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GTC ON-DEMAND

Defense
Presentation
Media
Implementing Graph Analytics with Python and Numba
Abstract:
We demonstrate how to implement the densest k-subgraph algorithm by Papailiopoulos et al, using the Numba CUDA compiler for Python. With the rise of social networks, more data scientists want to study the connections within and between the communities that dynamically organize on the Internet. Python is a very productive language for data scientists, but, on its own, may not provide the performance needed to analyze big data sets. To bridge this gap, the Numba compiler allows CUDA kernels to be written directly in the Python language and compiled for GPU execution. Using the densest k-subgraph algorithm as an example, we will show how the agility of Python can be combined with the high performance of GPU computing for graph analytics.
 
Topics:
Defense, Big Data Analytics, Programming Languages, Developer - Algorithms
Type:
Talk
Event:
GTC Silicon Valley
Year:
2015
Session ID:
S5419
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