Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/voschezang/holographic-projector-simulations
Optimizations of Simulations of Holographic Projectors using CUDA
https://github.com/voschezang/holographic-projector-simulations
cuda gpu holography parallel-computing photonics
Last synced: about 1 month ago
JSON representation
Optimizations of Simulations of Holographic Projectors using CUDA
- Host: GitHub
- URL: https://github.com/voschezang/holographic-projector-simulations
- Owner: voschezang
- License: mit
- Created: 2020-01-21T15:53:39.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2023-01-19T07:13:51.000Z (about 2 years ago)
- Last Synced: 2024-11-09T22:40:33.252Z (3 months ago)
- Topics: cuda, gpu, holography, parallel-computing, photonics
- Language: Python
- Homepage:
- Size: 30.9 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Holographic-Projector
High performance simulations of [holographic](https://en.wikipedia.org/wiki/Holography) projectors for GPU's, using [CUDA](https://docs.nvidia.com/cuda/).
The implementations are explained in [this paper](https://link.springer.com/chapter/10.1007/978-3-030-77970-2_11
).
The main CLI application `cuda/holo` computes superpositions of multiple target positions w.r.t multiple source positions.
### Project structure
```sh
cuda # C++/CUDA code. Additionally the CUDA libraries cuBLAS, thrust and CUB are used.
matlab # Matlab scripts that can be used to run simulations.
py # legacy Python code which was used for prototyping. This does not require a GPU.
```
## Results
Quality
A brute-force simulation.
A Monte Carlo simulation (approximation).
### Performance
Three algorithms, in comparison to a baseline.
More
### Setup & Dependenceis
The main dependencies are `nvcc` and `gcc`. Make sure they are installed and added to the path.
E.g. in case of Nikhef intranet:
```
/usr/local/cuda-11.0/bin
/cvmfs/sft.cern.ch/lcg/releases/gcc/*/bin
```
and optionally run
```
LD_LIBRARY_PATH=/cvmfs/sft.cern.ch/lcg/releases/gcc/8.3.0.1/x86_64-centos7/lib64:$LD_LIBRARY_PATH
```
### Usage
Compile the CUDA program using
```
make build
```
which is an alias for `nvcc -o holo main.cu -l curand -l cublas -std=c++14 -arch=compute_70 -code=sm_70`.
The compile-time constants can be changed by appending `-D{compile_time_constants}`.
Then you can run the CLI application using
```
cuda/holo
```
Run `cuda/holo -h` to list all valid arguments.
By default, the required source and target positions are generated automatically.
There are `3` types of distributions, and they are indicated using the symbols:
- `x` the original input distribution, with positions `u`.
- `y` the _projector_ distribution, with positions `v`.
- `z` the _projection_ distribution, with positions `w`. This distribution will represent the original input distribution.
Using `x` as source, a target distribution `y` can be computed.
Then, using `y` as source, `z` can be computed.
Alternatively it is possible to use external data.
Use the flag `-f {directory}` to incdicate the name of the directory that contains the dataset files.
These files should be binary arrays for double-precision floating-point values (Little-endian encoding by default) and should be named as follows.
- `x0_amp.dat, x0_phase.dat`
- `u0.dat` (for the source data)
- `v0.dat` (for the _projector_ target positions)
- `w0_0.dat` (for the _projection_ target positions) - this file is only used if the boolean flag `-F` is supplied.
The projector distribution is written to `y0_amp.dat` and the projection distribution is written to `z0_0_amp.dat`.
---
Interference patterns of 1 an 5 points.
### Overview of CUDA code
The main kernel to compute superpositions is `superposition::per_block`,
which repeatedly calls `superposition::phasor_displacement`.
The main files are `cuda/main.h,.cu`, in which functions declared in `cuda/transform.cu` are called to compute superposition transformations:
- a brute force transformation `transform_full()`
The are a number of variants of `SuperpositionPerBlockHelper` macros which are used in combination with `superposition_per_block_helper()` functions.
They allow the GPU geometry to be included as templates, which is required for CUB library functions.
Additionally `cuda/macros.h` contain macros and constants.
---
Various tests are included in `cuda/test.cu` and `cuda/test_gpu.cu`.
There are no tests written for the MC estimators (`transform()`).