https://github.com/root-project/genvectorx

Accelerated GenVector library
https://github.com/root-project/genvectorx

Last synced: about 2 months ago
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Accelerated GenVector library

• Host: GitHub
• URL: https://github.com/root-project/genvectorx
• Owner: root-project
• Created: 2023-11-09T14:44:10.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-03-22T10:03:20.000Z (10 months ago)
• Last Synced: 2024-03-27T07:46:35.685Z (9 months ago)
• Language: C++
• Size: 699 KB
• Stars: 0
• Watchers: 8
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# GenVectorX

Extented GenVector library for multi-target execution. 

# cmake configuration

## CUDA

A basic CUDA configuration looks like this:

```

cmake .. -Dcuda=ON -DCMAKE_CUDA_HOST_COMPILER=/usr/bin/g++ -DCMAKE_CUDA_COMPILER=/path/to/cuda/bin/nvcc -DCMAKE_CUDA_ARCHITECTURES=xx -DCMAKE_CUDA_FLAGS="-arch=sm_XX", where `XX` indicates the value of target CUDA capability.  

```

## SYCL

### Memory management

SYCL offers different memory management strategies. Here, we implement buffers+accessors or device pointers. Default memory management is handled with device pointers. In order to enable buffers+accessors, it is sufficient to set `-Dsycl_buffers=ON` in the cmake configuration. 

### AdaptiveCpp

A basic SYCL configuration with AdaptiveCpp looks like this:

```

cmake .. -Dadaptivecpp=ON -DAdaptiveCpp_DIR=/path/to/AdaptiveCpp/install/lib/cmake/AdaptiveCpp -DACPP_TARGETS=""  

```

AdaptiveCpp targets specification defines which compilation flows AdaptiveCpp should enable, and which devices from a compilation flow AdaptiveCpp should target during compilation. A CUDA example lookd like this: `-DACPP_TARGETS="cuda:sm_86"`. A HIP exaple looks like this: `-DACPP_TARGETS="hip:gfx90a"`

### oneAPI

A basic SYCL configuration with oneAPI targeting CUDA backends looks like this:

```

cmake ..  -Doneapi=ON -Dsyclcuda=ON -DCMAKE_CUDA_ARCHITECTURES=XX -DCUDA_TOOLKIT_ROOT_DIR=/path/to/cuda/

```

where `XX` indicates the value of target CUDA capability. 

A basic SYCL configuration with oneAPI targeting HIP backends looks like this:

```

cmake ..  -Doneapi=ON -Dsyclamd=ON -DCMAKE_OFFLOAD_ARCHITECTURES=gfxXXX 

```

where `XXX` is set accordingly to the AMD GPU model.

## Testing

In order to compile test targets, set `-Dtesting=ON` in the cmake configuration. Set `-Dsingle_precision=ON` for compiling single precision test targets.

In order to enable time measurements (and their print to stdout), set `-Dtiming=ON` in the cmake configuration.