Beyond Data and Model Parallelism for Deep Neural Networks — mentioned by eslaught

Wed, 19 Sep 2018 04:53:16 GMT

Wow, this is a tough set of questions! Some initial thoughts: > How does Legion differ from languages like Halide which separate scheduling from the algorithm? Yes, that's one of the similarities. Legion, Sequoia, and Halide all share a separation between the specification of the algorithm from the mapping to any particular architecture. The biggest difference between Legion and Halide, aside from the fact that Halide is much more domain-specific to image processing, is that Legion (being a dynamic runtime system) focuses more on higher-level orchestration of tasks executing in a distributed…

Paper and discussion history. A mention is not an endorsement.

Beyond Data and Model Parallelism for Deep Neural Networks — HN submission

Tue, 31 Jul 2018 15:08:05 GMT

Beyond Data and Model Parallelism for Deep Neural Networks (Stanford)

3 points; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.

Beyond Data and Model Parallelism for Deep Neural Networks · HackerPapers discussions

Beyond Data and Model Parallelism for Deep Neural Networks — mentioned by eslaught

Beyond Data and Model Parallelism for Deep Neural Networks — HN submission