hpc trends for 2013 · 2020. 1. 14. · class 2011 2012 change growth servers 9,915 10,381 466 4...
TRANSCRIPT
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Topics for Discussion
• Forecast highlights
• Shifts in budget allocations
• Processor architectures and their implications
– Memory
– Programming models
– Efficiency
• Big Data and its potential for HPC
• What’s cloud got to do with it
Total WW HPC Revenue
Table 1: 2012 vs. 2011 Comparison – Total HPC Market Revenue
($, millions) by Product Class
Product Class 2011 2012 Change Growth
Servers 9,915 10,381 466 4.7%
Storage 3,832 4,837 1,005 26.2%
Services 3,180 3,328 147 4.6%
Software 6,456 6,651 195 3.0%
Networks 1,987 2,121 134 6.7%
Other 1,786 1,842 56 3.1%
Total 27,156 29,159 2,003 7.4% Source: Intersect360 Research, 2013
Storage revenue was
significantly affected
by flooding in
Southeast Asia in the
second half of 2011,
causing a significant
amount of storage-
related revenue to be
deferred from late
2011 and recognized
in early 2012.
WW HPC Forecast
HPC Budget Distribution by Year
• ~$29B total market implies
~$44B total budget
• Hardware declined every
year until sudden rebound
in 2012
• Facilities increased every
year until reversing in 2012
• Public cloud is a small part
of the market
From HPC User Site Census
The primary challenges for users are:
• How to plan the balance between processors per node,
cores per processor, memory per node, I/O and
interconnect on node, total nodes, etc.
• How to adapt applications for node parallelism and on-
chip (i.e., multi-core) parallelism.
• How to organize the overall job mix. Smaller nodes may
be a better fit for processing large numbers of small jobs
or large set of jobs with a broad range of requirements.
Larger nodes may work best with a job mix skewed to
larger problems.
Memory Configuration
Accelerators (Mostly NVIDIA GPUs)
Challenges of Architecture Trends
• Power consumption
• Cost of memory
• New models of parallelization
• Languages and programming models, especially
for accelerated solutions
• System efficiency
• Personnel for administration, optimization,
programming services, etc.
Technical vs. Enterprise Computing
Technical Computing
• Top-line missions:
– Find the oil
– Design the minivan
– Cure the disease
• Driven by
price/performance
• Fast adoption of new
technologies, algorithms,
and approaches
Enterprise Computing
• Keeps business running
– Communicate/collaborate
– Market and sell the product
– Accounting, HR, finance, …
• Driven by RAS: reliability,
availability, serviceability
• Slow adoption of new
technologies, algorithms,
and approaches
Where Big Data Comes From
• “Big Data” is not a specific application type, but
rather a trend – or even a collection of trends –
spanning multiple application types
• Data growing in multiple ways:
– More data (volume of data)
– More types of data (variety of data)
– Faster ingest of data (velocity of data)
– Accessibility of data (internet, instrumentation, …)
• Exceeds organizational ability to manage data or
make competitive decisions based on it
Different Types of Big Data
• “Big” in Big Data is a relative term, like “High” in
High Performance Computing, not absolute TB
or IOPS
• Different types of challenges:
– Large files
– Large numbers of files
– Many users of files (concurrent access, copies)
– Fast rate of ingest
– Long or short lifespan of data
• Implication: If data, then value.
Scaling an Enterprise Data
Infrastructure
Parallel file systems
I/O interconnects
Operating systems
Cloud
Important Insights on Big Data
1. It is much broader than Hadoop – many
different types of users and applications.
2. Money is being spent on it now – often 25% of
the annual IT budget.
3. Performance matters – even enterprise users
are buying based on performance.
Big Data trends will lead to the adoption
of HPC technologies in more areas.
Percent Rating Cloud Barrier
“Significant”
Data movement and
security are “top” barriers,
but there is a long list
• From special study on
HPC and cloud, 2011
• N = 139 – 144
Cloud + Big Data:
When Trends Collide
• Cloud is a major business computing trend. Big Data
is a major business computing trend. Therefore …
• But the barriers to Big Data in cloud are the same as
HPC in cloud (security, data movement, etc.)
• Not as simple as offloading everything to Amazon
• If cloud is a priority, invest in management software
to coordinate workloads across public and private
I like sushi. I like ice cream.
Therefore I like sushi-flavored ice cream.
Conclusions for HPC in 2013
• Users have options for breakout performance,
but they all require effort
– Users are evaluating multi-core, accelerators, cloud,
programming models, etc., but not committing yet.
– What solutions will be efficient?
– What are the software and human skills required?
• There is an opportunity to expand the use of
HPC technology
• Big Data is a bigger near-term opportunity than
“Missing Middle” for most technologies
