2012
DOI: 10.1002/fld.3648
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Finite element assembly strategies on multi‐core and many‐core architectures

Abstract: SUMMARY We demonstrate that radically differing implementations of finite element methods (FEMs) are needed on multi‐core (CPU) and many‐core (GPU) architectures, if their respective performance potential is to be realised. Our numerical investigations using a finite element advection–diffusion solver show that increased performance on each architecture can only be achieved by committing to specific and diverse algorithmic choices that cut across the high‐level structure of the implementation. Making these com… Show more

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Cited by 99 publications
(73 citation statements)
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“…In this work we focus exclusively on the first two requirements and we pave the way for the fulfilment of the last one, which is being addressed in ongoing work. The use of modern many-core hardware is attractive for acceleration of the high-order spectral element framework and for enabling practical computations of realistic engineering problems [49,50].…”
Section: Introductionmentioning
confidence: 99%
“…In this work we focus exclusively on the first two requirements and we pave the way for the fulfilment of the last one, which is being addressed in ongoing work. The use of modern many-core hardware is attractive for acceleration of the high-order spectral element framework and for enabling practical computations of realistic engineering problems [49,50].…”
Section: Introductionmentioning
confidence: 99%
“…Although well established finite element methods could be supported by such a declarative abstraction, it lacks the flexibility offered by frameworks such as OP2 for developing new applications/algorithms. Currently, a runtime code generation, compilation and execution framework that is based on Python, called PyOP2 [27], and a larger framework that supports finite element application development called Firedrake [1,21] is being developed at Imperial College London.…”
Section: Related Workmentioning
confidence: 99%
“…As the mathematical structure of the PDE is available for analysis, important equation-specific optimisations may be performed that low level compilers cannot automate, or that would be too laborious to implement by hand [7,8]. UFL is remarkably compact: a finite element discretisation that might take thousands or tens of thousands of lines of FORTRAN or C++ code to implement can be cleanly expressed in just a handful of lines of UFL.…”
Section: Automated Code Generation In Computational Sciencementioning
confidence: 99%
“…* denotes the Hermitian transpose. If the adjoint model solution λ is known then one can compute the derivative with respect to many parameters, simultaneously, via (8).…”
Section: Block Structure Of An Adjoint Modelmentioning
confidence: 99%
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