Download CHEP_09_Pawlowski_final - Indico

More Computing
with
Less energy
Steve Pawlowski
Intel Senior Fellow
GM, Architecture and Planning
CTO, Digital Enterprise Group
Intel Corporation
CHEP ‘09
March 24, 2009
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2
Real World Problems Taking Us
BEYOND PETASCALE
SUM
1 ZFlops
Of Top500
100 EFlops
#1
10 EFlops
1 EFlops
100 PFlops
10 PFlops
1 PFlops
100 TFlops
10 TFlops
What we can just model
today with <100TF
1 TFlops
100 GFlops
10 GFlops
1 GFlops
100 MFlops
1993
3
Aerodynamic Analysis:
1 Petaflops
Example
Real
World
Challenges:
Laser Optics:
10 Petaflops
• Full modeling
of aninaircraft
in all conditions
Molecular
Dynamics
Biology:
20 Petaflops
• Green airplanes
Aerodynamic
Design:
1 Exaflops
• Genetically tailored
medicine
Computational
Cosmology:
10 Exaflops
•Turbulence
Understand
the
origin
of
the
universe
in Physics:
100 Exaflops
• Synthetic fuels
everywhere
Computational
Chemistry:
1 Zettaflops
• Accurate extreme weather prediction
Source: Dr. Steve Chen, “The Growing HPC Momentum in China”,
June 30th, 2006, Dresden, Germany
1999
2005
2011
2017
2023
2029
A look at CERN’s Computing Growth
Source: CERN,
Jarp Sverre
120
Tape Space
(PetaByte)
100
CERN Tape Library
Computing
80
60
21,500 Cores @
1400 SI2K per core
Disk Space
(PetaByte)
40
20
0
2007
4
2008
2009
2010
2011
2012
2013
Lots of computing (45% CAGR), lots of data; no upper boundary!
Relative Tr Performance
Moore’s Law and High
Performance Computing
1000
Exa
Peta
100
Relative Performance
(GFlops as the base)
Peta: Today’s COTS 11.5K
Processors assuming 2.7 GHz
1.E+08
Tera
500X
1.E+06
10
30X
G
250X
Exa
Peta
Tera: ASCI Red
9,298 Processors
1.E+04
2.5M X
4,000X
Tera
1
1986
1996
2006
2016
36X
1.E+02
G
Source: Intel labs
Transistor Performance
1.E+00
1986
1996
2006
2016
From Peta to Exa, 2X Transistor Performance, Requiring ~30K cores @2800 SPI2K
5
A look at CERN’s Computing Growth
120
30,000 Cores @ 2800
SI2K per core
Tape Space (PetaByte)
100
80
Computing
60
21,500 Cores @
1400 SI2K per core
40
Disk Space (PetaByte)
20
Source: CERN, Jarp Sverre
0
2007
6
2008
2009
2010
2011
2012
2013
2014
2015 2016
7
Reach Exascale by 2018
From GigFlops to ExaFlops
~2018
2008
~1997
~1987
Note: Numbers are based on Linpack Benchmark.
Dates are approximate.
“The pursuit of each milestone has led to important
breakthroughs in science and engineering.”
Source: IDC “In Pursuit of Petascale Computing: Initiatives Around the World,” 2007
8
What is Preventing us?
Power is Gating Every Part of Computing
An ExaFLOPS Machine without Power Management
Power Consumption
1000,000
EFLOP
2015-18
Power?
Other misc. power
consumptions:
…
Power supply losses
Cooling
… etc
?
Power (KW)
100,000
100+ MW?
Voltage is not scaling as in the past
10000
PFLOP
Disk
10MW
10EB disk
@ 10TB/disk @10W
Comm
70MW
100pJ comm per FLOP
Memory
80MW
0.1B/FLOP
@ 1.5nJ per Byte
Compute
70MW
170K chips
@ ~400W each
1000
TFLOP
GFLOP
MFLOP
100
1964
1985
1997
2008
2018
The Challenge of Exascale
Source: Intel, for illustration and assumptions, not product representative
9
HPC Platform Power
3%
5% 2% 1%
CPUs
31%
11%
CPU
Planar & VRs
Memory
PSUs
Memory
26%
Fans
Planar
&VRs
HDD
22%
Data from P3 Jet Power Calculator, V2.0
DP 80W Nehalem
Memory – 48GB (12 x 4GB DIMMs) Single Power Supply Unit @ 230Vac
PCI+GFX
Peripherals
Need a platform view of power consumption: CPU, Memory and VR, etc.
