Tiling optimizations for 3D scientific computations
Proceedings of the 2000 ACM/IEEE conference on Supercomputing
A New Approach to Pipeline FFT Processor
IPPS '96 Proceedings of the 10th International Parallel Processing Symposium
Time Domain Numerical Simulation for Transient Waves on Reconfigurable Coprocessor Platform
FCCM '05 Proceedings of the 13th Annual IEEE Symposium on Field-Programmable Custom Computing Machines
Stencil computation optimization and auto-tuning on state-of-the-art multicore architectures
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
Finding Speedup in Parallel Processors
ISPDC '08 Proceedings of the 2008 International Symposium on Parallel and Distributed Computing
3D finite difference computation on GPUs using CUDA
Proceedings of 2nd Workshop on General Purpose Processing on Graphics Processing Units
Accelerating seismic computations using customized number representations on FPGAs
EURASIP Journal on Embedded Systems - FPGA supercomputing platforms, architectures, and techniques for accelerating computationally complex algorithms
Assessing Accelerator-Based HPC Reverse Time Migration
IEEE Transactions on Parallel and Distributed Systems
Eliminating the memory bottleneck: an FPGA-based solution for 3d reverse time migration
Proceedings of the 19th ACM/SIGDA international symposium on Field programmable gate arrays
A Modified Split-Radix FFT With Fewer Arithmetic Operations
IEEE Transactions on Signal Processing
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With a continual request for faster speed and better resolution, seismic migration continues to be one of the most computationally demanding geoscience applications. The recent emergence of new high performance computing (HPC) architectures, such as multi-core CPUs, Graphic Processing Units (GPUs), and Field Programmable Gate Arrays (FPGAs), offers significant speed up if the method can be effectively mapped. For high-end finite difference and spectral migration methods, the differencing stencils and FFTs are usually the dominant cost. In our work, we parallelize and optimize the stencil and FFT kernels on multi-core CPUs, GPUs, and FPGAs. We make an extensive comparison between the finite difference method and the spectral method on both numerical accuracy and parallel computation performance. Our experiments demonstrate that, although spectral methods eliminate the spatial dispersion errors and can lead to a reduced computational complexity, finite difference migrations are able to achieve the same accuracy with both a better performance and a better scalability in many cases because of the more regular computation and memory access patterns. The only exception is spectral methods based on 2D FFTs, which continue to scale with the parallel computation capacity of modern architectures. The technological trends indicate that these findings will continue.