorded seismic data  Uses a finite-difference solution to the acoustic wave equation  Computationally expensive 3

Case Study: Accelerating Full Waveform Inversion via OpenCL on AMD GPUs What is FWI? From a basic starting point to an accurate velocity model 4

Case Study: Accelerating Full Waveform Inversion via OpenCL on AMD GPUs FWI Algorithm Initial Model Estimate Residuals Forward Propagate Source → Residuals Gradient Back Propagate Residuals → Gradient Step Length Forward Propagation(s) → Step Length Update Model Increase Frequency Loop over shots Loop over frequencies Loop until convergence 5

Case Study: Accelerating Full Waveform Inversion via OpenCL on AMD GPUs FWI Compute Cost  Cluster size of 10s to 100s of CPU nodes  Many days of runtime  Accuracy and quality reduced to keep runtime acceptable 6

Case Study: Accelerating Full Waveform Inversion via OpenCL on AMD GPUs The Project  GeoTomo develops high-end geophysical software products that help geophysicists around the world to image beneath the subsurface  GeoTomo had pre-existing cluster-ready multi-threaded (OpenMP based) CPU FWI solution  GeoTomo required their FWI application to run faster so they could deliver the results quicker to their clients –Looked to AMD GPUs to potentially accelerate their FWI and approached Acceleware for our help to make it happen 7

Case Study: Accelerating Full Waveform Inversion via OpenCL on AMD GPUs Why use GPUs? Performance! 8

Case Study: Accelerating Full Waveform Inversion via OpenCL on AMD GPUs OpenCL Overview  Parallel computing architecture standardized by the Khronos Group  OpenCL: –Is a royalty free standard –Provides an API to coordinate parallel computation across heterogeneous processors  Of interest because heterogeneous devices can significantly accelerate certain (primarily data-parallel) workloads –Defines a cross-platform programming language –Used on handheld/embedded devices through supercomputers 9

Case Study: Accelerating Full Waveform Inversion via OpenCL on AMD GPUs OpenCL Programming Model  Heterogeneous model, including provisions for a host connected to one or more devices –Example: GPUs, CPUs Host Device 1 GPU Device 2 GPU … Device N GPU 10

Case Study: Accelerating Full Waveform Inversion via OpenCL on AMD GPUs The OpenCL Programming Model  Data-parallel portions of an algorithm are executed on the device as kernels –Kernels are C functions with some restrictions and a few language extensions –Many (parallel) work-items execute the kernel  The host executes serial code between device kernel launches –Memory management –Data exchange to/from device (usually) –Error handling 11 Work-Group (0,0) Work-Group (1,0 ) Work-Group (0,1)Work-Group (1,1) Work-Group (0,2)Work-Group( 1,2) ND Range Work-Group (0,0) Work-Group (1,0) Work-Group (2,0) Work-Group (0,1) Work-Group (1,1) Work-Group (2,1) ND Range Host Device Host Device

Case Study: Accelerating Full Waveform Inversion via OpenCL on AMD GPUs OpenCL Memory Model  OpenCL kernels have access to four distinct memory regions: –Global  Allows read/write access from all work-items in all work-groups  Persistent across kernels –Local  Memory that is local to all work-items within a work-group –Constant  Region of memory that remains constant (read-only) during the execution of a kernel –Private  Memory that is private to a work-item  OpenCL vendors map memory regions into physical resources –Local/constant/private memory usually several orders of magnitude lower capacity but orders of magnitude faster than global memory 12

Case Study: Accelerating Full Waveform Inversion via OpenCL on AMD GPUs OpenCL Syntax – Memory Spaces  Host and device have separate memory spaces –Data is explicitly moved between them  Typically over PCIe bus  Host functions to allocate, copy, and free memory on device, eg. –clCreateBuffer() –clEnqueueReadBuffer() –clEnqueueWriteBuffer() –clReleaseMemoryObject() 13

Case Study: Accelerating Full Waveform Inversion via OpenCL on AMD GPUs Putting It All Together 14 A0A0 A0A0 A1A1 A1A1 A2A2 A2A2 A3A3 A3A3 A4A4 A4A4 A5A5 A5A5 A6A6 A6A6 A7A7 A7A7 B0B0 B0B0 B1B1 B1B1 B2B2 B2B2 B3B3 B3B3 B4B4 B4B4 B5B5 B5B5 B6B6 B6B6 B7B7 B7B7 C0C0 C0C0 C1C1 C1C1 C2C2 C2C2 C3C3 C3C3 C4C4 C4C4 C5C5 C5C5 C6C6 C6C6 C7C7 C7C7 C x = A x + B x One work-item per element Operation __kernel void VectorAdd(__global float* a, __global float* b, __global float* c) { int idx = get_global_id(0); c[idx] = a[idx] + b[idx]; } __kernel void VectorAdd(__global float* a, __global float* b, __global float* c) { int idx = get_global_id(0); c[idx] = a[idx] + b[idx]; } Each work-item has a unique index, typically used to index into arrays

