Reverse time migration (RTM) is a wave equation migration method that has high imaging accuracy and superior imaging effects in complex underground structures and has been extensively employed in industrial production. Surface offset gathers (SOGs) are crucial for migration velocity updating and muting the stretched waveform in the shallow layers of seismic data. However, RTM SOGs cannot be obtained directly. Usually, SOGs obtained through Kirchhoff migration are used for velocity updating, which fails to form a closed-loop process for RTM migration calculation and velocity updating. Attribute migration is a stable, simple, and efficient method to obtain RTM SOGs. Based on attribute migration, this article applied the constrained least-squares inversion instead of division to get a more accurate RTM SOGs and uses residual curvature analysis (RCA) to update the velocity. RTM SOGs can also be obtained by grouping seismic data according to offset and migrating each group separately. By comparison, SOGs obtained by these two methods had identical characteristics. Furthermore, the residual curvature spectrum after velocity analysis had identical characteristics. Therefore, we believe that the attribute migration method using constrained least-squares inversion can be employed for velocity updating and verify its effectiveness with model data and actual data. In summary, this article presents a stable and direct method to efficiently calculate SOGs for RTM and thus form a closed loop of RTM migration calculation and velocity updating.

Seismic exploration is a geophysical method used for imaging subsurface structures, capable of providing high-resolution images of the underground. In seismic data processing, Kirchhoff Pre-Stack Depth Migration (KPSDM) serves as one of the key techniques, playing a critical role in significantly enhancing the lateral resolution of imaging and providing accurate characterization of subsurface media. However, with the continuous growth in high-density seismic data volumes, the computational efficiency of KPSDM is primarily constrained by substantial computational loads, end-to-end I/O bottlenecks, and data storage pressures. To address the performance optimization challenges of computation-intensive applications that require frequent large-scale data transfers between the host and accelerator devices, this paper proposes GAP-DCCS, a GPU-based Generic Acceleration Paradigm with efficient Data Compression and Caching Strategy, which includes the following core strategies: (1) For compute-intensive modules, a GPU-based three-dimensional parallel acceleration is implemented, combined with memory access optimization techniques and overlapping strategies for data transfer and computation, to improve GPU resource utilization; (2) To alleviate the storage pressure of large-scale datasets, the BitComp compression algorithm is introduced to efficiently compress task data while maintaining output stability, significantly reducing storage requirements and end-to-end data transfer volume; (3) To tackle the I/O bottleneck caused by frequent large-scale data transfers between the host and devices, an adaptive dynamic caching data management mechanism is designed to effectively increase data reuse rates and markedly reduce end-to-end transfer frequency. Experimental results demonstrate that the proposed optimization method significantly enhances the computational performance of KPSDM, achieving a speedup of 123.51× on a single NVIDIA Tesla A800 GPU compared to a 16-core CPU. This optimization paradigm has not only been effectively validated in KPSDM but also offers a referable high-performance computing solution for other large-scale data processing tasks.

Numerical simulations of the strong nuclear force, known as quantum chromodynamics or QCD, have proven to be a demanding, forefront problem in high-performance computing. In this report, we describe a new computer, QCDOC (QCD On a Chip), designed for optimal price/performance in the study of QCD. QCDOC uses a six-dimensional, low-latency mesh network to connect processing nodes, each of which includes a single custom ASIC, designed by our collaboration and built by IBM, plus DDR SDRAM. Each node has a peak speed of 1Gigaflops and two 12,288node, 10+ Teraflops machines are to be completed in the fall of 2004. Currently, a 512 node machine is running, delivering efficiencies as high as 45% of peak on the conjugate gradient solvers that dominate our calculations and a 4096-node machine with a cost of $1.6M is under construction. This should give us a price/performance less than $1per sustained Megaflops.