https://github.com/morgwai/gpu-samples
some GPU processing using JOCL (openCL) and Aparapi
https://github.com/morgwai/gpu-samples
aparapi concurrency concurrent-programming gpu gpu-programming java multithreading pram
Last synced: 3 months ago
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some GPU processing using JOCL (openCL) and Aparapi
- Host: GitHub
- URL: https://github.com/morgwai/gpu-samples
- Owner: morgwai
- License: apache-2.0
- Created: 2021-10-05T11:53:32.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2021-10-19T07:48:50.000Z (over 3 years ago)
- Last Synced: 2025-01-19T10:43:06.756Z (4 months ago)
- Topics: aparapi, concurrency, concurrent-programming, gpu, gpu-programming, java, multithreading, pram
- Language: Java
- Homepage:
- Size: 166 KB
- Stars: 1
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# GPU samples
Parallel reduction and [pointer jumping](https://en.wikipedia.org/wiki/Pointer_jumping) algorithms summarizing values from an array adapted to run on a GPU using [Aparapi](https://aparapi.com/) and [JOCL](http://www.jocl.org/) (frontends to [openCL](https://www.khronos.org/opencl/)).
## building and running comparison of various sync methods in openCL parallel reduction
First, make sure that you have an openCL driver for your GPU installed: [Nvidia](https://developer.nvidia.com/cuda-downloads), [AMD Linux](https://www.amd.com/en/support/kb/release-notes/rn-amdgpu-unified-linux-21-30) (AMD on windows should be available by default, hopefully).
```bash
./mvnw package
java -jar target/pointer-jumping-gpu-1.0-SNAPSHOT-jar-with-dependencies.jar
```
This will run parallel reduction kernels using 3 different approaches to synchronization on
arrays of various sizes from 32k to 128M elements, 50 times for each size. On my machine it
takes about 5 minutes. For each size it will output average time for each sync method.
These are times I got on my integrated Intel GPU:
32k element array:
```
BARRIER average: 403076
SIMD average: 295953
HYBRID average: 269073
CPU average: 62924
```
128k:
```
BARRIER average: 768170
SIMD average: 483343
HYBRID average: 433704
CPU average: 175977
```
256k:
```
BARRIER average: 1018578
SIMD average: 793267
HYBRID average: 738423
CPU average: 367999
```
512k:
```
BARRIER average: 1191166
SIMD average: 1019678
HYBRID average: 828609
CPU average: 780270
```
1M:
```
BARRIER average: 1759843
SIMD average: 1580668
HYBRID average: 1366559
CPU average: 1288948
```
2M:
```
BARRIER average: 3406786
SIMD average: 3070155
HYBRID average: 2398054
CPU average: 2674748
```
3M:
```
BARRIER average: 4166284
SIMD average: 4192948
HYBRID average: 3480526
CPU average: 3575055
```
4M-4k:
```
BARRIER average: 6573353 (1 recursive step on HYBRID)
SIMD average: 6758205
HYBRID average: 5653419
CPU average: 5582159
```
4M:
```
BARRIER average: 13797841
SIMD average: 13367851
HYBRID average: 12600975
CPU average: 5427631
```
32M:
```
BARRIER average: 102840013
SIMD average: 103991061
HYBRID average: 95481061
CPU average: 41226782
```
128M:
```
BARRIER average: 363563970
SIMD average: 387534517
HYBRID average: 344870087
CPU average: 160136923
```
255M:
```
BARRIER average: 878887550 (1 recursive step on HYBRID)
SIMD average: 819652415
HYBRID average: 730983353
CPU average: 323803437
```