Big Data Smarter Networks

•

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DataWorks Summit

Tecnología

Big Data, Smarter Networks: Optimizing the
Network Infrastructure for Big Data

Slide 1 | PROPRIETARY AND CONFIDENTIAL
Copyright © 2011 Solarflare Communications, Inc. All Rights Reserved.

Introducing Solarflare

•  High-performance software
and hardware for 10GbE
server networking
•  Mission-critical applications
–  Securities and trading
“Solarflare’s product, EnterpriseOnload is a
–  HPC robust, rigorously tested and fully supported
solution that addresses our demanding support
–  Storage and service level requirements. In addition to
providing the highest-performance, lowest-
–  Cloud/Web 2.0 latency hardware, Solarflare’s unique and
•  Leader in financial services innovative application acceleration software
can be used to deploy quickly without any need
•  Partnerships with Arista, to re-write our applications.”
Cisco, Couchbase, IBM, Andrew Bach
Juniper, Redhat, Oracle, Senior Vice President of Network Services for NYSE Euronext

VMware

Slide 2 | PROPRIETARY AND CONFIDENTIAL

Data is Growing Faster than Moore’s Law

Business Analytics Requires a New Approach

Data Volume
Growing 44x
2010:
1.2
Zettabytes 2020: 35.2
Zettabytes IDC
Digital Universe
Study 2011
The Network is the Bottleneck

Slide 3 | PROPRIETARY AND CONFIDENTIAL
Source: IDC Digital Universe Study, sponsored by EMC, May 2010

But Computing is More than Just Moore’s Law

•  SandyBridge-Based
Servers/ Aka Romley
Motherboards
•  Intel Integrated I/0
Up to 4 channels
DDR3 1600 Mhz
•  Reduced bottlenecks
memory
•  Increase performance
Up to 8 cores
Up to 20 MB cache
up to 80%
Integrated
PCI Express*
3.0
Up to 40 lanes
per socket
Can more than Double
I/O Performance1

Xeon
Direct Data I/O (DDIO) 2600
Family

Xeon
5600
Series

[ Transactions per second ]
Slide 4 | PROPRIETARY AND CONFIDENTIAL

SFN6122F & Xeon E5-2600 Deliver Winning Combination

•  SFN6122F single-stream
latency is superb over all
message rates on Romley
platforms, right up to the
point of CPU core utilization

•  Ultra-low jitter (sub-micro at
99Percentile)

•  Benefits from Intel® Data
Direct I/O (DDIO) and
chipset IO – memory
bandwidth

•  Message rate headroom –
sfnt-stream / openonload-201109-u2
20Mpps with 4x sfnt-streams
“Westmere” = 2x Xeon 5687 (3.6GHz)
“Romley” = 2x E5-2687W (3.1GHz) – DDR 1333

Slide 5 | PROPRIETARY AND CONFIDENTIAL

Standard Server I/O Networking: RSS

•  RSS spreads flows
randomly over cores
NIC receive-side scaling •  Packets within a flow go to
VNIC VNIC VNIC VNIC same core
•  Works well when
–  Connections
ISR ISR ISR ISR
are long lived
–  One thread per
connection
App App App App
–  And the thread
happens to run
core0 core1 core2 core3 on the right
core
–  Great for
network
benchmarks
Slide 6 | PROPRIETARY AND CONFIDENTIAL

Smarter Server I/O Networking: Flow Affinity

NIC receive-side scaling •  Hardware deterministically
VNIC VNIC VNIC VNIC
directs flows to the ideal
core for handling the load
•  Google developed
ISR ISR ISR ISR Receive packet steering
(RPS)
•  Solarflare developed
Accelerated RFS for
App App App App multiqueue hardware
core0 core1 core2 core3 •  Supported today on
Solarflare adapters

Slide 7 | PROPRIETARY AND CONFIDENTIAL

Cisco and Solarflare Achieve Dramatic Latency Reductions
for Interactive Web 2.0 Applications

Slide 8 | PROPRIETARY AND CONFIDENTIAL

Big Data Management Solution

I/O Network CPU Hadoop OS Big Data
Interface Framework Management

3 - ioDrive2
Duo
1.2TB SFN5122F
ioMemory SFP + Dual Port 10GbE
3 GBytes/sec Server Adapter
2 - X5690 6 Core
12 Threads, 3.4 GHz
96GB RAM
V 1.0
Enterprise 6.2

•  Hyve BigD series8 from Synnex
•  Turnkey appliance-based big data management solution
•  Fully integrated, optimized, and easily deployed, enabling rapid
implementation of a Hadoop environment in days rather than months
June 20, 2012 © 2012 Zettaset, Inc. | Proprietary and
Slide 9 | PROPRIETARY AND CONFIDENTIAL Confidential 9

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Background: Some early applications of Computer Vision in Retail arose from e-commerce use cases - but increasingly, it is being used in physical stores in a variety of new and exciting ways, such as: ● Optimizing merchandising execution, in-stocks and sell-thru ● Enhancing operational efficiencies, enable real-time customer engagement ● Enhancing loss prevention capabilities, response time ● Creating frictionless experiences for shoppers Abstract: This talk will cover the use of Computer Vision in Retail, the implications to the broader Consumer Goods industry and share business drivers, use cases and benefits that are unfolding as an integral component in the remaking of an age-old industry. We will also take a ‘peek under the hood’ of Computer Vision and Deep Learning, sharing technology design principles and skill set profiles to consider before starting your CV journey. Deep learning has matured considerably in the past few years to produce human or superhuman abilities in a variety of computer vision paradigms. We will discuss ways to recognize these paradigms in retail settings, collect and organize data to create actionable outcomes with the new insights and applications that deep learning enables. We will cover the basics of object detection, then move into the advanced processing of images describing the possible ways that a retail store of the near future could operate. Identifying various storefront situations by having a deep learning system attached to a camera stream. Such things as; identifying item stocks on shelves, a shelf in need of organization, or perhaps a wandering customer in need of assistance. We will also cover how to use a computer vision system to automatically track customer purchases to enable a streamlined checkout process, and how deep learning can power plausible wardrobe suggestions based on what a customer is currently wearing or purchasing. Finally, we will cover the various technologies that are powering these applications today. Deep learning tools for research and development. Production tools to distribute that intelligence to an entire inventory of all the cameras situation around a retail location. Tools for exploring and understanding the new data streams produced by the computer vision systems. By the end of this talk, attendees should understand the impact Computer Vision and Deep Learning are having in the Consumer Goods industry, key use cases, techniques and key considerations leaders are exploring and implementing today.

