Michael L. Norman Principal Investigator Interim Director, SDSC

SAN DIEGO SUPERCOMPUTER CENTER

at the UNIVERSITY OF CALIFORNIA, SAN DIEGO

Michael L. NormanPrincipal InvestigatorInterim Director, SDSC

Allan SnavelyCo-Principal InvestigatorProject Scientist



What is Gordon?

• A “data-intensive” supercomputer based on SSD flash memory and virtual shared memory• Emphasizes MEM and IO over FLOPS

• A system designed to accelerate access to massive data bases being generated in all fields of science, engineering, medicine, and social science

• The NSF’s most recent Track 2 award to the San Diego Supercomputer Center (SDSC)

• Coming Summer 2011



Why Gordon?

• Growth of digital data is exponential• “data tsunami”

• Driven by advances in digital detectors, networking, and storage technologies

• Making sense of it all is the new imperative• data analysis workflows• data mining• visual analytics• multiple-database queries• on demand data-driven

applications



The Memory Hierarchy

Flash SSD, O(TB)1000 cycles

Potential 10x speedup for random I/O to large files and databases



Gordon Architecture: “Supernode”

• 32 Appro Extreme-X compute nodes• Dual processor Intel

Sandy Bridge• 240 GFLOPS• 64 GB

• 2 Appro Extreme-X IO nodes• Intel SSD drives

• 4 TB ea.• 560,000 IOPS

• ScaleMP vSMP virtual shared memory• 2 TB RAM aggregate• 8 TB SSD aggregate

240 GFComp.Node

64 GBRAM

240 GFComp.Node

64 GBRAM

4 TB SSDI/O Node

vSMP memory virtualization



Gordon Architecture: Full Machine

• 32 supernodes = 1024 compute nodes

• Dual rail QDR Infiniband network• 3D torus (4x4x4)

• 4 PB rotating disk parallel file system• >100 GB/s

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Gordon Peak Capabilities

Speed 245 TFLOPS

Mem (RAM) 64 TB

Mem (SSD) 256 TB

Mem (RAM+SSD) 320 TB

Ratio (MEM/SPEED) 1.31 BYTES/FLOP

IO rate to SSDs 35 Million IOPS

Network bandwidth 16 GB/s bi-directional

Network latency 1 sec.

Disk storage 4 PB

Disk IO Bandwidth >100 GB/sec



Gordon is designed specifically for data-intensive HPC applications

• Such applications involve “very large data-sets or very large input-output requirements”

• Two data-intensive application classes are important and growing

Data Mining

“the process of extracting hidden patterns from data… with the amount of data doubling every three years, data mining is becoming an increasingly important tool to transform this data into information.” Wikipedia

Data-IntensivePredictive Science

solution of scientific problems via simulations that generate large amounts of data



High Performance Computing (HPC) vs High Performance Data (HPD)

Attribute HPC HPD

Key HW metric Peak FLOPS Peak IOPS

Architectural features Many small-memory multicore nodes

Fewer large-memory SMP nodes

Typical application Numerical simulation Database queryData mining

Concurrency High concurrency Low concurrency or serial

Data structures Data easily partitionede.g. grid

Data not easily partitioned e.g. graph

Typical disk I/O patterns Large block sequential Small block random

Typical usage mode Batch process Interactive



Data mining applicationswill benefit from Gordon

• De novo genome assembly from sequencer reads & analysis of galaxies from cosmological simulations and observations

• Will benefit from large shared memory

• Federations of databases and Interaction network analysis for drug discovery, social science, biology, epidemiology, etc. • Will benefit from low latency

I/O from flash



Data-intensive predictive sciencewill benefit from Gordon

• Solution of inverse problems in oceanography, atmospheric science, & seismology• Will benefit from a balanced

system, especially large RAM per core & fast I/O

• Modestly scalable codes in quantum chemistry & structural engineering• Will benefit from large

shared memory



Dash:

towards a supercomputer for data

intensive computing



Project Timeline

• Phase 1: Dash development (9/09-7/11)• Phase 2: Gordon build and acceptance (3/11-7/11)• Phase 3: Gordon operations (7/11-6/14)



Comparison of the Dash and Gordon systems

Doubling capacity halves accessibility to any random data on a given mediaSystem Component Dash Gordon

Node Characteristics (# sockets, cores, DRAM) 2 sockets, 8 cores, 48 GB 2 sockets, TBD cores, 64 GB

Compute Nodes (#) 64 1024

Processor Type Nehalem Sandy Bridge

Clock Speed (GHz) 2.4 TBD

Peak Speed (Tflops) 4.9 245

DRAM (TB) 3 64

I/O Nodes (#) 2 64

I/O Controllers per Node 2 with 8 ports 1 with 16 ports

Flash (TB) 2 256

Total Memory: DRAM + flash (TB) 5 320

vSMP Yes Yes

32-node Supernodes 2 32

Interconnect InfiniBand InfiniBand

Disk .5 PB 4.5 PB



Gordon project wins storage challenge at SC09 with Dash



We won SC09 Data Challenge with Dash!

• With these numbers:• IOR 4KB

• RAMFS 4Million+ IOPS on up to .750 TB of DRAM (1 supernode’s worth)

• 88K+ IOPS on up to 1 TB of flash (1 supernode’s worth)• Speed up Palomar Transients database searches 10x to

100x• Best IOPS per dollar

• Since that time we boosted flash IOPS to 540K (hitting our 2011 performance targets – it is now 2009



Dash Update – early vSMP test results



Dash Update – early vSMP test results



Next Steps• Continue vSMP and flash SSD assessment and

development on Dash• Prototype Gordon application profiles using Dash

• New application domains• New usage modes and operational support mechanisms• New user support requirements

• Work with TRAC to identify candidate apps• Assemble Gordon User Advisory Committee• International Data-Intensive Conference Fall 2010

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Michael L. Norman Principal Investigator Interim Director, SDSC

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