seismic imaging

Superior seismic imaging for better drilling

1 min read · Sun, Jul 8 2018

seismic imaging fossil fuels extreme computing data analysis

Software could transform underground imaging of fossil fuel reserves by providing unprecedented detail in record time.

Code of collaboration

1 min read · Thu, May 26 2022

seismic imaging big data

David Keyes's and Matteo Ravasi's cross-disciplinary project results in improved efficiency for seismic processing, with promising applications for the energy industry.

Designing Tomorrow's Algorithms on Today's Architectures: a Case Study with Stencil Computations Using AMD 3D V-Cache™ Technology

1 min read · Mon, Sep 12 2022

HPC Performance optimization seismic imaging

Regular stencil computations constitute the main core kernel in many temporally explicit approaches for structured grid finite-difference, finite-volume, and finite-element discretizations of partial differential equation conservation laws. For various blocking dimensions, the Spatial Blocking (SB) approach enables data reuse within multiple cache levels. However, the straightforward generalization of SB to manycore architectures, with each core owning an exclusive share of cache may leave performance on the table. The next two figures show the typical 25-point 3D star stencil used in seismic

Long Qu

Research Scientist, Hierarchical Computations on Manycore Architectures

seismic imaging parallel algorithms

Dr. Long Qu is a Research Scientist at the Extreme Computing Research Center at KAUST, working with Prof. David E. Keyes and Dr. Hatem Ltaief. Research Interests Long Qu's research interests include seismic imaging and high performance computing on heterogeneous hardware architectures.

Kadir Akbudak

Consultant, Hierarchical Computations on Manycore Architectures

numerical methods low rank approximation seismic imaging parallel algorithms big data

Dr. Kadir Akbudak is a Consultant at Extreme Computing Research Center at KAUST, working with Professor David E. Keyes and Dr. Hatem Ltaief. Research Interests Kadir Akbudak's research interests include Combinatorial scientific computing, Graph and hypergraph partitioning, Locality-aware partitioning and scheduling, High performance kernels for sparse computations, Vectorization for irregular applications, Iterative numerical methods and Parallel numerical algorithms. Education Profile Ph.D., Computer Engineering, Bilkent University, 2015. M.S., Computer Engineering, Bilkent University, 2009