Closed ggservice007 closed 3 years ago
year_2015_data_parallel_via_simd_vector_type.pdf
The ray base layer is implemented by using C++. So we can write some simd utility to provide the Python user.
Hey, Ray shouldn't interfere with the native execution of other libraries which rely on SIMD instructions (so you should get the full power of SIMD from pytorch/tensorflow/arrow already). Could your provide an example where you thing Ray isn't doing/performing the way you expect it to?
I am developing a project which SIMD version cuGraph.
The first step is to implement the SIMD cudf.
I am implmeneting the SIMD DataFrames based on vaex or its ideas.
Vaex uses ApacheArrow data structures and C++ to speed up string operations.
Vaex is a high performance Python library for lazy Out-of-Core DataFrames (similar to Pandas). It calculates statistics such as mean, sum, count, standard deviation etc, on an N-dimensional grid for more than a billion (10^9) samples/rows per second.
https://github.com/vaexio/vaex
The next step is to use the parallel functionality provided by Ray.
I see, you can run any code that you can put in a python module (including any compiled cython) and Ray will just faithfully execute it.
Nice, more and more libraries to support SIMD. The numpy also supports SIMD after 1.20.0.
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Many high performance projects or libraries use the SIMD to accelerate such as apache arrows and pysimdjson
Apache Arrows https://arrow.apache.org/overview/
pysimdjson
https://github.com/TkTech/pysimdjson
Python bindings for the simdjson project, a SIMD-accelerated JSON parser. If SIMD instructions are unavailable a fallback parser is used, making pysimdjson safe to use anywhere.
Data-Parallel Programming via SIMD Vector Types
Can we some simd to parallel programming?