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https://github.com/thalesmg/haskell-accelerate-parconc
Example and benchmark of Accelerate-HS from Parallel and Concurrent Programming in Haskell
https://github.com/thalesmg/haskell-accelerate-parconc
accelerate cuda gpu-computing haskell parallel-computing
Last synced: 5 days ago
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Example and benchmark of Accelerate-HS from Parallel and Concurrent Programming in Haskell
- Host: GitHub
- URL: https://github.com/thalesmg/haskell-accelerate-parconc
- Owner: thalesmg
- License: gpl-3.0
- Created: 2018-04-22T21:16:43.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2018-04-22T21:17:21.000Z (almost 7 years ago)
- Last Synced: 2024-12-15T15:16:31.326Z (about 2 months ago)
- Topics: accelerate, cuda, gpu-computing, haskell, parallel-computing
- Language: Haskell
- Size: 17.6 KB
- Stars: 0
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: ChangeLog.md
- License: LICENSE
Awesome Lists containing this project
README
# Example of using Accelerate-HS from Parallel and Concurrent Programming in Haskell
## Environment
- OS: Ubuntu 17.10
- Installed CUDA according to the [official guide](https://developer.nvidia.com/cuda-downloads) (Linux > x86_64 > Ubuntu > 17.04 > deb (local)), CUDA v9.1, GeForce GTX 960M.
- `apt install llvm-5.0-dev`
- Stack resolver 11.5
- Stack extra deps:
- accelerate-llvm-1.1.0.0
- accelerate-llvm-native-1.1.0.1
- accelerate-llvm-ptx-1.1.0.1
- cuda-0.9.0.3
- llvm-hs-5.0.0
- llvm-hs-pure-5.0.0
More info on the [official Accelerate page](http://www.acceleratehs.org/get-started).
## Benchmarks
For some reason I yet do not understand, the vector dot product example from Accelerate's site is a lot slower with GPU than with CPU execution.
```
accelerate-parconc-0.1.0.0: benchmarks
Running 3 benchmarks...
Benchmark bench: RUNNING...
benchmarking GPU dotp
analysing with 1000 resamples
measurement overhead 1.136 ms
bootstrapping with 14 of 53 samples (26%)
time 1.413 ms (1.066 ms .. 1.623 ms)
0.811 R² (0.723 R² .. 0.880 R²)
mean 1.120 ms (914.5 μs .. 1.389 ms)
std dev 471.6 μs (426.2 μs .. 520.0 μs)
variance introduced by outliers: 98% (severely inflated)
benchmarking CPU dotp
analysing with 1000 resamples
bootstrapping with 23 of 75 samples (30%)
time 299.8 μs (289.8 μs .. 309.3 μs)
0.992 R² (0.988 R² .. 0.996 R²)
mean 298.0 μs (292.7 μs .. 304.3 μs)
std dev 15.31 μs (12.29 μs .. 21.51 μs)
found 1 outliers among 23 samples (4.3%)
1 (4.3%) high mild
variance introduced by outliers: 42% (moderately inflated)
Benchmark bench: FINISH
Benchmark shortest-cpu: RUNNING...
benchmarking shortest CPU
measurement took 225.1 s
analysing with 1000 resamples
measurement overhead 1.747 ms
bootstrapping with 3 of 4 samples (75%)
time 14.47 s (13.71 s .. 15.07 s)
1.000 R² (0.999 R² .. 1.000 R²)
mean 14.52 s (14.38 s .. 14.60 s)
std dev 129.1 ms (0.0 s .. 129.7 ms)
variance introduced by outliers: 19% (moderately inflated)
Benchmark shortest-cpu: FINISH
Benchmark shortest-gpu: RUNNING...
benchmarking shortest GPU
measurement took 57.50 s
analysing with 1000 resamples
measurement overhead -2.658 ms
bootstrapping with 3 of 4 samples (75%)
time 3.902 s (2.888 s .. 4.730 s)
0.989 R² (0.986 R² .. 1.000 R²)
mean 3.712 s (3.505 s .. 3.859 s)
std dev 221.5 ms (0.0 s .. 254.2 ms)
variance introduced by outliers: 19% (moderately inflated)
Benchmark shortest-gpu: FINISH
```