WASP: Selective Data Prefetching with Monitoring Runtime Warp Progress on GPUs

Yunho Oh, Myung Kuk Yoon, Jong Hyun Park, Yongjun Park, Won Woo Ro

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


This paper proposes a new data prefetching technique for Graphics Processing Units (GPUs) called Warp Aware Selective Prefetching (WASP). The main idea of WASP is to dynamically select warps whose progress is slower than that of the current warp as prefetching target warps. Under the in-order instruction execution model of GPUs, these prefetching target warps will certainly execute the same load as the current warp. Exploiting that, WASP prefetches the data for prefetching target warps, which allows the prefetched data to be accurately accessed. To simply verify the progress of the warps, WASP monitors the counts of the dynamic load executions for all warps. When a warp executes a load, WASP searches the warps with lower load execution counts than the current warp and generates the prefetch requests for them. In our evaluation, WASP achieves a 16.8 percent speedup compared to the baseline GPU.

Original languageEnglish
Article number8309426
Pages (from-to)1366-1373
Number of pages8
JournalIEEE Transactions on Computers
Issue number9
StatePublished - 1 Sep 2018

Bibliographical note

Funding Information:
This work was partly supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. R7117-16-0233, Development of Application Program Optimization Tools for High Performance Computing Systems) and Korea Evaluation Institute of Industrial Technology (KEIT) grant funded by the Korea government (MOTIE) (No.10080674, Development of Reconfigurable Artificial Neural Network Accelerator and Instruction Set Architecture).

Publisher Copyright:
© 2012 IEEE.


  • cache performance
  • data prefetching
  • warp scheduling


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