Tag Archives: Project ProThOS

– HP-DLF

HP-DLF: High Performance Deep Learning Framework

The goal of HP-DLF is to provide researchers and developers in the “deep learning” domain an easy access to current and future high-performance computing systems. For this purpose, a new software framework will be developed, which automates the highly complex parallel training of large neural networks on heterogeneous computing clusters. The focus is on scaling and energy efficiency, as well as high portability and user transparency. The goal is to scale the training of networks designed in existing frameworks, without additional user effort, over a three-digit number of compute nodes.

AnyDSL

AnyDSL – A Framework for Rapid Development of Domain-Specific Libraries

AnyDSL is a framework for domain-specific libraries (DSLs). These are implemented in our language Impala. In order to achieve high-performance, Impala partially evaluates any abstractions these libraries might impose. Partial evaluation and other optimizations are performed on AnyDSL’s intermediate representation Thorin.

More information can be found on the AnyDSL website: http://anydsl.github.io