A Parallel Preconditioned Conjugate Gradient Solver for the Poisson Problem on a Multi-GPU Platform

We present a parallel conjugate gradient solver for the Poisson problem optimized for multi-GPU platforms. Our approach includes a novel heuristic Poisson preconditioner well suited for massively-parallel SIMD processing. Furthermore, we address the problem of limited transfer rates over typical dat...

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Bibliographic Details
Main Authors: Ament, Marco, Knittel, Gunter, Weiskopf, Daniel, Strasser, Wolfgang
Format: Conference Proceeding
Language:English
Subjects:
Bandwidth
Character generation
Concurrent computing
Conjugate Gradient
Graphics
Hardware
Iterative methods
Jacobian matrices
Linear systems
Multi-GPU
Parallel Preconditioning
Poisson Problem
Scalability
Sparse matrices
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Summary:We present a parallel conjugate gradient solver for the Poisson problem optimized for multi-GPU platforms. Our approach includes a novel heuristic Poisson preconditioner well suited for massively-parallel SIMD processing. Furthermore, we address the problem of limited transfer rates over typical data channels such as the PCI-express bus relative to the bandwidth requirements of powerful GPUs. Specifically, naive communication schemes can severely reduce the achievable speedup in such communication-intense algorithms. For this reason, we employ overlapping memory transfers to establish a high level of concurrency and to improve scalability. We have implemented our model on a high-performance workstation with multiple hardware accelerators. We discuss the mathematical principles, give implementation details, and present the performance and the scalability of the system.
ISSN:1066-6192
2377-5750
DOI:10.1109/PDP.2010.51