Video details

Interactive C++ for Data Science - Vassil Vassilev - CppCon 2021

English --- C++ is used for many numerically intensive applications. A combination of performance and solid backward compatibility has led to its use for many research software codes over the past 20 years. Despite its power, C++ is often seen as difficult to learn and not well suited with rapid application development. The long edit-compile-run cycle is a large impediment to exploration and prototyping during development.
Cling has emerged as a recognized capability that enables interactivity, dynamic interoperability and rapid prototyping capabilities for C++ developers. Cling is an interactive C++ interpreter, built on top of the Clang and LLVM compiler infrastructure. The interpreter enables interactive exploration and makes the C++ language more welcoming for research. Cling supports the full C++ feature set including the use of templates, lambdas, and virtual inheritance.Cling’s field of origin is the field of high energy physics where it facilitates the processing of scientific data. The interpreter was an essential part of the software tools of the LHC experimental program and was part of the software used to detect the gravitational waves of the LIGO experiment. Interactive C++ has proven to be useful for other communities. The Cling ecosystem includes dynamic bindings tools to languages including python, D and Julia (cppyy); C++ in Jupyter Notebooks (xeus-cling); interactive CUDA; and automatic differentiation on the fly (clad).
This talk outlines key properties of interactive C++ such as execution results, entity redefinition, error recovery and code undo. It demonstrates the capability enabled by an interactive C++ platform in the context of data science. For example, the use of eval-style programming, C++ in Jupyter notebooks and CUDA C++. We talk about design and implementation challenges and go beyond just interpreting C++. We showcase template instantiation on demand, language interoperability on demand and bridging compiled and interpreted code. We show how to easily build new capabilities using the Cling infrastructure through developing an automatic differentiation plugin for C++ and CUDA.
--- Vassil Vassilev
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