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# Python 3.6/3.7 needed

# 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

	Linux (CUDA)	OS X (10.14+)	Windows
Build
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Contribution Guidelines

Related papers

• (SIGGRAPH Asia 2019) High-Performance Computation on Sparse Data Structures [Video] [BibTex]
  • by Yuanming Hu, Tzu-Mao Li, Luke Anderson, Jonathan Ragan-Kelley, and Frédo Durand
• (ICLR 2020) Differentiable Programming for Physical Simulation [Video] [BibTex] [Code]
  • by Yuanming Hu, Luke Anderson, Tzu-Mao Li, Qi Sun, Nathan Carr, Jonathan Ragan-Kelley, and Frédo Durand

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 mid Feb, 2020. Current progress: setting up/tuning for final benchmarks)

Updates

• (Feb 6, 2020) v0.4.5 released.

  • ti.init(arch=..., print_ir=..., default_fp=..., default_ip=...) now supported. ti.cfg.xxx is deprecated
  • Immediate data layout specification supported after ti.init. No need to wrap data layout definition with @ti.layout anymore (unless you intend to do so)
  • ti.is_active, ti.deactivate, SNode.deactivate_all supported in the new LLVM x64/CUDA backend. Example
  • Experimental Windows non-UTF-8 path fix (by Yubin Peng [archibate])
  • ti.global_var (which duplicates ti.var) is removed
  • ti.Matrix.rotation2d(angle) added

• (Feb 5, 2020) v0.4.4 released.

  • For developers: ffi-navigator support [doc]. (by masahi)
  • Fixed f64 precision support of sin and cos on CUDA backends (by Kenneth Lozes [KLozes])
  • Make Profiler print the arch name in its title (by Ye Kuang [k-ye])
  • Tons of invisible contributions by Ye Kuang [k-ye], for the WIP Metal backend
  • Profiler working on CPU devices. To enable, ti.cfg.enable_profiler = True. Call ti.profiler_print() to print kernel running times
  • General performance improvements

• (Feb 3, 2020) v0.4.3 released.

  • GUI.circles 2.4x faster
  • General performance improvements

• (Feb 2, 2020) v0.4.2 released.

  • GUI framerates are now more stable
  • Optimized OffloadedRangeFor with const bounds. Light computation programs such as mpm88.py is 30% faster on CUDA due to reduced kernel launches
  • Optimized CPU parallel range for performance

• (Jan 31, 2020) v0.4.1 released.

  • Fixed an autodiff bug introduced in v0.3.24. Please update if you are using Taichi differentiable programming.
  • Updated Dockerfile (by Shenghang Tsai [jackalcooper])
  • pbf2d.py visualization performance boosted (by Ye Kuang [k-ye])
  • Fixed GlobalTemporaryStmt codegen

• (Jan 30, 2020) v0.4.0 released.

  • Memory allocator redesigned
  • Struct-fors with pure dense data structures will be demoted into a range-for, which is faster since no element list generation is needed
  • Python 3.5 support is dropped. Please use Python 3.6(pip)/3.7(pip)/3.8(Windows: pip; OS X & Linux: build from source) (by Chujie Zeng [Psycho7])
  • ti.deactivate now supported on sparse data structures
  • GUI.circles (batched circle drawing) performance improved by 30x
  • Minor bug fixes (by Yubin Peng [archibate], Ye Kuang [k-ye])
  • Doc updated

• Full changelog