25th Year Panel

1
25th Year Panel WHATS AHEAD

• Vipin Kumar
• University of Minnesota
• kumar_at_cs.umn.edu http//www.cs.umn.edu/kumar

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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 ?

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Compute centric to Data Centric
Transition
Compute Intensive
Data Intensive
4
Compute centric to Data Centric
Transition
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
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Compute centric to Data Centric
Transition
Compute Intensive
Data Intensive
6
Great Challenges Facing the Society
Predicting the impact of climate change
Improving health care and reducing costs
Finding alternative/ green energy sources
Reducing hunger and poverty by increasing
agriculture production
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Scalable Data Analysis
Example Understanding Climate Change

• General Circulation Models Mathematical models
  with physical equations based on fluid dynamics

• Parameterization and non-linearity of
  differential equations are sources for
  uncertainty!

Detection of Global Dipole Structure
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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,

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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 ?

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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 ?

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Do We Need Benchmarks Specific to Data Intensive
Computing ?

• Performance metrics of several benchmarks
  gathered from Vtune
• Cache miss ratios, Bus usage, Page faults etc.
• Benchmark applications were grouped using Kohenen
  clustering to spot trends