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Abstract:
The ability to use GPUs to power real-time analytics past the billion row threshold is already here. But what about a trillion rows? The technical challenges to overcome that hurdle are more complex and require a delicate balance of memory management, data serialization over the network, servers working in lockstep, and managing redundancy and single points of failure. We'll outline and demonstrate how MapD tackled this problem and, more importantly, how you can visualize the outputs of various queries.
The ability to use GPUs to power real-time analytics past the billion row threshold is already here. But what about a trillion rows? The technical challenges to overcome that hurdle are more complex and require a delicate balance of memory management, data serialization over the network, servers working in lockstep, and managing redundancy and single points of failure. We'll outline and demonstrate how MapD tackled this problem and, more importantly, how you can visualize the outputs of various queries.  Back
 
Topics:
Accelerated Data Science, AI Startup, Federal, Data Center & Cloud Infrastructure
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7511
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Abstract:

Today database performance records are being shattered by new innovative ways of tackling big data problems. We're calling it "fast data" and we're leveraging the power of GPUs to query 40 billion dataset rows in just milliseconds. Thanks to a collaboration between MapD, Bitfusion, IBM Cloud and NVIDIA no data problem is too big or complex to process. Using Bitfusion's Boost software, MapD was able to leverage over 64 NVIDIA Tesla GPUs across 16 IBM Cloud servers to filter and aggregate multi-billion row datasets in just milliseconds. Seeing is believing. Come find out why GPUs are quickly becoming the engine for the next generation of enterprise computing applications. 

Today database performance records are being shattered by new innovative ways of tackling big data problems. We're calling it "fast data" and we're leveraging the power of GPUs to query 40 billion dataset rows in just milliseconds. Thanks to a collaboration between MapD, Bitfusion, IBM Cloud and NVIDIA no data problem is too big or complex to process. Using Bitfusion's Boost software, MapD was able to leverage over 64 NVIDIA Tesla GPUs across 16 IBM Cloud servers to filter and aggregate multi-billion row datasets in just milliseconds. Seeing is believing. Come find out why GPUs are quickly becoming the engine for the next generation of enterprise computing applications. 

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Topics:
HPC and Supercomputing, Big Data Analytics, Data Center & Cloud Infrastructure
Type:
Talk
Event:
GTC Europe
Year:
2016
Session ID:
SEU6232
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