Code Generation is an important field to predict explicit code or program structure from multimodal data sources such as incomplete code, programs in another programming language, natural language descriptions or execution examples. Code Generation tools can assist the development of automatic programming tools to improve programming productivity.
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We contribute a process for SQL-code generation in Python using template macros in Jinja2 as well as the prototype implementation of the process.
Despite recent research efforts, the vision of automatic code generation through API recommendation has not been realized.
Moreover, we support the APIs of several ML libraries and frameworks for the automated generation of the source code of the target software in Python and Java.
The Virtual Brain (TVB) is now available as open-source cloud ecosystem on EBRAINS, a shared digital research platform for brain science.
The focus of this contribution is to describe a novel implementation of hierarchical estimators of the Bank-Weiser type in a modern high-level finite element software with automatic code generation capabilities.
CODE GENERATION NUMERICAL ANALYSIS COMPUTATIONAL ENGINEERING, FINANCE, AND SCIENCE NUMERICAL ANALYSIS
Micro-core architectures combine many simple, low memory, low power-consuming CPU cores onto a single chip.
CODE GENERATION PROGRAMMING LANGUAGES DISTRIBUTED, PARALLEL, AND CLUSTER COMPUTING
A great part of software development involves conceptualizing or communicating the underlying procedures and logic that needs to be expressed in programs.
With the increasing demand to efficiently deploy DNNs on mobile edge devices, it becomes much more important to reduce unnecessary computation and increase the execution speed.
We show in this work that memory intensive computations can result in severe performance problems due to off-chip memory access and CPU-GPU context switch overheads in a wide range of deep learning models.