Rui's Blog
Search
K

Lecture 15: CUDA Case Studies. (3) Parallel Prefix Scan on the GPU. Using Multiple Streams in CUDA.

Lecture Summary

  • Last time
    • Case studies: parallel reduction on the GPU & 1D convolution
    • Looking beyond today: some more GPU computing feature, but looking for a while into optimization features
  • Today
    • One more cast study: parallel prefix scan
    • Using streams in GPU computing: increasing problem size; improving execution speeds

Parallel Prefix Scan on the GPU

Definition of the algorithm

Algo 1: Hillis & Steele (1986)

  • Simple, but suboptimal (O(N*log2(N)))

Algo 2: Harris-Sengupta-Owen (2007)

  • Convoluted, but O(N)
  • Balanced trees: A common parallel algorithm pattern
    • Upsweep from roots to the main trunk, and then down sweep from trunk to root
    • "Tree": Just a concept--the actual data structure is not used
The reduction/upsweep step
The down sweep step. Sheesh, this is just...

CUDA Streams

  • A CUDA-enabled GPU has 2 engines
    • An execution engine
    • A copy engine (which contains 2 sub-engines that can work simultaneously)
      • A H2D copy sub-engine
      • A D2H copy sub-engine
  • Async execution
    • Examples: Kernel launches, D2D mem copies, mem copies by functions with the Async suffix, etc
  • Overlapping Host <--> Device data transfer with device execution
    • Issue: The device execution stack is FIFO
      • Addressed by the usage of CUDA "streams"
  • Concurrency can be managed through streams
    • Concurrency means one of two things:
      • The copy and the execution engines of GPU working at the same time
      • Several different kernels being executed at the same time on the GPU
  • A stream is a sequence of CUDA commands issued by the host that executes on the GPU in issue-order
    • CUDA operations in different streams may run concurrently
    • CUDA operations from different streams may be interleaved
  • As soon as a CUDA function is invoked, a default stream (stream 0) is created
  • Create using cudaStreamCreate(), destroy using cudaStreamDestroy()