Find out about the newest method for Marine Hydrocarbon Exploration. In this session we will profile the use of Finite Difference Time Domain (FDTD) technique in combination with Mittet's method and GPUs to produce faster, cheaper, more accurate forward modeling for electromagnetic imaging (Controlled Source Electromagnetic or CSEM). Unlike many frequency domain CSEM techniques this accelerated method does not require simplifying assumptions to reduce the memory and computational burden and has excellent scaling properties (essentially linear) across clusters of GPU accelerated nodes. CSEM is used in the industry to enhance confidence in hydrocarbon reservoir discoveries.
Sparse linear algebra solvers are used in many areas of scientific computing and are a key target for GPU acceleration. In this session, Acceleware will present an overview of its linear algebra libraries including support for direct and sparse iterative solvers. We will discuss the supported algorithms, performance results, and their application to engineering simulations. We will also look at some of the more commonly used preconditioners and some of the challenges faced when accelerating these algorithms on the GPU.
Acceleware provides software solutions to harness the parallel processing capabilities of multi-core GPUs/CPUs for the Electronic Design and Oil & Gas industries. Our Acceleration platform seamlessly integrates with applications from industry leading vendors in the Electromagnetic Simulation, Seismic Data Processing, Reservoir Modeling industries and Linear Algebra solvers. Acceleware solutions enable users of single-threaded applications to access multi-core processing hardware and achieve dramatic compute speed-ups. With Acceleware solutions installed, run times for data processing and simulation applications are reduced by more than 50 times. Our customers share a common and urgent need - the need for powerful and timely computer modeling and testing. They are pushing the boundaries of innovation and demand faster product-development cycles for more complex products. They want more effective tools to interpret vast amounts of data.