Transcript IPDPS_2011_Vipin

25th Year Panel – WHAT’S AHEAD
Vipin Kumar
University of Minnesota
[email protected]
http://www.cs.umn.edu/~kumar
© Vipin Kumar
IPDPS-2011, May 18, 2011
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Applications
What will be the next wave of grand
challenge problems to focus on in the
next 10 years and beyond ?
© Vipin Kumar
IPDPS-2011, May 18, 2011
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Transition:
Compute centric to Data Centric
Compute Intensive
Data Intensive
© Vipin Kumar
IPDPS-2011, May 18, 2011
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Transition:
Compute centric to Data Centric
Compute Intensive
Data Intensive
Enabled by 6 decades of exponential growth in computing power, storage
capacity, networking and more recent development of the Internet technology,
data and compute clouds
© Vipin Kumar
IPDPS-2011, May 18, 2011
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Transition:
Compute centric to Data Centric
Compute Intensive
Data Intensive
© Vipin Kumar
IPDPS-2011, May 18, 2011
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Great Challenges Facing the Society
Improving health care and reducing costs
Predicting the impact of climate change
Finding alternative/ green energy sources
Reducing hunger and poverty by
increasing agriculture production
© Vipin Kumar
IPDPS-2011, May 18, 2011
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Scalable Data Analysis
General Circulation Models: Mathematical models
with physical equations based on fluid dynamics
Cell
Parameterization
and non-linearity
of differential
equations are
sources for
uncertainty!
Clouds
Land
Ocean
Anomalies from 1880-1919 (K)
Example: Understanding Climate Change
Figure Courtesy: ORNL
Detection of Global Dipole Structure
© Vipin Kumar
IPDPS-2011, May 18, 2011
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Most challenging algorithmic problems
Dense vs. Sparse
Structured versus Unstructured
Static vs. Dynamic
Data intensive computations tend to be unstructured, sparse
and dynamic
Restructuring algorithms for locality key to scalability
Crucial in the context of emerging architectures based on multi-core,
GPUs,…
© Vipin Kumar
IPDPS-2011, May 18, 2011
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Software
How will we ever be able to hide parallelism
obstacles for the masses and what is the road
forward towards that ?
© Vipin Kumar
IPDPS-2011, May 18, 2011
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Computing Platforms
How will we be able to keep improving the
performance growth of the past and what will be
the major challenges in the next 10 years and
beyond that ? What technical barriers are
anticipated and what disruptive technologies are
behind the corner ?
© Vipin Kumar
IPDPS-2011, May 18, 2011
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Do We Need Benchmarks Specific to Data Intensive Computing ?
Performance metrics of several benchmarks gathered from Vtune
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Cluster Number
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Cache miss ratios, Bus usage, Page faults etc.
Benchmark applications were grouped using Kohenen clustering to spot
trends:
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9
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1
0
SPEC INT
SPEC FP
MediaBench
TPC-H
MineBench
gcc
bzip2
gzip
mcf
twolf
vortex
vpr
parser
apsi
art
equake
lucas
mesa
mgrid
swim
wupwise
rawcaudio
epic
encode
cjpeg
mpeg2
pegwit
gs
toast
Q17
Q3
Q4
Q6
apriori
bayesian
birch
eclat
hop
scalparc
kMeans
fuzzy
rsearch
semphy
snp
genenet
svm-rfe

Reference: [Pisharath J., Zambreno J., Ozisikyilmaz B., Choudhary A., 2006]
© Vipin Kumar
IPDPS-2011, May 18, 2011
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