SAN DIEGO SUPERCOMPUTER CENTER at the UNIVERSITY OF CALIFORNIA; SAN DIEGO Gateways to Discovery: Cyberinfrastructure for the Long Tail of Science XSEDE’14 (16 July 2014) R. L. Moore, C. Baru, D. Baxter, G. Fox (Indiana U), A Majumdar, P Papadopoulos, W Pfeiffer, R. S. Sinkovits, S. Strande (NCAR), M. Tatineni, R. P. Wagner, N. Wilkins-Diehr, M. L. Norman UCSD/SDSC (except as noted)
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SAN DIEGO SUPERCOMPUTER CENTER at the UNIVERSITY OF CALIFORNIA; SAN DIEGO Gateways to Discovery: Cyberinfrastructure for the Long Tail of Science XSEDE’14.
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SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
Gateways to Discovery:Cyberinfrastructure for the Long Tail of Science
XSEDE’14 (16 July 2014)
R. L. Moore, C. Baru, D. Baxter, G. Fox (Indiana U), A Majumdar, P Papadopoulos, W Pfeiffer, R. S. Sinkovits, S. Strande (NCAR), M.
Tatineni, R. P. Wagner, N. Wilkins-Diehr, M. L. NormanUCSD/SDSC (except as noted)
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
HPC for the 99%
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
Comet is in response to NSF’s solicitation (13-528) to
• “… expand the use of high end resources to a much larger and more diverse community
• … support the entire spectrum of NSF communities
• ... promote a more comprehensive and balanced portfolio
• … include research communities that are not users of traditional HPC systems.“
The long tail of science needs HPC
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
Jobs and SUs at various scales across NSF resources
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Job Size (Cores)
Percentage of Jobs (Left Axis)SUs Charged (Right Axis)
One node
• 99% of jobs run on NSF’s HPC resources in 2012 used <2048 cores
• And consumed ~50% of the total core-hours across NSF resources
Job Size (Cores)
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SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
Comet Will Serve the 99%
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
Comet: System Characteristics • Available January 2015 • Total flops ~1.8-2.0 PF• Dell primary integrator
• Intel next-gen processors, former codename Haswell, with AVX2
Based on our experiences with Gordon, a number of applications will benefits from continued access to flash• Applications that generate large numbers of temp files
• Computational finance – analysis of multiple markets (NASDAQ, etc.)
• Text analytics – word correlations in Google Ngram data
• Computational chemistry codes that write one- and two-electron integral files to scratch
• Structural mechanics codes (e.g. Abaqus), which generate stiffness matrices that don’t fit into memory
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
Large memory nodes
While most user applications will run well on the standard compute nodes, a few domains will benefit from the large memory (1.5 TB nodes)• De novo genome assembly: ALLPATHS-LG,
SOAPdenovo, Velvet• Finite-element calculations: Abaqus• Visualization of large data sets
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
GPU nodes
Comet’s GPU nodes will serve a number of domains • Molecular dynamics applications have been one of the
biggest GPU success stories. Packages include Amber, CHARMM, Gromacs and NAMD
• Applications that depend heavily on linear algebra• Image and signal processing
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
Key Comet Strategies
• Target modest-scale users and new users/communities: goal of 10,000 users/year!
• Support capacity computing, with a system optimized for small/modest-scale jobs and quicker resource response using allocation/scheduling policies
• Build upon and expand efforts with Science Gateways, encouraging gateway usage and hosting via software and operating policies
• Provide a virtualized environment to support development of customized software stacks, virtual environments, and project control of workspaces
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
Comet will serve a large number of users, including new communities/disciplines
• Allocations/scheduling policies to optimize for high throughput of many modest-scale jobs (leveraging Trestles experience)• Optimized for rack-level jobs but cross-rack jobs feasible
• Optimized for throughput (ala Trestles)
• Per-project allocations caps to ensure large numbers of users
• Rapid access for start-ups with one-day account generation
• Limits on job sizes, with possibility of exceptions
• Gateway-friendly environment: Science gateways reach large communities w/ easy user access • e.g. CIPRES gateway alone currently accounts for ~25% of all users of NSF
resources, with 3,000 new users/year and ~5,000 users/year
• Virtualization provides low barriers to entry (see later charts)
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
Changing the face of XSEDE HPC users• System design and policies
• Allocations, scheduling and security policies which favor gateways
• Support gateway middleware and gateway hosting machines
• Customized environments with high-performance virtualization
• Flexible allocations for bursty usage patterns
• Shared node runs for small jobs, user-settable reservations
• Time dependent (noisy)OSU Microbenchmarks (3.9, osu_latency)
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
10x more bandwidth than Amazon EC2
19
• SR-IOV
• < 2% bandwidth loss over entire range
• > 95% peak bandwidth
• Amazon EC2
• < 35% peak bandwidth
• 900% to 2500% worse bandwidth than virtualized InfiniBand
OSU Microbenchmarks (3.9, osu_bw)
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
Weather Modeling – 15% Overhead
• 96-core (6-node) calculation
• Nearest-neighbor communication
• Scalable algorithms• SR-IOV incurs modest
(15%) performance hit• ...but still still 20%
faster*** than AmazonWRF 3.4.1 – 3hr forecast
*** 20% faster despite SR-IOV cluster having 20% slower CPUs
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
Quantum ESPRESSO: 5x Faster than EC2
• 48-core (3 node) calculation
• CG matrix inversion (irregular comm.)
• 3D FFT matrix transposes (All-to-all communication)
• 28% slower w/ SR-IOV• SR-IOV still > 500%
faster*** than EC2 Quantum Espresso 5.0.2 – DEISA AUSURF112 benchmark*** 20% faster despite SR-IOV cluster having 20% slower CPUs
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
SR-IOV is a huge step forward in high-performance virtualization
• Shows substantial improvement in latency over Amazon EC2, and it provides nearly zero bandwidth overhead
• Benchmark application performance confirms significant improvement over EC2
• SR-IOV lowers performance barrier to virtualizing the interconnect and makes fully virtualized HPC clusters viable
• Comet will deliver virtualized HPC to new/non-traditional communities that need flexibility without major loss of performance
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
BACKUP
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
NSF 13-528: Competitive proposals should address:• “Complement existing XD capabilities with new types of computational
resources attuned to less traditional computational science communities;
• Incorporate innovative and reliable services within the HPC environment to deal with complex and dynamic workflows that contribute significantly to the advancement of science and are difficult to achieve within XD;
• Facilitate transition from local to national environments via the use of virtual machines;
• Introduce highly useable and cost efficient cloud computing capabilities into XD to meet national scale requirements for new modes of computationally intensive scientific research;
• Expand the range of data intensive and/or computationally-challenging science and engineering applications that can be tackled with current XD resources;
• Provide reliable approaches to scientific communities needing a high-throughput capability.”
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
VCs on Comet: Operational Details- one VM per physical node -
Physical node(XSEDE stack)
Virtual machine(User stack)
HN
Virtual clusterhead node
HN HN
HN
HN
VC0 VC1
VC2
VC3
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
VCs on Comet: Operational Details- Head Node remains active after VC shutdown -
Physical node(XSEDE stack)
Virtual machine(User stack)
HN
Virtual clusterhead node
HN HN
HN
HN
VC0 VC1
VC2
VC3
SAN DIEGO SUPERCOMPUTER CENTER
at the UNIVERSITY OF CALIFORNIA; SAN DIEGO
VCs on Comet: Spinup/shutdown- Each VC has its own ZFS file system for storing VMIs –