Adds contributors and usage in README

README.md

@@ -1,9 +1,47 @@
 # GSParLib
 
-GSParLib is a C++ object-oriented multi-level API for GPU programming that allows code portability between different GPU platforms and targets stream and data parallelism.
+GSParLib is a C++ object-oriented multi-level API unifying OpenCL and CUDA for GPU programming that allows code portability between different GPU platforms and targets stream and data parallelism.
 
+Contributors and role:
+- Dinei A. Rockenbach [[ORCID](https://orcid.org/0000-0002-2091-9626)]: architecture design, interface definition, and development.
+- Gabriell Alves de Araujo [[ORCID](https://orcid.org/0000-0001-8179-2318)]: development of new features and most optimizations.
+- Dalvan Griebler [[ORCID](https://orcid.org/0000-0002-4690-3964)]: coordination, funds acquisition, and project management.
+
+## How to cite
 The scientific article presenting GSParLib is currently under review.
 
+## Usage
+
+A simple Vector Sum function using GSParLib Pattern API is shown below. Please refer to the [examples](examples/) folder to find complete examples using both Driver API and Pattern API, along with sequential versions of the algorithms for comparison.
+
+```c++
+float* vector_sum(const float max, const float* a, const float* b) {
+  float* result = new float[max];
+  try {
+    // Create a new Map object and defined the
+    // function to be executed for each data item
+    auto pattern = new GSPar::Pattern::Map(GSPAR_STRINGIZE_SOURCE(
+        result[x] = a[x] + b[x];
+    ));
+    // Sets the Map parameters, which will be translated
+    // by GSParLib into GPU Kernel parameters
+    pattern->setParameter("a", sizeof(float) * max, a)
+        .setParameter("b", sizeof(float) * max, b)
+        .setParameter("result", sizeof(float) * max, result, GSPAR_PARAM_OUT);
+    // Runs the pattern synchronously on the GPU
+    // using GSPar::Driver::CUDA or GSPar::Driver::OpenCL
+    pattern->run<GSPar::Driver::CUDA::Instance>({max, 0});
+    // Since the "result" is marked as an output parameter in setParameter,
+    // it is automatically copied from the GPU memory after running the kernel.
+    return result;
+  } catch (GSPar::GSParException &ex) {
+    // Any errors occurring during the execution will trigger an exception
+    std::cerr << "Exception: " << ex.what() << " - " << ex.getDetails() << std::endl;
+    exit(-1);
+  }
+}
+```
+
 ## Compilation
 
 - `make` builds the library
@@ -13,11 +51,11 @@ The scientific article presenting GSParLib is currently under review.
   - `make examples_sequential`
 - Alternatively, it is possible to compile individual examples by referring directly to their compiled names (the `cuda`/`opencl` suffix may be ommited). Ex.: `make bin/ex_driverapi_gpuinfo` compiles both CUDA and OpenCL versions of the [gpuinfo.cpp](examples/driver_api/gpuinfo.cpp) example.
 
-To compile with debugging enabled, use `DEBUG=1 make` (both when compiling the library and the examples). This automatically enables debugging flags, so that GSParLib print various debugging information during execution.
+To compile with debugging enabled, use `DEBUG=1 make` (both when compiling the library and the examples). This enables debugging code paths, so that GSParLib prints various debugging information during execution.
 
 ## Run examples
 
 After building the library it is necessary to make it available at runtime.
 To do this, execute `export LD_LIBRARY_PATH=<path>/bin:$LD_LIBRARY_PATH`, replacing `<path>` with the path to the repository's root folder.
 
-After this, just execute any example under the path `bin/ex_`
+After this, just execute any example under `bin/ex_`