[DOCS] Fixed README.md

README.md

@@ -2,19 +2,22 @@
 
 This is the development repository of Triton, a language and compiler for writing highly efficient custom Deep-Learning primitives. The aim of Triton is to provide an open-source environment to write fast code at higher productivity than CUDA, but also with much higher flexibility than [TVM](https://github.com/apache/incubator-tvm) and without having to manually specify compute schedules.
 
-The main scope of Triton at the moment are:
-- **Triton-C**: An imperative, single-threaded language for writing highly efficient compute-kernels at a relatively high abstraction level using numpy-like extensions of the C language.
-- **Triton-IR**: An intermediate-representation for optimizing multi-dimensional array operations in linear algebra programs
-- **Triton-JIT**: An optimizing just-in-time compiler for Triton-C, which generates GPU code on par with state-of-the-art CUDA-C  (e.g.,  [CUTLASS](https://github.com/NVIDIA/cutlass)) and PTX (e.g., [ISAAC](https://github.com/ptillet/isaac)). This includes transparent support for mixed-precision and Tensor Cores.
+The main components of Triton at the moment are:
 
-Bindings for **automatic** PyTorch custom op generations are included in -  **PyTriton**, along with a small DSL based on einsum that supports convolutions, shift-convolutions, direct einsums, etc.
+- **Triton-C**: An imperative, single-threaded language for writing highly efficient compute-kernels at a relatively high abstraction level (think numpy-like array operations in a C-like language).
+- **Triton-IR**: A special-purpose intermediate representation (Triton-IR) for aiding array-level program analysis and optimizations in Triton-C programs.
+- **Triton-JIT**: An optimizing just-in-time compiler for Triton-IR, which generates GPU code on par with state-of-the-art CUDA-C  (e.g.,  [CUTLASS](https://github.com/NVIDIA/cutlass)). This includes transparent support for mixed-precision and Tensor Cores.
+
+Bindings for **automatic** PyTorch custom op generations are included in  **PyTriton**, along with a small DSL based on einsum that supports convolutions, shift-convolutions, direct einsums, etc.
 
 The formal foundations of this project are described in the following MAPL2019 publication: [Triton: An Intermediate Language and Compiler for Tiled Neural Network Computations](http://www.eecs.harvard.edu/~htk/publication/2019-mapl-tillet-kung-cox.pdf). Please cite us if you use our work!
 
 
+
 ## Installation
 
-Triton is a fairly self-contained package and uses its own parser (forked from [wgtcc](https://github.com/wgtdkp/wgtcc)) and LLVM code-generator. However, at the moment it relies on LLVM-8.0+ for PTX code generation. The whole compiler stack (~30k lines of C++ code) should take around 15 secs to compile.
+Triton is a fairly self-contained package and uses its own parser (forked from [wgtcc](https://github.com/wgtdkp/wgtcc)) and LLVM-8.0+ for code generation.
+
 
 ```
 sudo apt-get install llvm-8-dev
@@ -25,13 +28,14 @@ cd examples;
 python einsum.py
 ```
 
-## Tutorials
+## Getting Started
+
+Please visit the [documentation](https://docs.triton-lang.org) to get started with Triton
+
+
+## Contributing
 
-- [The Triton-C language](https://github.com/ptillet/triton/blob/master/docs/triton-c.md)
-- [The PyTriton API](https://github.com/ptillet/triton/blob/master/docs/pytriton.md)
-- Extended Einstein Summations (coming soon...)
-- The Triton-IR representation (coming soon...)
-- The Triton-JIT compiler (coming soon...)
+Please keep in mind that this is a project I have been carrying out completely on my own as part of my Ph.D. thesis. While I am confident in the approach, there are still many things to fix and to polish. Please contact me (ptillet AT g.harvard.edu) or raise an issue if you want to contribute!
 
 ## ISAAC (deprecated) for fast inference