On the limits of GPU acceleration

Richard Vuduc, Aparna Chandramowlishwaran, Jee Choi, Murat Guney, Aashay Shringarpure
Georgia Institute of Technology
In HotPar’10: Proceedings of the 2nd USENIX conference on Hot topics in parallelism (2010), pp. 13-13


   title={On the limits of GPU acceleration},

   author={Vuduc, R. and Chandramowlishwaran, A. and Choi, J. and Guney, M. and Shringarpure, A.},

   booktitle={Proceedings of the 2nd USENIX conference on Hot topics in parallelism},



   organization={USENIX Association}


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This paper throws a small “wet blanket” on the hot topic of GPGPU acceleration, based on experience analyzing and tuning both multithreaded CPU and GPU implementations of three computations in scientific computing. These computations–(a) iterative sparse linear solvers; (b) sparse Cholesky factorization; and (c) the fast multipole method–exhibit complex behavior and vary in computational intensity and memory reference irregularity. In each case, algorithmic analysis and prior work might lead us to conclude that an idealized GPU can deliver better performance, but we find that for at least equal-effort CPU tuning and consideration of realistic workloads and calling-contexts, we can with two modern quad-core CPU sockets roughly match one or two GPUs in performance.
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