Update README.md

README.md

@@ -2,15 +2,7 @@
 # VkFFT - Vulkan/CUDA/HIP/OpenCL/Level Zero/Metal Fast Fourier Transform library
 VkFFT is an efficient GPU-accelerated multidimensional Fast Fourier Transform library for Vulkan/CUDA/HIP/OpenCL/Level Zero/Metal projects. VkFFT aims to provide the community with an open-source alternative to Nvidia's cuFFT library while achieving better performance. VkFFT is written in C language and supports Vulkan, CUDA, HIP, OpenCL, Level Zero and Metal as backends.
 
-## Check out my poster at SC22: https://sc22.supercomputing.org/presentation/?id=rpost143&sess=sess273
-
-## Check out my panel at Nvidia's GTC 2021 in Higher Education and Research category: https://gtc21.event.nvidia.com/
-
-## Python interface to VkFFT can be found here: https://github.com/vincefn/pyvkfft
-
-## Rust bindings to VkFFT can be found here: https://github.com/semio-ai/vkfft-rs
-
-## Benchmark results of VkFFT can be found here: https://openbenchmarking.org/test/pts/vkfft
+## The white paper of VkFFT is out - if you use VkFFT and want to cite it: https://ieeexplore.ieee.org/document/10036080
 
 ## Currently supported features:
   - 1D/2D/3D systems
@@ -33,9 +25,6 @@ VkFFT is an efficient GPU-accelerated multidimensional Fast Fourier Transform li
   - VkFFT supports Vulkan, CUDA, HIP, OpenCL, Level Zero and Metal as backend to cover wide range of APIs
   - Header-only library with Vulkan interface, which allows appending VkFFT directly to user's command buffer. Kernels are compiled at run-time
 ## Future release plan
- - ##### Planned
-    - Publication based on implemented optimizations
-    - Test mobile GPUs (they should work)
  - ##### Ambitious
     - Multiple GPU job splitting
 
@@ -91,6 +80,16 @@ For both precisions, all tested libraries exhibit logarithmic error scaling. The
 
 For FP32, twiddle factors can be calculated on-the-fly in FP32 or precomputed in FP64/FP32. With FP32 twiddle factors (right) VkFFT is slightly less precise in Bluestein’s and Rader’s algorithms. If needed, this can be solved with FP64 precomputation. 
 
+## Check out my poster at SC22: https://sc22.supercomputing.org/presentation/?id=rpost143&sess=sess273
+
+## Check out my panel at Nvidia's GTC 2021 in Higher Education and Research category: https://gtc21.event.nvidia.com/
+
+## Python interface to VkFFT can be found here: https://github.com/vincefn/pyvkfft
+
+## Rust bindings to VkFFT can be found here: https://github.com/semio-ai/vkfft-rs
+
+## Benchmark results of VkFFT can be found here: https://openbenchmarking.org/test/pts/vkfft
+
 ## Contact information
 The initial version of VkFFT is developed by Tolmachev Dmitrii\
 E-mail 1: <[email protected]>