CUDA by Example: An Introduction to General-Purpose GPU Programming

By Jie Cheng. 2010

1,528 citations recorded by the source corpus.

Subfields: Hardware and Architecture

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Hardware and Architecture

1. Feng Yan
2. G. Em Karniadakis
3. Dongkun Zhang
4. Zhen Li
5. Chengliang Zhang
6. Xuhui Meng
7. Wei Wang
8. Minchen Yu

Citations in the corpus

• PPINN: Parareal Physics-Informed Neural Network for time-dependent PDEs (2019, methodology): “The reason is that we need to transfer the dataset from the CPU memory to the GPU, and then transfer it back to the CPU memory, which can be even more time consuming than the computational process for small dataset [23].”
• Medical image processing on the GPU - Past, present and future (2013, background): “Interested readers are referred to books about GPU programming (Kirk and Hwu, 2010; Sanders and Kandrot, 2011; Farber, 2011) and the CUDA programming guide for further details.”
• MArk: Exploiting Cloud Services for Cost-Effective, SLO-Aware Machine Learning Inference Serving (2019, methodology): “Second, it can take advantage of batch operation optimization from both software and hardware [30, 62].”