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GTC ON-DEMAND

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Abstract:
Learn how to accelerate many small-sized linear algebra problems - from kernels to large-scale solvers. We describe techniques targeting parallelization, vectorization, and communication, which have become extremely challenging on many-core architectures/GPUs. Standard interfaces, called batched APIs, are proposed to be included in highly-optimized libraries like MAGMA that provide the most extended set of batched BLAS and LAPACK functionalities to date. We'll describe the developments as well as their use to accelerate applications from big data analytics to high-order FEM tensor computations, and low-rank approximations for solvers and preconditioners. We'll also concentrate on the GPU acceleration of a large-scale distributed-memory solver that uses a hierarchically compressed coefficient matrix.
Learn how to accelerate many small-sized linear algebra problems - from kernels to large-scale solvers. We describe techniques targeting parallelization, vectorization, and communication, which have become extremely challenging on many-core architectures/GPUs. Standard interfaces, called batched APIs, are proposed to be included in highly-optimized libraries like MAGMA that provide the most extended set of batched BLAS and LAPACK functionalities to date. We'll describe the developments as well as their use to accelerate applications from big data analytics to high-order FEM tensor computations, and low-rank approximations for solvers and preconditioners. We'll also concentrate on the GPU acceleration of a large-scale distributed-memory solver that uses a hierarchically compressed coefficient matrix.  Back
 
Topics:
Algorithms & Numerical Techniques, Performance Optimization
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8475
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