From ddc8fc1b9247c2888ebd00f8d454613f81238c46 Mon Sep 17 00:00:00 2001 From: Mehdi Ataei Date: Tue, 28 Nov 2023 11:36:11 -0500 Subject: [PATCH 1/6] Added XLB library Added XLB library https://github.com/Autodesk/XLB --- readme.md | 1 + 1 file changed, 1 insertion(+) diff --git a/readme.md b/readme.md index 29aa424..6042bc1 100644 --- a/readme.md +++ b/readme.md @@ -112,6 +112,7 @@ This section contains libraries that are well-made and useful, but have not nece - [QDax](https://github.com/adaptive-intelligent-robotics/QDax) - Quality Diversity optimization in Jax. - [JAX Toolbox](https://github.com/NVIDIA/JAX-Toolbox) - Nightly CI and optimized examples for JAX on NVIDIA GPUs using libraries such as T5x, Paxml, and Transformer Engine. - [Pgx](http://github.com/sotetsuk/pgx) - Vectorized board game environments for RL with an AlphaZero example. +- [XLB](https://github.com/Autodesk/XLB) - Distributed Multi-GPU Lattice Boltzmann Simulation Framework for Differentiable Scientific Machine Learning. From 133a46b4d16f33fcce4e3b9550327690f0c3b8b7 Mon Sep 17 00:00:00 2001 From: Mehdi Ataei Date: Tue, 28 Nov 2023 11:44:51 -0500 Subject: [PATCH 2/6] Added the white paper --- readme.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/readme.md b/readme.md index 6042bc1..d0174fc 100644 --- a/readme.md +++ b/readme.md @@ -217,6 +217,8 @@ This section contains papers focused on JAX (e.g. JAX-based library whitepapers, - [__Compiling machine learning programs via high-level tracing__. Roy Frostig, Matthew James Johnson, Chris Leary. _MLSys 2018_.](https://mlsys.org/Conferences/doc/2018/146.pdf) - White paper describing an early version of JAX, detailing how computation is traced and compiled. - [__JAX, M.D.: A Framework for Differentiable Physics__. Samuel S. Schoenholz, Ekin D. Cubuk. _NeurIPS 2020_.](https://arxiv.org/abs/1912.04232) - Introduces JAX, M.D., a differentiable physics library which includes simulation environments, interaction potentials, neural networks, and more. - [__Enabling Fast Differentially Private SGD via Just-in-Time Compilation and Vectorization__. Pranav Subramani, Nicholas Vadivelu, Gautam Kamath. _arXiv 2020_.](https://arxiv.org/abs/2010.09063) - Uses JAX's JIT and VMAP to achieve faster differentially private than existing libraries. +- [__XLB: Distributed Multi-GPU Lattice Boltzmann Simulation Framework for Differentiable Scientific Machine Learning__. Mohammadmehdi Ataei, Hesam Salehipour. _arXiv 2023_.](https://arxiv.org/abs/2311.16080) - White paper describing the [XLB](https://github.com/Autodesk/XLB) library: benchmarks, validations, and more details about the library. + From c59a2a40e48ac329bc46e48188a9e00a8b23860a Mon Sep 17 00:00:00 2001 From: Mehdi Ataei Date: Sat, 23 Dec 2023 22:36:40 -0500 Subject: [PATCH 3/6] Update readme.md --- readme.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/readme.md b/readme.md index d0174fc..a065bce 100644 --- a/readme.md +++ b/readme.md @@ -112,7 +112,7 @@ This section contains libraries that are well-made and useful, but have not nece - [QDax](https://github.com/adaptive-intelligent-robotics/QDax) - Quality Diversity optimization in Jax. - [JAX Toolbox](https://github.com/NVIDIA/JAX-Toolbox) - Nightly CI and optimized examples for JAX on NVIDIA GPUs using libraries such as T5x, Paxml, and Transformer Engine. - [Pgx](http://github.com/sotetsuk/pgx) - Vectorized board game environments for RL with an AlphaZero example. -- [XLB](https://github.com/Autodesk/XLB) - Distributed Multi-GPU Lattice Boltzmann Simulation Framework for Differentiable Scientific Machine Learning. +- [XLB](https://github.com/Autodesk/XLB) - A Differentiable Massively Parallel Lattice Boltzmann Library in Python for Physics-Based Machine Learning. @@ -217,7 +217,7 @@ This section contains papers focused on JAX (e.g. JAX-based library whitepapers, - [__Compiling machine learning programs via high-level tracing__. Roy Frostig, Matthew James Johnson, Chris Leary. _MLSys 2018_.](https://mlsys.org/Conferences/doc/2018/146.pdf) - White paper describing an early version of JAX, detailing how computation is traced and compiled. - [__JAX, M.D.: A Framework for Differentiable Physics__. Samuel S. Schoenholz, Ekin D. Cubuk. _NeurIPS 2020_.](https://arxiv.org/abs/1912.04232) - Introduces JAX, M.D., a differentiable physics library which includes simulation environments, interaction potentials, neural networks, and more. - [__Enabling Fast Differentially Private SGD via Just-in-Time Compilation and Vectorization__. Pranav Subramani, Nicholas Vadivelu, Gautam Kamath. _arXiv 2020_.](https://arxiv.org/abs/2010.09063) - Uses JAX's JIT and VMAP to achieve faster differentially private than existing libraries. -- [__XLB: Distributed Multi-GPU Lattice Boltzmann Simulation Framework for Differentiable Scientific Machine Learning__. Mohammadmehdi Ataei, Hesam Salehipour. _arXiv 2023_.](https://arxiv.org/abs/2311.16080) - White paper describing the [XLB](https://github.com/Autodesk/XLB) library: benchmarks, validations, and more details about the library. +- [__XLB: A Differentiable Massively Parallel Lattice Boltzmann Library in Python__. Mohammadmehdi Ataei, Hesam Salehipour. _arXiv 2023_.](https://arxiv.org/abs/2311.16080) - White paper describing the [XLB](https://github.com/Autodesk/XLB) library: benchmarks, validations, and more details about the library. From c6b68a22bdc1d30b130bf33953a714bfcf8ba505 Mon Sep 17 00:00:00 2001 From: Nicholas Vadivelu Date: Sun, 24 Dec 2023 01:52:39 -0500 Subject: [PATCH 4/6] Add lint double link ignore --- readme.