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GTC ON-DEMAND

Presentation
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Abstract:
Connected vehicles generate a treasure trove of anonymized data with unlimited potential for the automobile industry. Vehicle usage, fleet movement, mobility patterns, telematics and spatiotemporal data are invaluable for everyone from development to manufacturing. Hear how the BMW Group has built dashboards with OmniSci Immerse to visualize this telematics data. See a live demonstration of this application as it's used to visualize spatiotemporal telematics data and interact with massive datasets-in-motion with near-zero latency. Join Caroline Persson, Data Scientist at the BMW Group, and Todd Mostak, CEO of OmniSci, as they explain how the parallel processing power of GPUs unlocks a wealth of use cases across manufacturing and other major industries, driving operational analytics, geospatial analytics, and data science. Persson will provide an example use case and show how automobile manufacturers can ingest huge volumes of streaming telematics data coming from vehicles and apply machine learning models to descriptive driver behavior and patterns.
Connected vehicles generate a treasure trove of anonymized data with unlimited potential for the automobile industry. Vehicle usage, fleet movement, mobility patterns, telematics and spatiotemporal data are invaluable for everyone from development to manufacturing. Hear how the BMW Group has built dashboards with OmniSci Immerse to visualize this telematics data. See a live demonstration of this application as it's used to visualize spatiotemporal telematics data and interact with massive datasets-in-motion with near-zero latency. Join Caroline Persson, Data Scientist at the BMW Group, and Todd Mostak, CEO of OmniSci, as they explain how the parallel processing power of GPUs unlocks a wealth of use cases across manufacturing and other major industries, driving operational analytics, geospatial analytics, and data science. Persson will provide an example use case and show how automobile manufacturers can ingest huge volumes of streaming telematics data coming from vehicles and apply machine learning models to descriptive driver behavior and patterns.  Back
 
Topics:
Accelerated Data Science, Deep Learning & AI Frameworks
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9537
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Abstract:

It is common knowledge that GPUs can dramatically accelerate HPC and machine learning/AI workloads, but can they do the same for general purpose analytics? In this talk, Todd Mostak, CEO of MapD, will provide real-world examples of how a new generation of GPU-powered analytics platforms can enable enterprises from a range of verticals to dramatically accelerate the process of insight generation at scale. In particular, he will focus on how the key technical differentiators of GPUs: their massive computational bandwidth, fast memory, and native rendering pipeline, make them uniquely suited to allow analysts and data scientists to query, visualize and power machine learning over large, often high-velocity, datasets. Using the open source MapD analytics platform as an example, Todd will detail the technical approaches his team took to leverage the full parallelism of GPUs and demo how the platform allows analysts to interactively explore datasets containing tens of billions of records.

It is common knowledge that GPUs can dramatically accelerate HPC and machine learning/AI workloads, but can they do the same for general purpose analytics? In this talk, Todd Mostak, CEO of MapD, will provide real-world examples of how a new generation of GPU-powered analytics platforms can enable enterprises from a range of verticals to dramatically accelerate the process of insight generation at scale. In particular, he will focus on how the key technical differentiators of GPUs: their massive computational bandwidth, fast memory, and native rendering pipeline, make them uniquely suited to allow analysts and data scientists to query, visualize and power machine learning over large, often high-velocity, datasets. Using the open source MapD analytics platform as an example, Todd will detail the technical approaches his team took to leverage the full parallelism of GPUs and demo how the platform allows analysts to interactively explore datasets containing tens of billions of records.

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Topics:
Accelerated Data Science, AI Startup, GIS
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S81008
Streaming:
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Abstract:
Todd will explore the topic of GPUs & their role in geovisualisation. In particular, attendees will hear about how complex visualisations with massive amounts of geospatial data are an ideal match for GPUs, unlocking extreme speeds for interactive data exploration and real-time insight generation. The ability to instantly interact with billions of rows of geospatial data can be used across industries such as ad tech, energy, financial services, government, retail and service providers, allowing them to quickly find anomalies and drill-down into the individual level without pre-aggregating or downsampling.
Todd will explore the topic of GPUs & their role in geovisualisation. In particular, attendees will hear about how complex visualisations with massive amounts of geospatial data are an ideal match for GPUs, unlocking extreme speeds for interactive data exploration and real-time insight generation. The ability to instantly interact with billions of rows of geospatial data can be used across industries such as ad tech, energy, financial services, government, retail and service providers, allowing them to quickly find anomalies and drill-down into the individual level without pre-aggregating or downsampling.  Back
 