Case Study: Accelerating Full Waveform Inversion via OpenCL on AMD GPUs Vector Add – Host Code 15 void VectorAdd(float* aH, float* bH, float* cH, int N) { int N_BYTES = N * sizeof(float); // Device management code … cl_mem aD = clCreateBuffer(…,N_BYTES, …); cl_mem bD = clCreateBuffer(…,N_BYTES, …); cl_mem cD = clCreateBuffer(…,N_BYTES, …); clEnqueueWriteBuffer(...,aD,…,N_BYTES,aH,…); clEnqueueWriteBuffer(...,bD,…,N_BYTES,bH,…); // Pass kernel arguments and launch kernel … clEnqueueNDRangeKernel(…, &N, …); clEnqueueReadBuffer(...,cD,…,N_BYTES,cH,…); } void VectorAdd(float* aH, float* bH, float* cH, int N) { int N_BYTES = N * sizeof(float); // Device management code … cl_mem aD = clCreateBuffer(…,N_BYTES, …); cl_mem bD = clCreateBuffer(…,N_BYTES, …); cl_mem cD = clCreateBuffer(…,N_BYTES, …); clEnqueueWriteBuffer(...,aD,…,N_BYTES,aH,…); clEnqueueWriteBuffer(...,bD,…,N_BYTES,bH,…); // Pass kernel arguments and launch kernel … clEnqueueNDRangeKernel(…, &N, …); clEnqueueReadBuffer(...,cD,…,N_BYTES,cH,…); } Allocate memory on device Transfer input arrays to device Launch kernel Transfer output array to host

Case Study: Accelerating Full Waveform Inversion via OpenCL on AMD GPUs Project Steps  1) Profiling –Acquired code, datasets and reference benchmarks from GeoTomo –Set up local machines with near-equivalent hardware, compiled code and confirmed reference benchmark numbers –Augmented code with timers to determine time spent in parallel regions, areas of interest 16

Case Study: Accelerating Full Waveform Inversion via OpenCL on AMD GPUs Project Steps  2) Feasibility Analysis –Investigated memory footprint for FWI jobs  GPU memory limited to 6GB per card –Investigated potential speedup / time to port code  Maximum speed up determined by time spent in parallel regions (Amdahl’s Law)  Time to port dependent on feature set –E.g. domain decomposition across multiple GPUs 17

Case Study: Accelerating Full Waveform Inversion via OpenCL on AMD GPUs Project Steps  3) Implementation –Creating testing harnesses –Kernel implementation –Resolving hardware driver issues –Enabling multi-GPU device support –Optimization iterations  4) Wrapup –Delivery of port, along with installation documentation –Trained GeoTomo developer on OpenCL 18

Case Study: Accelerating Full Waveform Inversion via OpenCL on AMD GPUs Key GeoTomo Optimizations  1) Coalescing –Changing memory access patterns in the kernels to those best suited for GPUs  Global memory is accessed via a request for a multi-byte word  Combine load/store requests from consecutive work-items to reduce the number of requested words –Fewer requests  less contention to global memory  Make one big multi-word burst request to global memory whenever possible –Contiguous bursts -> less global memory overhead 19

Case Study: Accelerating Full Waveform Inversion via OpenCL on AMD GPUs Key GeoTomo Optimizations  2) Iterative kernel for stencil operations Input Volumes Stencil Kernels * Outputs are weighted combinations of surrounding elements from input volumes Off-axis weights are zero Acknowledgement: Paulius Micikevicius,

Case Study: Accelerating Full Waveform Inversion via OpenCL on AMD GPUs Key GeoTomo Optimizations  Naïve implementation would have each work-item read all of its neighboring elements directly from global memory –Possible to hit maximum GPU memory bandwidth but redundant reads hurt performance 21

Case Study: Accelerating Full Waveform Inversion via OpenCL on AMD GPUs Key GeoTomo Optimizations  Alternative: Iterating over 2D slices along slowest dimension –Single items responsible for column of output array –Work-group caches 2D plane of input in local memory –Work-items store inputs in direction of iteration in registers –Reduces required number of global memory reads significantly Single Work- item View RegisterLocal memory Acknowledgement: Paulius Micikevicius,

Case Study: Accelerating Full Waveform Inversion via OpenCL on AMD GPUs Key GeoTomo Optimizations  3) Kernel Fusion –Reduce redundant memory accesses by fusing kernels that operate on the same volume together –Improves performance by reducing redundant global memory reads  4) Kernel Fission –Improve occupancy by lowering kernel resource requirements (registers) via kernel simplification –Allows for more work-items to run concurrently on GPU, improving masking of global memory latency 23

Case Study: Accelerating Full Waveform Inversion via OpenCL on AMD GPUs Performance Results  FWI 15 Hz, 15 shots –GPU version 7997 seconds –CPU (5 cores per shot) seconds [8.4X] –CPU (30 cores per shot) seconds [20.9X]  GPU: Sapphire Radeon HD 7970 GHz Edition –6GB model 24

Case Study: Accelerating Full Waveform Inversion via OpenCL on AMD GPUs Performance Results “Using GPU’s we can use higher frequencies and more if not all of the shots to improve the resolution and coverage.” James Jackson, President, GeoTomo 25

Case Study: Accelerating Full Waveform Inversion via OpenCL on AMD GPUs Questions? OpenCL Courses  June 3-6, 2014, Calgary, Canada  Private onsite classes also available  Acceleware.com/opencl-training Acceleware.com/opencl-training OpenCL Consulting  Feasibility studies  Code commercialization  Porting and optimization  Mentoring  Acceleware.com/services Acceleware.com/services Contact Us  Tel:  26