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Title Squeeze: Efficient Compact Fractals for Tensor Core GPUs
Authors Felipe Quezada, Cristobal Navarro, Nancy Hitschfeld, Benjamin Bustos
Publication date May 2022
Abstract This work presents Squeeze, an efficient compact fractal
processing scheme for tensor core GPUs. By
combining discrete-space transformations between compact and expanded forms,
one can do data-
parallel computation on a fractal with neighborhood access without needing
to expand the fractal
in memory. The space transformations are formulated as two GPU tensor-core
accelerated thread
maps, lambda ( omega ) and nu ( omega ), which act as compact-to-expanded and
expanded-to-compact space functions,
respectively. The cost of the maps is O (log 2 log s (n)) time, with n being
the side of a n x n embedding
for the fractal in its expanded form, and s the linear scaling factor. The
proposed approach works for
any fractal that belongs to the Non-overlapping-Bounding-Boxes (NBB) class
of discrete fractals, and
can be extended to three dimensions as well. Experimental results using a
discrete Sierpinski Triangle
as a case study shows up to ~ 12 x of speedup and a memory reduction
factor of up to ~ 315 x with
respect to a GPU-based expanded-space bounding box approach. These results
show that the proposed
compact approach will allow the scientific community to efficiently tackle
problems that up to now
could not fit into GPU memory.
Pages 10-19
Volume 135
Journal name Future Generation Computer Systems
Publisher Elsevier Science (Amsterdam, The Netherlands)
Reference URL View reference page