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taichi_benchmark reviews and mentions
- Taichi Lang: A high-performance parallel programming language embedded in Python
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I compared the performance of numerical computations facilitated by different acceleration toolkits... And here are the results!
In fact, Taichi's compiler relies on heavy underlying engineering to enable high performance. In the aforementioned code snippet, the summation is actually an atomic operation, which cannot be parallelized and is thus subject to limited operation efficiency. The most commonly used method of parallel computing optimization in vector summation is reduction, which is one of the must-have skills to learn parallel computing. The benchmark report concludes that, thanks to automatic reduction optimization implemented by its compiler, Taichi achieves a performance comparable to manually implemented CUB and way better than Numba.
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Accelerate Python code 100x by import taichi as ti
Sure, here are some benchmarks that could be helpful: https://github.com/taichi-dev/taichi_benchmark
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ETH Zürich uses Taichi Lang in its Physically-based Simulation course (AS 21)
Some are uncertain about Taichi Lang's performance against other frameworks such as CUDA, and would require a comprehensive benchmarking report. Actually, we published a systematic benchmarking report when we released Taichi Lang v1.0.0. Taichi has more or less comparable performance to CUDA. But, for sure, you will have much fewer lines of code with Taichi Lang!
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A note from our sponsor - WorkOS
workos.com | 23 Apr 2024
Stats
taichi-dev/taichi_benchmark is an open source project licensed under Apache License 2.0 which is an OSI approved license.
The primary programming language of taichi_benchmark is Python.
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