An adaptive performance modeling tool for GPU architectures
By Sara S. Baghsorkhi, Matthieu Delahaye, Sanjay J. Patel, William D. Gropp, Wen-mei W. Hwu. ACM SIGPLAN Symposium on Principles & Practice of Parallel Programming, 2010
335 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Hardware and Architecture, Information Systems
View Paper: https://www.semanticscholar.org/paper/55220bc99ffe36591a4b31a2ee9e40620381e0ca
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
- Rupesh Nasre
- John Douglas Owens
- Keshav Pingali
- Jingfei Kong
- Ping Xiang
- Yao Zhang
- Yi Yang
- Huiyang Zhou
In Hardware and Architecture
- Rupesh Nasre
- John Douglas Owens
- Keshav Pingali
- Jingfei Kong
- Ping Xiang
- Yao Zhang
- Yi Yang
- Huiyang Zhou
Citations in the corpus
- A GPGPU compiler for memory optimization and parallelism management (2010, methodology): “Another way is to use an analytical performance model [7], [ 15 ] to predict the performance of each version, but this requires higher accuracy than current models.”
- A quantitative performance analysis model for GPU architectures (2011, background): “[8] and Hong and Kim [9] recently authored excellent studies on GPU performance modeling.”
- A quantitative study of irregular programs on GPUs (2012, background): “A few studies propose empirical models to predict the effect of control-flow divergence and the memory hierarchy on performance [2], [3], [13].”