Accelerating CUDA graph algorithms at maximum warp

By Sungpack Hong, Sang Kyun Kim, Tayo Oguntebi, Kunle Olukotun. ACM SIGPLAN Symposium on Principles & Practice of Parallel Programming, 2011

392 citations recorded by the source corpus.

How researchers see this paper

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

Green-Marl: a DSL for easy and efficient graph analysis (2012, background): “This is practical as recent work [4] has demonstrated that fairly large-sized graph problems can be processed in the physical memory of a modern high-end server machine.”
Efficient Parallel Graph Exploration on Multi-Core CPU and GPU (2011, methodology): “Due to such importance, significant research has been conducted to efficiently implement a parallel BFS for a wide array of computing systems [12]–[19].”
A scalable processing-in-memory accelerator for parallel graph processing (2015, methodology): “Some works use GPGPUs to accelerate graph processing [15, 18, 19, 40].”
A quantitative study of irregular programs on GPUs (2012, background): “propose virtual warp sizing to speed up irregular GPU implementations of BFS and other codes [24].”
Scalable GPU graph traversal (2012, methodology): “[18] describe a vectorized version of the vertexoriented method that is similar to the CSR sparse matrix-vector (SpMV) multiplication approach by Bell and Garland [6].”