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Title: Declarative interfaces for HEP data analysis: FuncADL and ADL/CutLang
Abstract Analysis description languages are declarative interfaces for HEP data analysis that allow users to avoid writing event loops, simplify code, and enable performance improvements to be decoupled from analysis development. One example is FuncADL, inspired by functional programming and developed using Python as a host language. FuncADL borrows concepts from database query languages to isolate the interface from the underlying physical and logical schemas. The same query can be used to select data from different sources and formats and with different execution mechanisms. FuncADL is one of the tools being developed by IRIS-HEP for highly scalable physics analysis for the LHC and HL-LHC. FuncADL is demonstrated by implementing example analysis tasks designed by HSF and IRIS-HEP. Another language example is ADL, which expresses the physics content of an analysis in a standard and unambiguous way, independent of computing frameworks. In ADL, analyses are described in human-readable text files composed of blocks with a keyword-expression structure. Two infrastructures are available to render ADL executable: CutLang, a runtime interpreter written in C++; and adl2tnm, a transpiler converting ADL into C++ or Python code. ADL/CutLang are already used in several physics studies and educational projects, and are adapted for use with LHC Open Data.  more » « less
Award ID(s):
1836650
NSF-PAR ID:
10430287
Author(s) / Creator(s):
; ; ; ; ; ;
Date Published:
Journal Name:
Journal of Physics: Conference Series
Volume:
2438
Issue:
1
ISSN:
1742-6588
Page Range / eLocation ID:
012075
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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