GPU-accelerated charge mapping

Sanaullah, Ahmed; Mojumder, Saiful A.; Lewis, Kathleen M.; Herbordt, Martin C.

doi:10.1109/hpec.2016.7761599

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…The performance values for the GPU solutions employing 1-4×GTX1080Ti and 1 V100 (on AWS) have been measured (see previous section on benchmarks). The remaining operating points have been estimated using numbers from existing benchmarks 15 .…”

Section: E Resultsmentioning

confidence: 99%

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On the Feasibility of FPGA Acceleration of Molecular Dynamics Simulations

Schaffner¹,

Benini²

2018

Preprint

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Classical molecular dynamics (MD) simulations are important tools in life and material sciences since they allow studying chemical and biological processes in detail. However, the inherent scalability problem of particle-particle interactions and the sequential dependency of subsequent time steps render MD computationally intensive and difficult to scale. To this end, specialized FPGA-based accelerators have been repeatedly proposed to ameliorate this problem. However, to date none of the leading MD simulation packages fully support FPGA acceleration and a direct comparison of GPU versus FPGA accelerated codes has remained elusive so far.With this report, we aim at clarifying this issue by comparing measured application performance on GPU-dense compute nodes with performance and cost estimates of a FPGA-based singlenode system. Our results show that an FPGA-based system can indeed outperform a similarly configured GPU-based system, but the overall application-level speedup remains in the order of 2× due to software overheads on the host. Considering the price for GPU and FPGA solutions, we observe that GPU-based solutions provide the better cost/performance tradeoff, and hence pure FPGA-based solutions are likely not going to be commercially viable. However, we also note that scaled multi-node systems could potentially benefit from a hybrid composition, where GPUs are used for compute intensive parts and FPGAs for latency and communication sensitive tasks.

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Section: E Resultsmentioning

confidence: 99%

“…Besides these MPI and GPU-based solutions, FPGA accelerators have repeatedly been proposed as a viable alternative to accelerate the compute intensive parts [10][11][12][13][14][15][16][17][18][19][20][21][22] . However, these studies only show estimated or measured speedup with respect to older CPU implementations.…”

Section: Introductionmentioning

confidence: 99%