Fast quantized arithmetic on x86: Trading compute for data movement

A. Stojanov, T.M. Smith, D.-A. Alistarh, M. Puschel, in:, 2018 IEEE International Workshop on Signal Processing Systems, IEEE, 2018.

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Conference Paper | Published | English

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Author
Stojanov, Alen; Smith, Tyler Michael; Alistarh, Dan-AdrianIST Austria; Puschel, Markus
Department
Abstract
We introduce Clover, a new library for efficient computation using low-precision data, providing mathematical routines required by fundamental methods in optimization and sparse recovery. Our library faithfully implements variants of stochastic quantization that guarantee convergence at low precision, and supports data formats from 4-bit quantized to 32-bit IEEE-754 on current Intel processors. In particular, we show that 4-bit can be implemented efficiently using Intel AVX despite the lack of native support for this data format. Experimental results with dot product, matrix-vector multiplication (MVM), gradient descent (GD), and iterative hard thresholding (IHT) demonstrate that the attainable speedups are in many cases close to linear with respect to the reduction of precision due to reduced data movement. Finally, for GD and IHT, we show examples of absolute speedup achieved by 4-bit versus 32-bit, by iterating until a given target error is achieved.
Publishing Year
Date Published
2018-12-31
Proceedings Title
2018 IEEE International Workshop on Signal Processing Systems
Volume
2018-October
Article Number
8598402
Conference
SiPS: Workshop on Signal Processing Systems
Conference Location
Cape Town, South Africa
Conference Date
2018-10-21 – 2018-10-24
IST-REx-ID

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Stojanov A, Smith TM, Alistarh D-A, Puschel M. Fast quantized arithmetic on x86: Trading compute for data movement. In: 2018 IEEE International Workshop on Signal Processing Systems. Vol 2018-October. IEEE; 2018. doi:10.1109/SiPS.2018.8598402
Stojanov, A., Smith, T. M., Alistarh, D.-A., & Puschel, M. (2018). Fast quantized arithmetic on x86: Trading compute for data movement. In 2018 IEEE International Workshop on Signal Processing Systems (Vol. 2018–October). Cape Town, South Africa: IEEE. https://doi.org/10.1109/SiPS.2018.8598402
Stojanov, Alen, Tyler Michael Smith, Dan-Adrian Alistarh, and Markus Puschel. “Fast Quantized Arithmetic on X86: Trading Compute for Data Movement.” In 2018 IEEE International Workshop on Signal Processing Systems, Vol. 2018–October. IEEE, 2018. https://doi.org/10.1109/SiPS.2018.8598402.
A. Stojanov, T. M. Smith, D.-A. Alistarh, and M. Puschel, “Fast quantized arithmetic on x86: Trading compute for data movement,” in 2018 IEEE International Workshop on Signal Processing Systems, Cape Town, South Africa, 2018, vol. 2018–October.
Stojanov A, Smith TM, Alistarh D-A, Puschel M. 2018. Fast quantized arithmetic on x86: Trading compute for data movement. 2018 IEEE International Workshop on Signal Processing Systems. SiPS: Workshop on Signal Processing Systems vol. 2018–October.
Stojanov, Alen, et al. “Fast Quantized Arithmetic on X86: Trading Compute for Data Movement.” 2018 IEEE International Workshop on Signal Processing Systems, vol. 2018–October, 8598402, IEEE, 2018, doi:10.1109/SiPS.2018.8598402.

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