10
Exponential Power and Computing Growth
1
Relative Energy/Op
Power at a glance:
5V
(assume 31% CPU Power in a system)
•Today’s Peta: 0.7- 2 nj/op
G
Vcc scaling
•Today’s COTS: 2nj/op
0.1
(assume 100W/50GFlops)
Tera
•Unmanaged Exa: if 1GW, 0.31nj/op;
Peta
0.01
Exa
Exa
0.001
1986
1.E+08
1996
2006
Relative Performance
2016
Peta
1.E+06
1.E+04
Relative Power
Tera
Relative Performance and Power
(GFlops as the base)
1M X
1.E+02
G
4,000X
80X
1.E+00
1986
1996
2006
Unmanaged growth in power will reach Giga Watt level at Exascale
11
2016
To Reach ExaFlops
Flops
1.E+15Peta
1.E+14
1.E+13
1.E+12 Tera
Intel® Core™ uArch
1.E+11
Pentium® 4 Architecture
1.E+10
1.E+09 Giga
Pentium® II Architecture
Pentium® Architecture
1.E+08
1.E+07
Future Projection
Pentium® III Architecture
386
486
1.E+06
1985
Source: Intel
1990
1995
2000
2005
2010
2015
Power goal = 200W / Socket, to reach Linpack ExaFlops:
• 5 pJ / op / socket * 40 TFlops - 25K sockets peak or 33K sustained, or
•10 pJ / op / socket * 20 TFlops - 50K sockets peak (conservative)
12
Intel estimates of future trends. Intel estimates are based in part on historical capability of Intel products and projections
for capability improvement. Actual capability of Intel products will vary based on actual product configurations.
2020
Parallelism for Energy Efficient
Performance
10000000
Many Core
Relative Performance
1000000
100000
Multi Threaded
Future Projection
10000
Speculative, OOO
1000
Super Scalar
100
486
386
10
286
8086
1
0.1
0.01
1970
13
Multi-Core
Era of
Pipelined
Architecture
1980
Era of
Instruction
Level
Parallelism
1990
2000
Era of Thread
& Processor
Level
Parallelism
2010
Intel estimates of future trends. Intel estimates are based in part on historical capability of Intel products and projections
for capability improvement. Actual capability of Intel products will vary based on actual product configurations.
2020
Reduce Memory and
Communication Power
Chip to memory
~1.5nJ per Byte
~300pJ per Byte
Core-to-core
~10pJ per Byte
Chip to chip
~100pJ per Byte
Data movement is expensive
14
15
16
17
Solid State Drive
Future Performance and Energy Efficiency
SSD GigaBytes
Assume: Capacity of the SSD grows at a
CAGR of about 1.5; historical HDD at 1.6
5000
Vision
10 ExaBytes at 2018:
100
• 2 Million SSD’s vs. ½ Million
HDD
50
Future projection
0
2008
2010
2012
2014
2016
Source: Intel, calculations based on today’s vision
2018
• If @2.5w each, total 5MW
• If HDD (300 IOPS) and SSD (10k
IOPS) constant: SSD has 140X
IOPS
Innovations to improve IO: 2X less power with 140x performance gain
18
Reliability, Reliability and Reliability
Density is on the Rise
Reliability is an Issue
Simplify for Reliability
•Solid State Drives or
Diskless nodes
•Moore’s Law provides
more transistors
•Many core provides more
computing
•HPC requires super high
socket count
large numbers
•Silent Data Corruption
(SDC)
•Detectable
Uncorrectable Errors
(DUE)
Computing Errors
•Fewer cables by using
backplanes
•Simpler node design
(fewer Voltage
Regulator Modules,
fewer capacitors, …)
Simplification
Mean Time Between Failure (MTBF) Trends down:
(Probably) (large number) = Probably NOT
19
Increase Data Center Compute Density
Compute Density
Silicon
Process
20
New
+ Technology
Small
Power
+ Form Factor + Management
Data Center
+ Innovation
Target 50% yearly
improvements in
performance/watt
Year
Source: Intel, based on Intel YoY improvement
with SpecPower Benchmark
Revised Exascale System Power
ExaFLOPS Machine without Power Mgmt
Other misc. power
consumption:
…
Power supply losses
Cooling
… etc
100+ MW?
ExaFLOPS Machine Future Vision
Other misc. power
consumption:
…
Power supply losses
Cooling
… etc
<<100MW
Disk
10MW
10EB disk
@ 10TB/disk @10W
SSD
5MW
Comm
70MW
100pJ com per FLOP
Comm
7MW
Memory
Compute
80MW
0.1B/FLOP
@ 1.5nJ per Byte
70MW
170K chips
@ ~400W each
Memory
16MW
Compute
8-16MW
10EB SSD
@ 5TB/SSD @2.5W
10pJ comm per FLOP
0.1B/FLOP
@ 300pJ per Byte
25K - 80K chips
@~200W each
Source: Intel, for illustration and assumptions, not product representative
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