# taichi
**Repository Path**: wangyh8216/taichi
## Basic Information
- **Project Name**: taichi
- **Description**: https://github.com/taichi-dev/taichi
BY：胡渊鸣
知乎：https://zhuanlan.zhihu.com/p/97700605
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 1
- **Forks**: 0
- **Created**: 2020-03-05
- **Last Updated**: 2020-12-19
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
| **Documentations** | **Chat** | taichi-nightly | taichi-nightly-cuda-10-0 | taichi-nightly-cuda-10-1 |
|:-----|:-----|:----|:----|:----|
| [](http://taichi.readthedocs.io/en/latest/?badge=latest) | [](https://gitter.im/taichi-dev/Lobby?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) | [](https://pepy.tech/project/taichi-nightly) | [](https://pepy.tech/project/taichi-nightly-cuda-10-0) | [](https://pepy.tech/project/taichi-nightly-cuda-10-1) |
```bash
# Python 3.6/3.7 needed for all platforms. Python 3.8 supported only on OS X and Windows
# CPU only. No GPU/CUDA needed. (Linux, OS X and Windows)
python3 -m pip install taichi-nightly
# With GPU (CUDA 10.0) support (Linux only)
python3 -m pip install taichi-nightly-cuda-10-0
# With GPU (CUDA 10.1) support (Linux only)
python3 -m pip install taichi-nightly-cuda-10-1
# Build from source if you work in other environments
```
## [Contribution Guidelines](https://taichi.readthedocs.io/en/latest/contributor_guide.html)
|| **Linux (CUDA)** | **OS X (10.14+)** | **Windows** |
|:------|:-----|:-----|:-----|
|**Build**|[](http://f11.csail.mit.edu:8080/job/taichi/)| [](https://travis-ci.com/taichi-dev/taichi) | [](https://ci.appveyor.com/project/yuanming-hu/taichi/branch/master)|
|**PyPI**|[](https://travis-ci.com/yuanming-hu/taichi-wheels-test)|[](https://travis-ci.com/yuanming-hu/taichi-wheels-test)|[](https://ci.appveyor.com/project/IteratorAdvance/taichi-wheels-test)|
## Updates
- (Mar 3, 2020) v0.5.6 released:
- Fixed runtime LLVM bitcode loading failure on Linux
- Fixed a GUI bug in `ti.GUI.line` (by **Mingkuan Xu [xumingkuan]**)
- Fixed frontend syntax error false positive (static range-fors) (by **Mingkuan Xu [xumingkuan]**)
- `arch=ti.arm64` is now supported. (Please build from source)
- CUDA supported on NVIDIA Jetson. (Please build from source)
- (Mar 2, 2020) v0.5.5 released: **Experimental CUDA 10.0/10.1 support on Windows. Feedbacks are welcome!**
- (Mar 1, 2020) v0.5.4 released
- Metal backend now supports < 32bit args (#530) (by **Ye Kuang [k-ye]**)
- Added `ti.imread/imwrite/imshow` for convenient image IO (by **Yubin Peng [archibate]**)
- `ti.GUI.set_image` now takes all numpy unsigned integer types (by **Yubin Peng [archibate]**)
- Bug fix: [Make sure KernelTemplateMapper extractors's size is the same as the number of args](https://github.com/taichi-dev/taichi/issues/534) (by **Ye Kuang [k-ye]**)
- [Avoid duplicate evaluations in chaining comparison (such as `1 < ti.append(...) < 3 < 4`)](https://github.com/taichi-dev/taichi/issues/540) (by **Mingkuan Xu [xumingkuan]**)
- Frontend kernel/function structure checking (#544) (by **Mingkuan Xu [xumingkuan]**)
- Throw exception instead of SIGABRT to obtain RuntimeError in Python-scope (by **Yubin Peng [archibate]**)
- Mark sync bit only after running a kernel on GPU (by **Ye Kuang [k-ye]**)
- `@ti.classkernel` is deprecated. Always use `ti.kernel`, no matter you are decorating a class member function or not (by **Ye Kuang [k-ye]**)
- Fix ti.func AST transform (due to locals() not saving compile result) #538, #539 (by **Yubin Peng [archibate]**)
- Add a KernelSimplicityASTChecker to ensure grad kernel is compliant (#553) (by **Ye Kuang [k-ye]**)
- Fixed MSVC C++ mangling which leads to unsupported characters in LLVM NVPTX ASM printer
- CUDA unified memory dependency is now removed. Set `TI_USE_UNIFIED_MEMORY=0` to disable unified memory usage
- Improved `ti.GUI.line` performance
- (For developers) compiler significantly refactored and folder structure reorganized
- (Feb 25, 2020) v0.5.3 released
- Better error message when try to declare tensors after kernel invocation (by **Yubin Peng [archibate]**)
- Logging: `ti.warning` renamed to `ti.warn`
- Arch: `ti.x86_64` renamed to `ti.x64`. `ti.x86_64` is deprecated and will be removed in a future release
- (For developers) Improved runtime bit code compilation thread safety (by **Yubin Peng [archibate]**)
- Improved OS X GUI performance (by **Ye Kuang [k-ye]**)
- Experimental support for new integer types `u8, i8, u16, i16, u32` (by **Yubin Peng [archibate]**)
- Update doc (by **Ye Kuang [k-ye]**)
- (Feb 20, 2020) v0.5.2 released
- Gradients for `ti.pow` now supported (by **Yubin Peng [archibate]**)
- Multi-threaded unit testing (by **Yubin Peng [archibate]**)
- Fixed Taichi crashing when starting multiple instances simultaneously (by **Yubin Peng [archibate]**)
