SC2015 AUSTIN, TEXAS

ACCELERATION GOES MAINSTREAM. A recap of Accelerating Innovation Day.

Acceleration Brought To You By

1. IBM Watson: 1.7x faster thanks to NVIDIA GPUs

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2. Xilinx and IBM: Announced a four-year relationship

for data center and network function virtualization

collaboration based on Xilinx FPGA accelerators.

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3. Accelerated Networking: Switch-IB 2 from Mellanox

and ExpEther Tech. 100GB/s speeds. Perfect for

POWER systems.

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4. New OpenPOWER-Based Systems: E4 Computing

Engineering and Penguin Computing have new systems

based on OpenPOWER.

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5. IBM Ports Key Applications: New Internet of Things, Spark,

Big Data and Cognitive Era applications ports.

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OpenPOWER—the perfect platform for developers.

Expanded GPU services on SuperVessel: GPU

computing as-a-service capabilities for Caffe, Torch

and Theano.

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Expanded FPGA Services on SuperVessel: Coherent

reconfigurable accelerators are now available to

developers via the cloud.

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New Cluster at University of Texas at Austin: A

POWER8 accelerated cluster is now available to

academic researchers.

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Oregon State University Expands OSUOSL:

Additional compute and memory capacity

for POWER8-based systems in the Open

Source Lab.

Read More Here

Find out how to use all of these here

CENTERS OF EXCELLENCE

POWER ACCELERATION AND DESIGN CENTERS

RESEARCH

USER GROUP

CLIENT CENTERS

INNOVATION CENTERS

The day-to-day impact of acceleration and faster computing.

Demos from Accelerating

Innovation Day

Watson Explorer Health Care Annotator Presented by Peter Hofstee/Tim Kaldewey/Kubilay Atasu

Challenge: Show the different sequence of

word clouds from health care documents

processed without acceleration versus with

FPGA acceleration.

Result: The accelerated searches process

more documents than the non-accelerated

searches in the same amount of time.

Real world takeaway: Doctors can learn

about allergy risks and find connections

between historical lab results for new patients

faster with FPGAs.

Adverse Drug Reaction Prediction Presented by: Randy Swanberg/Minsik Cho/ Rajesh Bordawekar

Challenge: Use a logistic regression model

with technology from OpenPOWER, Spark and

NVIDIA on a dataset from 2011 to predict drug –

drug interactions (DDIs).

Result: Predicted 73% of DDIs discovered after 2011.

Real world takeaway: Using a GPU accelerator to

evaluate a similar dataset from 2015 can accurately

predict yet undiscovered DDIs.

Watson Retrieve and Rank Presented by: Tim Kaldewey/David Tam/David Wendy

Challenge: Compare response times of

generic browser queries against Wikipedia data

for accelerated and un-accelerated searches.

Result: With NVIDIA Accelerators Watson is able

to answer questions 1.64x faster than before.

Real world takeaway: Practical implementation of

Watson comes one step closer to being real-time.

Degrees of Social Separation Presented by: Randy Swanberg/Jan Rellermeyer

Challenge: Calculate the degree of separation

for every actor to Kevin Bacon in a database of

10,000 movies with SPARK social analytics.

Result: With SPARK adjusted to ‘spill’ to a flash

device rather than a hard disk drive, a 4x reduction

in DRAM requirements was achieved.

Real world takeaway: POWER8 plus CAPI flash

acceleration makes it more practical to use SPARK

with highly iterative and demanding workloads.

Thanks!For more on the OpenPOWER Foundation

please visit Openpowerfoundation.org