University of Pennsylvania, CIS 565: GPU Programming and Architecture, Project 2
- Yash Vardhan
- Tested on: Windows 10 Pro, Intel i5-4200U (4) @ 2.600GHz 4GB, NVIDIA GeForce 840M 2048MB
In this project, I have implemented the stream compaction and scan(prefix sum) in CUDA on both a CPU and a GPU, and have compared the performance of both of these side-by-side. The parallel version of Stream Compaction run on a GPU is a useful tool for many applications like deferred shading, path tracer algorithms. Algorithms like scan (a.k.a. prefix-sum) are the basis of many algorithms. They are specifically designed to run on GPU architecture.
The scan algorithms were implemented on -
- CPU
- Naive Scan on GPU
- Work-efficient Scan on GPU
- Scan using Thrust on GPU.
Block Sizes were varied from 16 to 1024. The performance peaked around 64,128 and 256. I selected to benchmark the performance graphs on block size of 128.
The size of array was increased incrementally with a magnitude of 2^4. The transistion from 2^16 to 2^20 showed a signinficant diversion between thrust and naive GPU impementation. Also a difference between Work-Efficient and naive GPU implementation was observed. The Work-efficient GPU Scan could be optimized much more by using shared memory, which will reflect ina future benchmark test.
Tested on array size of 512
Tested on array size of 512