Topics:
Other
Type:
Talk
Event:
GTC Europe
Year:
2017
Session ID:
23345
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Abstract:
Due to the explosion of geospatial data from sensors, smart phones, social media, drones and other vehicles of transportation, new opportunities are arising for government agencies to leverage GPUs to analyze and visualize geospatial data. We'll discuss how GPU-accelerated analytics can be harnessed to extract the fastest insights possible from geospatial data, and how this technology is enabling federal agencies to take faster action and make more informed decisions around issues of security, voting, campaigns, political donations, public services and more.
Due to the explosion of geospatial data from sensors, smart phones, social media, drones and other vehicles of transportation, new opportunities are arising for government agencies to leverage GPUs to analyze and visualize geospatial data. We'll discuss how GPU-accelerated analytics can be harnessed to extract the fastest insights possible from geospatial data, and how this technology is enabling federal agencies to take faster action and make more informed decisions around issues of security, voting, campaigns, political donations, public services and more.  Back
 
Topics:
Developer Tools, Data Center & Cloud Infrastructure, HPC and Supercomputing
Type:
Talk
Event:
GTC Washington D.C.
Year:
2017
Session ID:
DC7189
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Abstract:
Although the major federal agencies have substantial technology budgets, they pale in comparison to the aggregate amount of funding going into startups around the world. Agencies are increasingly looking to outside vendors, especially startups, to provide advancements in critical technologies relating to AI, cybersecurity, and other mission-critical areas. Hear from a panel of experts, including the director of science and technology at the CIA, about how and why agencies are increasingly working with startups and emerging technologies. How can startups best position themselves to do business with the federal government? What areas of technology are currently the most attractive? What are the key issues that need to be addressed?
Although the major federal agencies have substantial technology budgets, they pale in comparison to the aggregate amount of funding going into startups around the world. Agencies are increasingly looking to outside vendors, especially startups, to provide advancements in critical technologies relating to AI, cybersecurity, and other mission-critical areas. Hear from a panel of experts, including the director of science and technology at the CIA, about how and why agencies are increasingly working with startups and emerging technologies. How can startups best position themselves to do business with the federal government? What areas of technology are currently the most attractive? What are the key issues that need to be addressed?  Back
 
Topics:
Leadership and Policy in AI
Type:
Panel
Event:
GTC Washington D.C.
Year:
2017
Session ID:
DC7244
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Abstract:
We'll discuss the approach to and advantages of using GPUs to not only power through large-scale database queries but also use the graphics pipeline of the GPU to rapidly and efficiently visualize the outputs of billions of rows of data. The application of the GPU for both query and render results in a fast system for multi-terabyte scale analytic challenges. We'll cover the high-level benefits of the approach and delve into the technical details associated with GPU-powered databases, server side rendering, and other software refinements needed to squeeze the maximum amount of performance from this exceptional hardware platform.
We'll discuss the approach to and advantages of using GPUs to not only power through large-scale database queries but also use the graphics pipeline of the GPU to rapidly and efficiently visualize the outputs of billions of rows of data. The application of the GPU for both query and render results in a fast system for multi-terabyte scale analytic challenges. We'll cover the high-level benefits of the approach and delve into the technical details associated with GPU-powered databases, server side rendering, and other software refinements needed to squeeze the maximum amount of performance from this exceptional hardware platform.  Back
 
Topics:
Accelerated Data Science, AI Startup, Federal, Real-Time Graphics
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7475
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Abstract:
We'll explain why GPU-powered in-memory databases and analytics platforms are the logical successor to CPU in-memory systems, largely due to recent increases in onboard memory available on GPUs. With sufficient memory, GPUs possess numerous advantages over CPUs, including much greater compute and memory bandwidth and a native graphics pipeline. We'll demo how MapD is able to leverage multiple GPUs per server to extract orders-of-magnitude performance increases over CPU-based systems, bringing interactive querying and visualization to multi-billion row datasets.
We'll explain why GPU-powered in-memory databases and analytics platforms are the logical successor to CPU in-memory systems, largely due to recent increases in onboard memory available on GPUs. With sufficient memory, GPUs possess numerous advantages over CPUs, including much greater compute and memory bandwidth and a native graphics pipeline. We'll demo how MapD is able to leverage multiple GPUs per server to extract orders-of-magnitude performance increases over CPU-based systems, bringing interactive querying and visualization to multi-billion row datasets.  Back
 
Topics:
Big Data Analytics, Performance Optimization
Type:
Talk
Event:
GTC Silicon Valley
Year:
2016
Session ID:
S6472
Streaming:
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Abstract:

As people wish to interactively explore increasingly larger datasets, existing tools are unable to deliver acceptable performance. The distributed-nature of systems like Spark leads to latencies detrimental to interactive data exploration, while single-node visualization solutions like Tableau and Qlikview are not powerful enough to deliver sub-second response times for even intermediate-sized datasets. In this talk, we will argue that dense GPU servers, containing 4-16 GPUs each, can provide analytics query throughput exceeding what can be achieved on even large clusters, while avoiding the latencies and complications associated with running over a network. We will look at MapD, which can query and visualize multi-billion row datasets in milliseconds, as an example of such a system. Finally, we will show how the significantly higher performance achievable with a GPU system translates into new modes and paradigms of data analysis.