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/readme.md b/readme.md index a065bce..4884a99 100644 --- a/readme.md +++ b/readme.md @@ -217,7 +217,9 @@ This section contains papers focused on JAX (e.g. JAX-based library whitepapers, - [__Compiling machine learning programs via high-level tracing__. Roy Frostig, Matthew James Johnson, Chris Leary. _MLSys 2018_.](https://mlsys.org/Conferences/doc/2018/146.pdf) - White paper describing an early version of JAX, detailing how computation is traced and compiled. - [__JAX, M.D.: A Framework for Differentiable Physics__. Samuel S. Schoenholz, Ekin D. Cubuk. _NeurIPS 2020_.](https://arxiv.org/abs/1912.04232) - Introduces JAX, M.D., a differentiable physics library which includes simulation environments, interaction potentials, neural networks, and more. - [__Enabling Fast Differentially Private SGD via Just-in-Time Compilation and Vectorization__. Pranav Subramani, Nicholas Vadivelu, Gautam Kamath. _arXiv 2020_.](https://arxiv.org/abs/2010.09063) - Uses JAX's JIT and VMAP to achieve faster differentially private than existing libraries. + - [__XLB: A Differentiable Massively Parallel Lattice Boltzmann Library in Python__. Mohammadmehdi Ataei, Hesam Salehipour. _arXiv 2023_.](https://arxiv.org/abs/2311.16080) - White paper describing the [XLB](https://github.com/Autodesk/XLB) library: benchmarks, validations, and more details about the library. + From 2ef34eee86b9e453457c48a38f6b9079489e0567 Mon Sep 17 00:00:00 2001 From: Nicholas Vadivelu Date: Sun, 24 Dec 2023 01:55:44 -0500 Subject: [PATCH 5/6] Add ignore double link --- readme.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/readme.md b/readme.md index 4884a99..4014f43 100644 --- a/readme.md +++ b/readme.md @@ -112,7 +112,9 @@ This section contains libraries that are well-made and useful, but have not nece - [QDax](https://github.com/adaptive-intelligent-robotics/QDax) - Quality Diversity optimization in Jax. - [JAX Toolbox](https://github.com/NVIDIA/JAX-Toolbox) - Nightly CI and optimized examples for JAX on NVIDIA GPUs using libraries such as T5x, Paxml, and Transformer Engine. - [Pgx](http://github.com/sotetsuk/pgx) - Vectorized board game environments for RL with an AlphaZero example. + - [XLB](https://github.com/Autodesk/XLB) - A Differentiable Massively Parallel Lattice Boltzmann Library in Python for Physics-Based Machine Learning. + From 0f0476ec008d469e0baa3393fe7ef79b097ed2b9 Mon Sep 17 00:00:00 2001 From: Mehdi Ataei Date: Sun, 24 Dec 2023 03:13:09 -0500 Subject: [PATCH 6/6] Fixed the linter issue --- readme.md | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/readme.md b/readme.md index 4014f43..d6e1a9f 100644 --- a/readme.md +++ b/readme.md @@ -219,9 +219,7 @@ This section contains papers focused on JAX (e.g. JAX-based library whitepapers, - [__Compiling machine learning programs via high-level tracing__. Roy Frostig, Matthew James Johnson, Chris Leary. _MLSys 2018_.](https://mlsys.org/Conferences/doc/2018/146.pdf) - White paper describing an early version of JAX, detailing how computation is traced and compiled. - [__JAX, M.D.: A Framework for Differentiable Physics__. Samuel S. Schoenholz, Ekin D. Cubuk. _NeurIPS 2020_.](https://arxiv.org/abs/1912.04232) - Introduces JAX, M.D., a differentiable physics library which includes simulation environments, interaction potentials, neural networks, and more. - [__Enabling Fast Differentially Private SGD via Just-in-Time Compilation and Vectorization__. Pranav Subramani, Nicholas Vadivelu, Gautam Kamath. _arXiv 2020_.](https://arxiv.org/abs/2010.09063) - Uses JAX's JIT and VMAP to achieve faster differentially private than existing libraries. - -- [__XLB: A Differentiable Massively Parallel Lattice Boltzmann Library in Python__. Mohammadmehdi Ataei, Hesam Salehipour. _arXiv 2023_.](https://arxiv.org/abs/2311.16080) - White paper describing the [XLB](https://github.com/Autodesk/XLB) library: benchmarks, validations, and more details about the library. - +- [__XLB: A Differentiable Massively Parallel Lattice Boltzmann Library in Python__. Mohammadmehdi Ataei, Hesam Salehipour. _arXiv 2023_.](https://arxiv.org/abs/2311.16080) - White paper describing the XLB library: benchmarks, validations, and more details about the library.