8 Steps to 3.7 TFLOP/s on NVIDIA V100 GPU: Roofline Analysis and Other Tricks

huikong2013

Abstract—Performance optimization can be a daunting task
especially as the hardware architecture becomes more and more
complex. This paper takes a kernel from the Materials Science
code BerkeleyGW, and demonstrates a few performance analysis
and optimization techniques. Despite challenges such as high
register usage, low occupancy, complex data access patterns,
and the existence of several long-latency instructions, we have
achieved 3.7 TFLOP/s of double-precision performance on an
NVIDIA V100 GPU, with 8 optimization steps. This is 55% of
the theoretical peak, 6.7 TFLOP/s, at nominal frequency 1312
MHz, and 70% of the more customized peak based on our
58% FMA ratio, 5.3 TFLOP/s. An array of techniques used to
analyze this OpenACC kernel and optimize its performance are
shown, including the use of hierarchical Rooflfline performance
model and the performance tool Nsight Compute. This kernel
exhibits computational characteristics that are commonly seen
in many high-performance computing (HPC) applications, and
are expected to be very helpful to a general audience of HPC
developers and computational scientists, as they pursue more
performance on NVIDIA GPUs.
Index Terms—NVIDIA GPU, hierarchical Rooflfline analysis,
Nsight Compute, performance optimization

zoe0615

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Ryggeor

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im.leo

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zlhrsy

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zlhrsy

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dwu_eagle2016

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lilyrose_lr

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ldd-1987

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