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)

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 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) Cumulative Usage

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 ~ PF Dell primary integrator Intel next-gen processors, former codename Haswell, with AVX2 Aeon storage vendor Mellanox FDR InfiniBand Standard compute nodes Dual Haswell processors 128 GB DDR4 DRAM (64 GB/socket!) 320 GB SSD (local scratch) GPU nodes Four NVIDIA GPUs/node Large-memory nodes (Mar 2015) 1.5 TB DRAM Four Haswell processors/node Hybrid fat-tree topology FDR (56 Gbps) InfiniBand Rack-level (72 nodes) full bisection bandwidth 4:1 oversubscription cross-rack Performance Storage 7 PB, 200 GB/s Scratch & Persistent Storage Durable Storage (reliability) 6 PB, 100 GB/s Gateway hosting nodes and VM image repository 100 Gbps external connectivity to Internet2 & ESNet

SAN DIEGO SUPERCOMPUTER CENTER at the UNIVERSITY OF CALIFORNIA; SAN DIEGO Comet Architecture Juniper 100 Gbps Arista 40GbE (2x) Data Mover (4x) R&E Network Access Data Movers Internet 2 7x 36-port FDR in each rack wired as full fat-tree. 4:1 over subscription between racks. 72 HSWL 320 GB IB Core (2x) N GPU 4 Large- Memory Bridge (4x) Performance Storage 7 PB, 200 GB/s Durable Storage 6 PB, 100 GB/s Arista 40GbE (2x) N racks FDR 36p HSWL 320 GB 72 HSWL Mid-tier Additional Support Components (not shown for clarity) NFS Servers, Virtual Image Repository, Gateway/Portal Hosting Nodes, Login Nodes, Ethernet Management Network, Rocks Management Nodes Node-Local Storage FDR 40GbE 10GbE

SAN DIEGO SUPERCOMPUTER CENTER at the UNIVERSITY OF CALIFORNIA; SAN DIEGO SSDs – building on Gordon success 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 Third party apps Leverage and augment investments elsewhere FutureGrid experience, image packaging, training, on-ramp XSEDE (ECSS NIP & Gateways, TEOS, Campus Champions) Build off established successes supporting new communities Example-based documentation in Comet focus areas Unique HPC University contributions to enable community growth

SAN DIEGO SUPERCOMPUTER CENTER at the UNIVERSITY OF CALIFORNIA; SAN DIEGO Virtualization Environment Leveraging expertise of Indiana U/ FutureGrid team VM jobs scheduled just like batch jobs (not conventional cloud environment with immediate elastic access) VMs will be easy on-ramp for new users/communities, including low porting time Flexible software environments for new communities and apps VM repository/library Virtual HPC cluster (multi-node) with near-native IB latency and minimal overhead (SRIOV)

SAN DIEGO SUPERCOMPUTER CENTER at the UNIVERSITY OF CALIFORNIA; SAN DIEGO Single Root I/O Virtualization in HPC Problem: complex workflows demand increasing flexibility from HPC platforms Pro: Virtualization  flexibility Con: Virtualization  IO performance loss (e.g., excessive DMA interrupts) Solution: SR-IOV and Mellanox ConnectX-3 InfiniBand HCAs One physical function (PF)  multiple virtual functions (VF), each with own DMA streams, memory space, interrupts Allows DMA to bypass hypervisor to VMs

SAN DIEGO SUPERCOMPUTER CENTER at the UNIVERSITY OF CALIFORNIA; SAN DIEGO High-Performance Virtualization on Comet Mellanox FDR InfiniBand HCAs with SR-IOV Rocks and OpenStack Nova to manage VMs Flexibility to support complex science gateways and web-based workflow engines Custom compute appliances and virtual clusters developed with FutureGrid and their existing expertise Backed by virtualized Lustre running over virtualized InfiniBand

SAN DIEGO SUPERCOMPUTER CENTER at the UNIVERSITY OF CALIFORNIA; SAN DIEGO Benchmark comparisons of SR-IOV Cluster v AWS (early 2013): Hardware/Software Configuration Native, SR-IOVAmazon EC2 PlatformRocks 6.1 (EL6) Virtualization via kvm Amazon Linux (EL6) cc2.8xlarge Instances CPUs2x Xeon E (2.2GHz) 16 cores per node 2x Xeon E (2.6GHz) 16 cores per node RAM64 GB DDR3 DRAM60.5 DDR3 DRAM InterconnectQDR4X InfiniBand Mellanox ConnectX-3 (MT27500) Intel VT-d, SR-IOV enabled in firmware, kernel, drivers mlx4_core 1.1 Mellanox OFED 2.0 HCA firmware GbE common placement group

SAN DIEGO SUPERCOMPUTER CENTER at the UNIVERSITY OF CALIFORNIA; SAN DIEGO 50x less latency than Amazon EC2 18 SR-IOV < 30% overhead for Messages < 128 bytes < 10% overhead for eager send/recv Overhead  0% for bandwidth-limited regime Amazon EC2 > 5000% worse latency 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 Amazon WRF – 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 – 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 : 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 cluster head node 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 cluster head node 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 – - latency hiding tricks on startup - Physical node (XSEDE stack) Virtual machine (User stack) HN Virtual cluster head node HN VC0 VC1 VC2 VC3 ZFS pool Virtual machine disk image