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High-Precision Numerical Simulations on a CUDA GPU: Kerr Black Hole Tails

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Abstract

Computational science has advanced significantly over the past decade and has impacted almost every area of science and engineering. Most numerical scientific computation today is performed with double-precision floating-point accuracy (64-bit or \(\sim \)15 decimal digits); however, there are a number of applications that benefit from a higher level of numerical precision. In this paper, we describe such an application in the research area of black hole physics: studying the late-time behavior of decaying fields in Kerr black hole space-time. More specifically, this application involves a hyperbolic partial-differential-equation solver that uses high-order finite-differencing and quadruple (128-bit or \(\sim \)30 decimal digits) or octal (256-bit or \(\sim \)60 decimal digits) floating-point precision. Given the computational demands of this high-order and high-precision solver, in addition to the rather long evolutions required for these studies, we accelerate the solver using a many-core Nvidia graphics-processing-unit and obtain an order-of-magnitude speed-up over a high-end multi-core processor. We thus demonstrate a practical solution for demanding problems that utilize high-precision numerics today.

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Notes

  1. Perhaps the most significant and tragic event that occurred due a limited numerical precision issue in recent history was the failure of the Patriot Missle during the Persian Gulf War in the early 1990s. This was attributed to the limited precision capabilities of the control computer that was unable to operate accurately over long periods of time due to the accumulation of round-off error [3].

  2. A pseudo-spectral approach is better suited to address the challenges mentioned in this context; however, our higher-order finite-difference implementation requires relatively modest changes to our original second-order code, therefore we simply proceed with that approach. It is worth pointing out that other work in the same context using a pseudo-spectral approach posed similar precision issues [22], and thus made use of quadruple precision numerics on a CPU.

  3. The Cell BE port of the QD library performed quite well. However, the GPU implementation performed poorly, simply because we did not make any GPU-specific optimizations at the time and worked with much older, relatively low-performing hardware.

  4. It is worth pointing out that (surprisingly) the performance of the Intel C++ compiler supported long double datatype is much lower than that of double-double (DD) from the QD library.

  5. Note a slight variation to this trend in the CPU-DD plot. This is more clearly seen in Fig. 4.

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Khanna, G. High-Precision Numerical Simulations on a CUDA GPU: Kerr Black Hole Tails. J Sci Comput 56, 366–380 (2013). https://doi.org/10.1007/s10915-012-9679-3

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