upd README

Author: Aympab

README.md

@@ -1,16 +1,22 @@
-# Batched Advection kernels
+# Batched Kernels with Memory Allocations (BKMA)
 
-This code implements a 1D advection operator inside a multidimensionnal space. It implements a [semi-Lagrangian scheme](https://en.wikipedia.org/wiki/Semi-Lagrangian_scheme) using the [SYCL 2020](https://registry.khronos.org/SYCL/specs/sycl-2020/html/sycl-2020.html) progamming models.
+![Advection process](docs/fig/AdvectionProcess.png)
 
-To reproduce the benchmark, follow the [benchmark README.md](benchmark/README.md) instructions.
+## 1D Convolution operator
+Implement a [1D convolution operator](https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html) in-place using BKMA strategies.
 
-## General algorithm
-For one time step, the algorithm's structure is as follow:
+## Lagrangian Advection
 
-![Advection process](docs/fig/AdvectionProcess.png)
+This code implements a 1D advection operator inside a multidimensionnal space. It implements a [semi-Lagrangian scheme](https://en.wikipedia.org/wiki/Semi-Lagrangian_scheme) using the [SYCL 2020](https://registry.khronos.org/SYCL/specs/sycl-2020/html/sycl-2020.html) progamming models.
+
+To reproduce the benchmark, follow the [benchmark README.md](benchmark/README.md) instructions.
 
 ### SYCL Implementations
-The algorithm is implemented in various ways using different SYCL constructs. It requires local memory allocation via the [local accessor](https://registry.khronos.org/SYCL/specs/sycl-2020/html/sycl-2020.html#sec:accessor.local). The implementations are in the `src/core/impl` directory.
+The algorithm is implemented in various ways using different SYCL constructs. It requires local memory allocation via the [local accessor](https://registry.khronos.org/SYCL/specs/sycl-2020/html/sycl-2020.html#sec:accessor.local). The implementations are in the `src/core` directory.
+
+- BasicRange (out of place), no hierarchical parallelism involved
+- NDRange (in-place), work-groups and work-items, direct mapping of the problem dimensions
+- AdaptiveWg (in-place or out-of-place), optimized work-group sizes, streaming, optimal local memory usage
 
 # 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.
@@ -19,13 +25,13 @@ Use the `./compile.sh --help` to see the options.
 Example usage:
 ```sh
 #generate the advection executable and advection.ini file
-./compile.sh --hw x86_64 --sycl intel-llvm 
+./compile.sh --hw cpu --sycl dpcpp 
 
-#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_dpcpp_a100 folder with benchmarks
+./compile.sh --hw a100 --sycl dpcpp --benchmark_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 
+#create build_acpp_mi300 folder with tests and execute tests
+./compile.sh --hw mi300 --sycl acpp --build-tests --run-tests 
 ```
 
 ## Manually build the project
@@ -34,40 +40,9 @@ Flags varies on the SYCL implementation you are using.
 - For acpp, export the `ACPP_TARGETS` environment variable before compiling
 
 # Run the executable
-1. Set the runtime parameters in `build/src/advection.ini`
-
-    ```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
-    workGroupSizeY = 1
-    # Outputs a solution.log file to be read with the python notebook
-    outputSolution = false
-
-    [geometry]
-    nb = 512   # nb of speed points (batch dimension)
-    n1 = 1024 # nb of spatial points (dimension of interest)
-    n2 = 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`
+1. Set the runtime parameters in `build/src/<conv1d|advection>.ini`
+2. Run the executable `build/src/advection/<conv1d|advection>`
 
 
 ### Credits
-This code is largely inspired by the [vlp4D](https://github.com/yasahi-hpc/vlp4d) code.
+The advection operator in this code is largely inspired by the [vlp4D](https://github.com/yasahi-hpc/vlp4d) code.