sixth moment recommendation engines for every business

Sixth MomentComputing

recommendation systems for every business

online retail digital media

recommendation systems for every business

mobile apps

online gaming

cloud infrastructure

affordabilityavailabilityscalability

accelerated computing

less timeless energy

less dollars

accelerating big data

Shazam

eBay / Cortexica

Twitter / Salesforce.com

10M querier per day against27M content library

500+ keypoint fngerprint search of like things

500M tweets against1M expressions daily

Jen-Hsun HuangNvidia CEO & co-founderAnnual Investor Day 2013

big data platforms

MapReducee.g. Hadoop

traditionaldatabase cluster

e.g. MPI

multicore+ accelerators

ease of programming simple analytics

ease of programming complex analytics

performance

energy

data placementexternal

diskinternalmemory

conceptborrowed

from slides ofDavid A. Bader

Steve McConnellCode Complete

ideaborrowed

from slides ofMichael A. Heroux

efficiency vs other quality metricsHow focusing on the factor below afects the factor to the right

correctness

usability

efficiency

reliability

integrity

adaptability

accuracy

robustness

corr

ectn

ess

usab

ility

effici

ency

relia

bilit

y

inte

grity

adap

tabi

lity

accu

racy

robu

stne

ss

helps it

hurts it

Efficiency is the hard part.Improving efficiency hurtsall the other quality metrics.

case studieshardware specs

2x Intel Sandy Bridge8 cores (16 threads) / CPU

2.6 GHz

2x Nvidia Tesla K202688 CUDA cores / GPU

705 MHz

case studysmall

1 million users20 thousand items50 million records(50 items per user on average)

20 most similar items for each item20 recommendations for each user(400 thousand total similarities)(20 million total recommendations)

nearest neighbor algorithm (item-based)Tanimoto (a.k.a. Jaccard) similarity20 nearest neighbors per item

2 minutes(120 seconds)

case studysmall

1 million users20 thousand items50 million records(50 items per user on average)

20 most similar items for each item20 recommendations for each user(400 thousand total similarities)(20 million total recommendations)

latent variable model100 features per user / itemalternating least squares algorithm (10 iterations)

2 minutes20 seconds(140 seconds)

case studymedium

10 million users20 thousand items500 million records(50 items per user on average)

20 most similar items for each item20 recommendations for each user(400 thousand total similarities)(200 million total recommendations)

nearest neighbor algorithm (item-based)Tanimoto (a.k.a. Jaccard) similarity20 nearest neighbors per item

28 minutes

case studymedium

10 million users20 thousand items500 million records(50 items per user on average)

20 most similar items for each item20 recommendations for each user(400 thousand total similarities)(200 million total recommendations)

latent variable model100 features per user / itemalternating least squares algorithm (10 iterations)

32 minutes

Sixth MomentComputing

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