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GTC ON-DEMAND

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This talk will present the results of running the following Graph500 and DARPA Graph Challenge benchmarks and highlight the improvements over other platforms: BFS Graph500 • Single Source Shortest Paths Graph500 • PageRank Pipeline Graph Challenge • Triangle Counting Graph Challenge • K-Truss Graph Challenge The tremendous performance advantages of the DGX-2 platform for deep-learning has recently gained a lot of publicity. However, that is not the only analytic environment that can take advantage of the DGX-2 architecture. Having sixteen fully connected 32GB Volta GPUs presents an intriguing platform for Graph Analytics. The 512GB of combined GPU memory and full NVLink connection between the GPUs offers a number of advantages over a distributed MPI-based approach.

This talk will present the results of running the following Graph500 and DARPA Graph Challenge benchmarks and highlight the improvements over other platforms: BFS Graph500 • Single Source Shortest Paths Graph500 • PageRank Pipeline Graph Challenge • Triangle Counting Graph Challenge • K-Truss Graph Challenge The tremendous performance advantages of the DGX-2 platform for deep-learning has recently gained a lot of publicity. However, that is not the only analytic environment that can take advantage of the DGX-2 architecture. Having sixteen fully connected 32GB Volta GPUs presents an intriguing platform for Graph Analytics. The 512GB of combined GPU memory and full NVLink connection between the GPUs offers a number of advantages over a distributed MPI-based approach.

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Topics:
Accelerated Data Science
Type:
Talk
Event:
GTC Washington D.C.
Year:
2018
Session ID:
DC8110
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Abstract:
Modern data science workflows typically combine multiple tools, APIs, and technologies. Therefore, shared standard data structures are essential to building an efficient workflow. Without a shared data structure, workflows must copy and convert data when going from one tool to another. Data movement and conversion introduces latency and unneeded complexity.  The GPU Open Analytics Initiative (GOAI) is an effort by Continuum Analytics, H2O.ai, MapD, BlazingDB, Graphistry and the Gunrock project (UC Davis) with the aim to create open frameworks that allow developers and data scientists to build applications using standard data formats and APIs on GPUs.  This will allow data scientist to seamlessly move between tools at GPU-accelerated speeds. This talk will present the various components of the GOAI framework and walk through a typical data science task using GOAI.
Modern data science workflows typically combine multiple tools, APIs, and technologies. Therefore, shared standard data structures are essential to building an efficient workflow. Without a shared data structure, workflows must copy and convert data when going from one tool to another. Data movement and conversion introduces latency and unneeded complexity.  The GPU Open Analytics Initiative (GOAI) is an effort by Continuum Analytics, H2O.ai, MapD, BlazingDB, Graphistry and the Gunrock project (UC Davis) with the aim to create open frameworks that allow developers and data scientists to build applications using standard data formats and APIs on GPUs.  This will allow data scientist to seamlessly move between tools at GPU-accelerated speeds. This talk will present the various components of the GOAI framework and walk through a typical data science task using GOAI.  Back
 
Topics:
Developer Tools
Type:
Talk
Event:
GTC Washington D.C.
Year:
2017
Session ID:
DC7147
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