Cling – The New Interactive Interpreter for ROOT 6

Vasilev, V; Canal, Philippe; Naumann, N. A.; Russo, Paul S.

doi:10.1088/1742-6596/396/5/052071

Cited by 38 publications

(30 citation statements)

References 1 publication

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“…On top of that, Clad is integrated directly into ROOT to provide AD functionality as an integral part of the framework. ROOT has a C++ interpreter Cling [17] which is built on the top of LLVM and Clang. This allows Clad to be attached to Cling as a plugin in a similar way as it can be attached to Clang.…”

Section: Architecture and Implementationmentioning

confidence: 99%

Automatic Differentiation in ROOT

2020

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In mathematics and computer algebra, automatic differentiation (AD) is a set of techniques to evaluate the derivative of a function specified by a computer program. AD exploits the fact that every computer program, no matter how complicated, executes a sequence of elementary arithmetic operations (addition, subtraction, multiplication, division, etc.), elementary functions (exp, log, sin, cos, etc.) and control flow statements. AD takes source code of a function as input and produces source code of the derived function. By applying the chain rule repeatedly to these operations, derivatives of arbitrary order can be computed automatically, accurately to working precision, and using at most a small constant factor more arithmetic operations than the original program. This paper presents AD techniques available in ROOT, supported by Cling, to produce derivatives of arbitrary C/C++ functions through implementing source code transformation and employing the chain rule of differential calculus in both forward mode and reverse mode. We explain its current integration for gradient computation in TFormula. We demonstrate the correctness and performance improvements in ROOT’s fitting algorithms.

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Section: Architecture and Implementationmentioning

confidence: 99%

Automatic Differentiation in ROOT

2020

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“…Cling [13], the interactive compiler in ROOT, allows the use of just-in-time compiler for compilation. In practice, this means that we can compile hard-coded evaluation logic parsed from the decision-tree models.…”

Section: Just-in-time (Jit) Compilationmentioning

confidence: 99%

Fast Inference for Machine Learning in ROOT/TMVA

Albertsson

Moneta

et al. 2020

EPJ Web Conf.

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ROOT provides, through TMVA, machine learning tools for data analysis at HEP experiments and beyond. However, with the rapidly evolving ecosystem for machine learning, the focus of TMVA is shifting. We present the new developments and strategy of TMVA, which will allow the analyst to integrate seamlessly, and effectively, different workflows in the diversified machine-learning landscape. Focus is put on a fast machine learning inference system, which will enable analysts to deploy their machine learning models rapidly on large scale datasets. We present the technical details of a fast inference system for decision tree algorithms, included in the next ROOT release (6.20). We further present development status and proposal for a fast inference interface and code generator for ONNX-based Deep Learning models.

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“…Figure 5 shows the runtime for XGBoost and two different BDT inference backends. Since ROOT comes with the C++ interpreter cling [21], we are able to just-in-time compile (jit) code at runtime, which is useful for the optimization of inference code to static runtime parameters such as the depth of the trees. TMVA provides two backends, one which compiles the inference code using cling and a second backend computing the predictions without jitting.…”

Section: Fast Decision Tree Inferencementioning

confidence: 99%

Machine Learning with ROOT/TMVA

Albertsson

Gleyzer

et al. 2020

EPJ Web Conf.

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ROOT provides, through TMVA, machine learning tools for data analysis at HEP experiments and beyond. We present recently included features in TMVA and the strategy for future developments in the diversified machine learning landscape. Focus is put on fast machine learning inference, which enables analysts to deploy their machine learning models rapidly on large scale datasets. The new developments are paired with newly designed C++ and Python interfaces supporting modern C++ paradigms and full interoperability in the Python ecosystem. We present as well a new deep learning implementation for convolutional neural network using the cuDNN library for GPU. We show benchmarking results in term of training time and inference time, when comparing with other machine learning libraries such as Keras/Tensorflow.

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Cling – The New Interactive Interpreter for ROOT 6

Cited by 38 publications

References 1 publication

Automatic Differentiation in ROOT

Automatic Differentiation in ROOT

Fast Inference for Machine Learning in ROOT/TMVA

Machine Learning with ROOT/TMVA

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