Computer Vision: Coming to a Store Near You

DataWorks Summit

Whole genome shotgun based next generation transcriptomics and metagenomics studies often generate 100 to 1000 gigabytes (GB) sequence data derived from tens of thousands of different genes or microbial species. De novo assembling these data requires an ideal solution that both scales with data size and optimizes for individual gene or genomes. Here we developed an Apache Spark-based scalable sequence clustering application, SparkReadClust (SpaRC), that partitions the reads based on their molecule of origin to enable downstream assembly optimization. SpaRC produces high clustering performance on transcriptomics and metagenomics test datasets from both short read and long read sequencing technologies. It achieved a near linear scalability with respect to input data size and number of compute nodes. SpaRC can run on different cloud computing environments without modifications while delivering similar performance. In summary, our results suggest SpaRC provides a scalable solution for clustering billions of reads from the next-generation sequencing experiments, and Apache Spark represents a cost-effective solution with rapid development/deployment cycles for similar big data genomics problems.

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2. Introducing Solarflare •  High-performance software and hardware for 10GbE server networking •  Mission-critical applications –  Securities and trading “Solarflare’s product, EnterpriseOnload is a –  HPC robust, rigorously tested and fully supported solution that addresses our demanding support –  Storage and service level requirements. In addition to providing the highest-performance, lowest- –  Cloud/Web 2.0 latency hardware, Solarflare’s unique and •  Leader in financial services innovative application acceleration software can be used to deploy quickly without any need •  Partnerships with Arista, to re-write our applications.” Cisco, Couchbase, IBM, Andrew Bach Juniper, Redhat, Oracle, Senior Vice President of Network Services for NYSE Euronext VMware Slide 2 | PROPRIETARY AND CONFIDENTIAL

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4. But Computing is More than Just Moore’s Law •  SandyBridge-Based Servers/ Aka Romley Motherboards •  Intel Integrated I/0 Up to 4 channels DDR3 1600 Mhz •  Reduced bottlenecks memory •  Increase performance Up to 8 cores Up to 20 MB cache up to 80% Integrated PCI Express* 3.0 Up to 40 lanes per socket Can more than Double I/O Performance1 Xeon Direct Data I/O (DDIO) 2600 Family Xeon 5600 Series [ Transactions per second ] Slide 4 | PROPRIETARY AND CONFIDENTIAL

5. SFN6122F & Xeon E5-2600 Deliver Winning Combination •  SFN6122F single-stream latency is superb over all message rates on Romley platforms, right up to the point of CPU core utilization •  Ultra-low jitter (sub-micro at 99Percentile) •  Benefits from Intel® Data Direct I/O (DDIO) and chipset IO – memory bandwidth •  Message rate headroom – sfnt-stream / openonload-201109-u2 20Mpps with 4x sfnt-streams “Westmere” = 2x Xeon 5687 (3.6GHz) “Romley” = 2x E5-2687W (3.1GHz) – DDR 1333 Slide 5 | PROPRIETARY AND CONFIDENTIAL

6. Standard Server I/O Networking: RSS •  RSS spreads flows randomly over cores NIC receive-side scaling •  Packets within a flow go to VNIC VNIC VNIC VNIC same core •  Works well when –  Connections ISR ISR ISR ISR are long lived –  One thread per connection App App App App –  And the thread happens to run core0 core1 core2 core3 on the right core –  Great for network benchmarks Slide 6 | PROPRIETARY AND CONFIDENTIAL

7. Smarter Server I/O Networking: Flow Affinity NIC receive-side scaling •  Hardware deterministically VNIC VNIC VNIC VNIC directs flows to the ideal core for handling the load •  Google developed ISR ISR ISR ISR Receive packet steering (RPS) •  Solarflare developed Accelerated RFS for App App App App multiqueue hardware core0 core1 core2 core3 •  Supported today on Solarflare adapters Slide 7 | PROPRIETARY AND CONFIDENTIAL

8. Cisco and Solarflare Achieve Dramatic Latency Reductions for Interactive Web 2.0 Applications Slide 8 | PROPRIETARY AND CONFIDENTIAL

9. Big Data Management Solution I/O Network CPU Hadoop OS Big Data Interface Framework Management 3 - ioDrive2 Duo 1.2TB SFN5122F ioMemory SFP + Dual Port 10GbE 3 GBytes/sec Server Adapter 2 - X5690 6 Core 12 Threads, 3.4 GHz 96GB RAM V 1.0 Enterprise 6.2 •  Hyve BigD series8 from Synnex •  Turnkey appliance-based big data management solution •  Fully integrated, optimized, and easily deployed, enabling rapid implementation of a Hadoop environment in days rather than months June 20, 2012 © 2012 Zettaset, Inc. | Proprietary and Slide 9 | PROPRIETARY AND CONFIDENTIAL Confidential 9

10. Thank You! Page 10

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