As people wish to interactively explore increasingly larger datasets, existing tools are unable to deliver acceptable performance. The distributed-nature of systems like Spark leads to latencies detrimental to interactive data exploration, while single-node visualization solutions like Tableau and Qlikview are not powerful enough to deliver sub-second response times for even intermediate-sized datasets. In this talk, we will argue that dense GPU servers, containing 4-16 GPUs each, can provide analytics query throughput exceeding what can be achieved on even large clusters, while avoiding the latencies and complications associated with running over a network. We will look at MapD, which can query and visualize multi-billion row datasets in milliseconds, as an example of such a system. Finally, we will show how the significantly higher performance achievable with a GPU system translates into new modes and paradigms of data analysis.

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Topics:
Big Data Analytics, Data Center & Cloud Infrastructure, Real-Time Graphics
Type:
Talk
Event:
GTC Silicon Valley
Year:
2015
Session ID:
S5544
Streaming:
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Abstract:

Most of what claims to be interactive visualization of big datasets relies on one of two strategies: pre-canning and sampling. However, both of these techniques have well-known limitations. Enter Map-D, a distributed end-to-end data analytics and visualization platform that can run on any number of GPUs, allowing millisecond query latencies over multi-terabyte datasets. In addition to supporting ultra-fast relational table and array querying, Map-D uses the native graphics pipeline of the GPU to render 2D and 3D visualizations of the results. By streaming these visualizations to a user''s browser via interactive 30fps H264 video, it can appear as if billions of data points are in the DOM, even on low-powered mobile clients.

Most of what claims to be interactive visualization of big datasets relies on one of two strategies: pre-canning and sampling. However, both of these techniques have well-known limitations. Enter Map-D, a distributed end-to-end data analytics and visualization platform that can run on any number of GPUs, allowing millisecond query latencies over multi-terabyte datasets. In addition to supporting ultra-fast relational table and array querying, Map-D uses the native graphics pipeline of the GPU to render 2D and 3D visualizations of the results. By streaming these visualizations to a user''s browser via interactive 30fps H264 video, it can appear as if billions of data points are in the DOM, even on low-powered mobile clients.

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Topics:
Visual Computing Theater
Type:
Talk
Event:
SIGGRAPH
Year:
2014
Session ID:
SIG4151
Streaming:
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Abstract:

map-D makes big data interactive for anyone! map-D is a super-fast GPU database that allows anyone to interact and visualize streaming big data in real time. Its unique architecture runs 70-1,000x faster than other in-memory databases or big data analytics platforms. To boot, it works with any size or kind of dataset; works with data that is streaming live on to the system; uses cheap, off-the-shelf hardware; is easily scalable.map-D is focused on learning from big data. At the moment, the map-D team is working on projects with MIT CSAIL, the Harvard Center for Geographic Analysis and the Harvard-Smithsonian Center for Astrophysics. Join Todd Mostak and Tom Graham, key members of the map-D team, as they demonstrate the speed and agility of map-D and describe the live processing, search and mapping of over 1 billion tweets.

map-D makes big data interactive for anyone! map-D is a super-fast GPU database that allows anyone to interact and visualize streaming big data in real time. Its unique architecture runs 70-1,000x faster than other in-memory databases or big data analytics platforms. To boot, it works with any size or kind of dataset; works with data that is streaming live on to the system; uses cheap, off-the-shelf hardware; is easily scalable.map-D is focused on learning from big data. At the moment, the map-D team is working on projects with MIT CSAIL, the Harvard Center for Geographic Analysis and the Harvard-Smithsonian Center for Astrophysics. Join Todd Mostak and Tom Graham, key members of the map-D team, as they demonstrate the speed and agility of map-D and describe the live processing, search and mapping of over 1 billion tweets.

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Topics:
Databases, Data Mining, Business Intelligence, Defense
Type:
Webinar
Event:
GTC Webinars
Year:
2014
Session ID:
GTCE073
Streaming:
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Abstract:

Map-D (Massively Parallel Database) uses multiple NVIDIA GPUs to interactively query and visualize big data in real-time. Map-D is an SQL-enabled column store that generates 70-400X speedups over other in-memory databases. This talk discusses the basic architecture of the system, the advantages and challenges of running queries on the GPU, and the implications of interactive and real-time big data analysis in the social sciences and beyond.

Map-D (Massively Parallel Database) uses multiple NVIDIA GPUs to interactively query and visualize big data in real-time. Map-D is an SQL-enabled column store that generates 70-400X speedups over other in-memory databases. This talk discusses the basic architecture of the system, the advantages and challenges of running queries on the GPU, and the implications of interactive and real-time big data analysis in the social sciences and beyond.

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Topics:
HPC and Supercomputing
Type:
Talk
Event:
Supercomputing
Year:
2013
Session ID:
SC3115
Streaming:
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