Li-Jia-Jun / Project2-Stream-Compaction

CUDA Stream Compaction

University of Pennsylvania, CIS 565: GPU Programming and Architecture, Project 2

Jiajun Li

Linkedin: link

Tested on: Windows 10, i7-12700 @ 2.10GHz, 32GB, RTX3080 12GB

CUDA Compute Capability: 8.6

Overview

In this project, different scan methods and some of scan applications are implemented:

CPU side:

1. CPU navie scan
2. CPU compaction using CPU navie scan
3. CPU navie radix sort using CPU navie scan

GPU side:

1. GPU navie scan
2. GPU efficient scan with threads reduction
3. GPU efficient stream compaction using GPU efficient scan

Full explanations of each method can be found in GPU Gem 3 Ch 39.

The project also includes thrust::scan as a benchmark in performance analysis.

Project Setup

This project included the following changes from the original project:

1. Add radix_sort.h and radix_sort.cu to stream_compaction/CMakeLists

2. Add radix sort test code in src\main.cpp

Output example

****************
** SCAN TESTS **
****************
    [  45  11  41  13  34  22   1  22   6   5   3   5  21 ...   9   0 ]
==== cpu scan, power-of-two ====
  elapsed time: 0.0519ms    (std::chrono Measured)
    [   0  45  56  97 110 144 166 167 189 195 200 203 208 ... 801487 801496 ]
==== cpu scan, non-power-of-two ====
  elapsed time: 0.0519ms    (std::chrono Measured)
    [   0  45  56  97 110 144 166 167 189 195 200 203 208 ... 801379 801414 ]
    passed
==== naive scan, power-of-two ====
  elapsed time: 0.453632ms    (CUDA Measured)
    passed
==== naive scan, non-power-of-two ====
  elapsed time: 0.403456ms    (CUDA Measured)
    passed
==== work-efficient scan, power-of-two ====
  elapsed time: 0.31744ms    (CUDA Measured)
    passed
==== work-efficient scan, non-power-of-two ====
  elapsed time: 0.08192ms    (CUDA Measured)
    passed
==== thrust scan, power-of-two ====
  elapsed time: 0.044032ms    (CUDA Measured)
    passed
==== thrust scan, non-power-of-two ====
  elapsed time: 0.045056ms    (CUDA Measured)
    passed

*****************************
** STREAM COMPACTION TESTS **
*****************************
    [   1   0   3   0   3   0   2   3   1   3   3   1   1 ...   0   0 ]
==== cpu compact without scan, power-of-two ====
  elapsed time: 0.0657ms    (std::chrono Measured)
    [   1   3   3   2   3   1   3   3   1   1   2   3   1 ...   3   1 ]
    passed
==== cpu compact without scan, non-power-of-two ====
  elapsed time: 0.0648ms    (std::chrono Measured)
    [   1   3   3   2   3   1   3   3   1   1   2   3   1 ...   3   3 ]
    passed
==== cpu compact with scan ====
  elapsed time: 0.1507ms    (std::chrono Measured)
    [   1   3   3   2   3   1   3   3   1   1   2   3   1 ...   3   1 ]
    passed
==== work-efficient compact, power-of-two ====
  elapsed time: 0.17408ms    (CUDA Measured)
    passed
==== work-efficient compact, non-power-of-two ====
  elapsed time: 0.171008ms    (CUDA Measured)
    passed

*****************************
** RADIX SORT TESTS **
*****************************
==== cpu radix sort, power-of-two ====
  elapsed time: 0.4766ms    (std::chrono Measured)
    [   0   0   0   0   0   0   0   0   0   0   0   0   0 ...  49  49 ]
==== cpu radix sort, non-power-of-two ====
  elapsed time: 0.4721ms    (std::chrono Measured)
    [   0   0   0   0   0   0   0   0   0   0   0   0   0 ...  49  49 ]

Performance Analysis

In all the following analysis, less time is better.

Scan

• Work efficient scan out performs cpu scan when number of elements is greater than 2^16.

• Work efficient scan roughly align with thrust scan when number of elements is greater than 2^18.

• Navie GPU scan is always slower than navie CPU scan. This is because GPU method accesses data trhough global memory, which is considerably costy.

Stream Compaction

• Using scan will make it slower in the CPU implementation because scan introduces more iterations over array.

• Work efficient scan starts to out perform cpu scan when number of elements is greater than 2^18.

• For work efficient scan, it performs slightly better when the number of elements is not power of two.

Radix Sort

Future Improvement

1. Implement parallel radix sort and compare it with navie radix sort.

2. Make GPU scans even more efficient by using share memory.