White Paper from Workshop on Large-scale Parallel Numerical Computing Technology (LSPANC 2020): HPC and Computer Arithmetic toward Minimal-Precision Computing
Roman Iakymchuk, Daichi Mukunoki, Artur Podobas, Fabienne, J\'ez\'equel, Toshiyuki Imamura, Norihisa Fujita, Jens Huthmann, Shuhei Kudo,, Yiyu Tan, Jens Domke, Kai Torben Ohlhus, Takeshi Fukaya, Takeo Hoshi, Yuki, Murakami, Maho Nakata, Takeshi Ogita, Kentaro Sano, Taisuke Boku

TL;DR
This paper discusses the development of a comprehensive minimal-precision computing system that combines hardware and software techniques, including precision-tuning, arbitrary-precision libraries, and FPGA technology, to optimize performance and accuracy.
Contribution
It introduces a broad system integrating multiple technologies for minimal-precision computing, extending beyond previous precision-tuning approaches.
Findings
Overview of minimal- and mixed-precision technologies
Future directions for minimal-precision computing system
Discussion of current challenges in the field
Abstract
In numerical computations, precision of floating-point computations is a key factor to determine the performance (speed and energy-efficiency) as well as the reliability (accuracy and reproducibility). However, precision generally plays a contrary role for both. Therefore, the ultimate concept for maximizing both at the same time is the minimal-precision computing through precision-tuning, which adjusts the optimal precision for each operation and data. Several studies have been already conducted for it so far (e.g. Precimoniuos and Verrou), but the scope of those studies is limited to the precision-tuning alone. Hence, we aim to propose a broader concept of the minimal-precision computing system with precision-tuning, involving both hardware and software stack. In 2019, we have started the Minimal-Precision Computing project to propose a more broad concept of the minimal-precision…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsNumerical Methods and Algorithms · Error Correcting Code Techniques · Parallel Computing and Optimization Techniques
