Accelerator integration in a tile-based SoC: lessons learned with a hardware floating point compression engine

Liu, Xueyang; Gonzalez-Guerrero, Patricia; Peng, Ivy; Minnich, Ronald; Gokhale, Maya

doi:10.1145/3624062.3624245

Proceedings of the SC '23 Workshops of the International Conference on High Performance Computing, Network, Storage, and Analys 2023

DOI: 10.1145/3624062.3624245

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Accelerator integration in a tile-based SoC: lessons learned with a hardware floating point compression engine

Xueyang Liu,

Patricia Gonzalez-Guerrero,

Ivy Peng

et al.

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2024

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ZFP: A compressed array representation for numerical computations

Lindstrom,

Hittinger,

Diffenderfer

et al. 2024

The International Journal of High Performance Computing Applica

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HPC trends favor algorithms and implementations that reduce data motion relative to FLOPS. We investigate the use of lossy compressed data arrays in place of traditional IEEE floating point arrays to store the primary data of calculations. Simulation is fundamentally an exercise in controlled approximation, and error introduced by finite-precision arithmetic (or lossy compression) is just one of several sources of error that need to be managed to ensure sufficient accuracy in a computed result. We describe ZFP, a compressed numerical format designed for in-memory storage of multidimensional arrays, and summarize theoretical results that demonstrate that the error of repeated lossy compression can be bounded and controlled. Furthermore, we establish a relationship between grid resolution and compression-induced errors and show that, contrary to conventional floating point, ZFP reduces finite-difference errors with finer grids. We present example calculations that demonstrate data reduction by 4x or more with negligible impact on solution accuracy. Our results further demonstrate several orders-of-magnitude increase in accuracy using ZFP over IEEE floating point and Posits for the same storage budget.

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ZFP: A compressed array representation for numerical computations

Lindstrom,

Hittinger,

Diffenderfer

et al. 2024

The International Journal of High Performance Computing Applica

View full text Add to dashboard Cite

show abstract

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Accelerator integration in a tile-based SoC: lessons learned with a hardware floating point compression engine

Cited by 1 publication

References 10 publications

ZFP: A compressed array representation for numerical computations

ZFP: A compressed array representation for numerical computations

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