Batched Advection kernels

This code implements a 1D advection operator inside a multidimensionnal space. It implements a semi-Lagrangian scheme using the SYCL 2020 progamming models.

To reproduce the benchmark, follow the benchmark README.md instructions.

General algorithm

For one time step, the algorithm's structure is as follow:

SYCL Implementations

The algorithm is implemented in various ways using different SYCL constructs. It requires shared memory allocation via the local accessor. The implementations are in the src/core/impl directory.

Build the project:

You can use the compile.sh script to compile for various hardware and sycl-implementations. For multi-device compilation flows, build the project manually. Use the ./compile.sh --help to see the options.

Example usage:

#generate the advection executable and advection.ini file
./compile.sh --hw x86_64 --sycl intel-llvm 

#create build_intel-llvm_a100 folder with benchmarks
./compile.sh --hw a100 --sycl intel-llvm --benchmark_BUILD_DIR=/path/to/google/benchmark/build 

#create build_acpp_mi250 folder with tests and execute tests
./compile.sh --hw mi250 --sycl acpp --build-tests --run-tests 

Manually build the project

Flags varies on the SYCL implementation you are using.

• For DPC++, add the correct flags via the -DDPCPP_FSYCL_TARGETS cmake variable.
• For acpp, export the ACPP_TARGETS environment variable before compiling

Run the executable

1. Set the runtime parameters in build/src/advection.ini

   [run]
   # Total number of iterations
   maxIter = 100
   # Wheter to run on the GPU or CPU
   gpu     = true
   # The kernel type to use for advection
   kernelImpl  = Hierarchical
   # Size of work groups use in the kernels
   workGroupSizeX = 128
   workGroupSizeB = 1
   # Outputs a solution.log file to be read with the python notebook
   outputSolution = false
   
   [geometry]
   nb = 512   # nb of speed points (batch dimension)
   nx = 1024 # nb of spatial points (dimension of interest)
   ns = 2 # fictive dimension, is also stride for x-dim
   
   [discretization]
   dt  = 0.001
   
   minRealX  = 0
   maxRealX  = 1
   minRealVx = -1
   maxRealVx = 1

   Deltas $d_{vx}$ and $d_x$ are deduced by the number of points and min/max values.

2. Run the executable build/src/advection

Credits

This code is largely inspired by the vlp4D code.