Ruidong Gu

Works (4)

Updated: July 5th, 2023 15:18

2020 article

A Loop-aware Autotuner for High-Precision Floating-point Applications

2020 IEEE INTERNATIONAL SYMPOSIUM ON PERFORMANCE ANALYSIS OF SYSTEMS AND SOFTWARE (ISPASS), pp. 285–295.

By: R. Gu n, P. Beata n & M. Becchi n

author keywords: autotuner; mixed-precision; floating-point
topics (OpenAlex): Numerical Methods and Algorithms; Parallel Computing and Optimization Techniques; Low-power high-performance VLSI design
TL;DR: This work proposes an auto-tuner for applications requiring high-precision floating-point arithmetic to deliver a prescribed level of accuracy, and generates a mixed precision program that trades off performance and accuracy by selectively using different precisions for different variables and operations. (via Semantic Scholar)
Source: Web Of Science
Added: May 24, 2021

2020 article

GPU-FPtuner: Mixed-precision Auto-tuning for Floating-point Applications on GPU

2020 IEEE 27TH INTERNATIONAL CONFERENCE ON HIGH PERFORMANCE COMPUTING, DATA, AND ANALYTICS (HIPC 2020), pp. 294–304.

By: R. Gu n & M. Becchi n

author keywords: GPU; floating-point arithmetic; mixed-precision arithmetic; accuracy; performance; autotuning
topics (OpenAlex): Numerical Methods and Algorithms; Parallel Computing and Optimization Techniques; Advanced Data Storage Technologies
TL;DR: A mixed precision autotuner for GPU applications that rely on floating-point arithmetic that takes into account code patterns prone to error propagation and GPU-specific considerations to generate a tuning plan that balances performance and accuracy. (via Semantic Scholar)
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Source: Web Of Science
Added: August 2, 2021

2019 article

A Comparative Study of Parallel Programming Frameworks for Distributed GPU Applications

CF '19 - PROCEEDINGS OF THE 16TH ACM INTERNATIONAL CONFERENCE ON COMPUTING FRONTIERS, pp. 268–273.

By: R. Gu n & M. Becchi n

author keywords: Parallel computing; Distributed applications; Homogeneous cluster
topics (OpenAlex): Parallel Computing and Optimization Techniques; Distributed and Parallel Computing Systems; Advanced Data Storage Technologies
TL;DR: This work considers several popular parallel programming frameworks for distributed applications and analyzes their memory model, execution model, synchronization model and GPU support, and compares their programmability, performance, scalability, and load-balancing capability on homogeneous computing cluster equipped with GPUs. (via Semantic Scholar)
Source: Web Of Science
Added: July 29, 2019

2017 conference paper

Understanding the performance-accuracy tradeoffs of floating-point arithmetic on GPUs

Proceedings of the 2017 ieee international symposium on workload characterization (iiswc), 207–218.

By: S. Surineni*, R. Gu n, H. Nguyen* & M. Becchi n

topics (OpenAlex): Numerical Methods and Algorithms; Parallel Computing and Optimization Techniques; Low-power high-performance VLSI design
TL;DR: Analysis of the use of different floating-point precisions on GPU using a variety of synthetic and real-world benchmark applications provides insights to guide users to the selection of the arithmetic precision leading to a good performance/accuracy tradeoff depending on the arithmetic operations and mathematical functions used in their program and the degree of multithreading of the code. (via Semantic Scholar)
Source: NC State University Libraries
Added: August 6, 2018

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