- Metal backend now supports `ti.pow` (by **Ye Kuang [k-ye]**)
- Better algebraic simplification (by **Mingkuan Xu [xumingkuan]**)
- `ti.normalized` now optionally takes a argument `eps` to prevent division by zero in differentiable programming
- Improved random number generation by decorrelating PRNG streams on CUDA
- Set environment variable `TI_LOG_LEVEL` to `trace`, `debug`, `info`, `warn`, `error` to filter out/increase verbosity. Default=`info`
- [bug fix] fixed a loud failure on differentiable programming code generation due to a new optimization pass
- Added `ti.GUI.triangle` [example](https://github.com/taichi-dev/taichi/blob/master/misc/test_gui.py#L11)
- Doc update: added `ti.cross` for 3D cross products
- Use environment variable `TI_TEST_THREADS` to override testing threads
- [For Taichi developers, bug fix] `ti.init(print_processed=True)` renamed to `ti.init(print_preprocessed=True)`
- Various development infrastructure improvements by **Yubin Peng [archibate]**
- Official Python3.6 - Python3.8 packages on OS X (by **wYw [Detavern]**)
- (Feb 16, 2020) v0.5.1 released
- Keyboard and mouse events supported in the GUI system. Check out [mpm128.py](https://github.com/taichi-dev/taichi/blob/4f5cc09ae0e35a47ad71fdc582c1ecd5202114d8/examples/mpm128.py) for a interactive demo! (by **Yubin Peng [archibate] and Ye Kuang [k-ye]**)
- Basic algebraic simplification passes (by **Mingkuan Xu [xumingkuan]**)
- (For developers) `ti` (`ti.exe`) command supported on Windows after setting `%PATH%` correctly (by **Mingkuan Xu [xumingkuan]**)
- General power operator `x ** y` now supported in Taichi kernels (by **Yubin Peng [archibate]**)
- `.dense(...).pointer()` now abbreviated as `.pointer(...)`. `pointer` now stands for a dense pointer array. This leads to cleaner code and better performance. (by **Kenneth Lozes [KLozes]**)
- (Advanced struct-fors only) `for i in X` now iterates all child instances of `X` instead of `X` itself. Skip this if you only use `X=leaf node` such as `ti.f32/i32/Vector/Matrix`.
- Fixed cuda random number generator racing conditions
- (Feb 14, 2020) **v0.5.0 released with a new Apple Metal GPU backend for Mac OS X users!** (by **Ye Kuang [k-ye]**)
- Just initialize your program with `ti.init(..., arch=ti.metal)` and run Taichi on your Mac GPUs!
- A few takeaways if you do want to use the Metal backend:
- For now, the Metal backend only supports `dense` SNodes and 32-bit data types. It doesn't support `ti.random()` or `print()`.
- Pre-2015 models may encounter some undefined behaviors under certain conditions (e.g. read-after-write). According to our tests, it seems like the memory order on a single GPU thread could go inconsistent on these models.
- The `[]` operator in Python is slow in the current implementation. If you need to do a large number of reads, consider dumping all the data to a `numpy` array via `to_numpy()` as a workaround. For writes, consider first generating the data into a `numpy` array, then copying that to the Taichi variables as a whole.
- Do NOT expect a performance boost yet, and we are still profiling and tuning the new backend. (So far we only saw a big performance improvement on a 2015 MBP 13-inch model.)
- [Full changelog](changelog.md)
## Short-term goals
- (Done) Fully implement the LLVM backend to replace the legacy source-to-source C++/CUDA backends (By Dec 2019)
- The only missing features compared to the old source-to-source backends:
- Vectorization on CPUs. Given most users who want performance are using GPUs (CUDA), this is given low priority.
- Automatic shared memory utilization. Postponed until Feb/March 2020.
- (Done) Redesign & reimplement (GPU) memory allocator (by the end of Jan 2020)
- (WIP) Tune the performance of the LLVM backend to match that of the legacy source-to-source backends (Hopefully by Feb, 2020. Current progress: setting up/tuning for final benchmarks)
## Related papers
- [**(SIGGRAPH Asia 2019) High-Performance Computation on Sparse Data Structures**](http://taichi.graphics/wp-content/uploads/2019/09/taichi_lang.pdf) [[Video]](https://youtu.be/wKw8LMF3Djo) [[BibTex]](https://raw.githubusercontent.com/yuanming-hu/taichi/master/misc/taichi_bibtex.txt)
- by *Yuanming Hu, Tzu-Mao Li, Luke Anderson, Jonathan Ragan-Kelley, and Frédo Durand*
- [**(ICLR 2020) Differentiable Programming for Physical Simulation**](https://arxiv.org/abs/1910.00935) [[Video]](https://www.youtube.com/watch?v=Z1xvAZve9aE) [[BibTex]](https://raw.githubusercontent.com/yuanming-hu/taichi/master/misc/difftaichi_bibtex.txt) [[Code]](https://github.com/yuanming-hu/difftaichi)
- by *Yuanming Hu, Luke Anderson, Tzu-Mao Li, Qi Sun, Nathan Carr, Jonathan Ragan-Kelley, and Frédo Durand*
