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Reduce overhead of read_data and write_data

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评估

难度
4/5
预计耗时
3-5 天
新手友好度
35/100
Issue 类型
重构
描述清晰度
基本清楚
活跃度
停滞
技术栈
numpy, python
领域
api, performance

调研方向

从 Python 绑定入口 read_data 和 write_data 开始,使用链接的 Discourse 讨论串来了解报告的开销,以及有关 reshape、分配和错误检查的说明。将 Python 侧的计时结果与 preCICE 的计时结果进行比较,然后验证这些函数在避免不必要的复制和分配的同时仍保持其行为。

由索引模型根据 Issue 内容生成。

描述

In this discourse thread, I tracked down the increased duration spent in the "do-nothing solver" down to read_data and write_data.

Most logical explanation would be the additional

  • input vertex_ids and values are copied to a vector, even though passing np.reshape(X, -1) to the preCICE API suffices and prevents copies.
  • output values are allocated, then passed to the API, then allocated to build an np.array
  • we do a lot of additional error checking (which is good)

Example of rhoVW on solver2, being vectorial data of large mesh:

  • Time measured in preCICE: 7ms (note: this doesn't allocate)
  • Time measured in Python: 40ms (including overhead from activating profiling in python, this needs to allocate, so overhead scales with size)

Notes:

  • With some tweaking I can get this down to 30ms. This makes the function actually shorter, simpler, and easier to follow.
  • np.flatten() copies the input, while np.reshape doesn't if it can avoid it.
  • The majority of the generated code seems to be error handling, which we could potentially be avoided by using the CPP API directly for calls to getDataDimensions and do this in one place.
  • This overhead could be profiled with something like https://github.com/precice/precice/issues/1647
主要语言
Cython
星标
30
派生
19
PR 合并指标
30 天内没有已合并 PR

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  3. Fork 仓库,在一个分支上完成修改。
  4. 提交 Pull Request,并在描述里引用这个 Issue 编号。

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