The Effect of In-Network Computing-Capable Interconnects on the Scalability of CAE Simulations
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1 The Effect of In-Network Computing-Capable Interconnects on the Scalability of CAE Simulations Ophir Maor HPC Advisory Council
2 The HPC-AI Advisory Council World-wide HPC non-profit organization More than 400 member companies / universities / organizations Bridges the gap between HPC-AI usage and its potential Provides best practices and a support/development center Explores future technologies and future developments Leading edge solutions and technology demonstrations
3 HPC Advisory Council Members
4 HPC-AI Advisory Council Cluster Center (Examples) Supermicro / Foxconn 32-node cluster Dual Socket Intel Gold 6138 Skylake 2GHz Dell PowerEdge R730/R node cluster Dual Socket Intel E5-2697A V4 Broadwell 2.60 GHz IBM S822LC POWER8 8-node cluster Dual Socket IBM POWER8 10-core 2.86 GHz GPU: NVIDIA Kepler K80 GPUs 4
5 Multiple Applications Best Practices Published Abaqus CPMD Lattice QCD OpenFOAM ABySS Dacapo LAMMPS OpenMX AcuSolve Desmond LS-DYNA OptiStruct Amber DL-POLY minife PARATEC AMG AMR ANSYS CFX ANSYS FLUENT ANSYS Mechanical BQCD BSMBench CAM-SE CCSM CESM COSMO CP2K Eclipse FLOW-3D GADGET-2 Graph500 GROMACS Himeno HIT3D HOOMD-blue HPCC HPCG HYCOM ICON MILC MSC Nastran MR Bayes App MM5 MPQC NAMD Nekbone NEMO App NWChem Octopus OpenAtom PFA PFLOTRAN Quantum ESPRESSO RADIOSS SNAP SPECFEM3D STAR-CCM+ STAR-CD VASP VSP WRF App App
6 Exponential Data Growth Everywhere Cloud and Web 2.0 HPC Big Data Security Internet of Things Enterprise Storage Machine Learning Business Intelligence source: IDC
7 Breaking the Application Latency Wall Network 10 years ago Communication Framework Network Today Communication Framework Network Future Communication Framework ~10 microsecond ~100 microsecond ~0.1 microsecond ~10 microsecond ~0.05 microsecond ~1 microsecond 7
8 The Ever Growing Demand for Higher Performance Terascale Petascale Exascale 1 st Roadrunner SMP to Clusters Single-Core to Many-Core APP HW SW Co-Design Application Software Hardware
9 Need for Intelligent Faster Interconnect CPU-Centric (Onload) Data-Centric (Offload) Must Wait for the Data Creates Performance Bottlenecks Analyze Data as it Moves! Faster Data Speeds and In-Network Computing Enable Higher Performance and Scale 9
10 SHARP - Scalable Aggregation and Reduction Technology Reliable Scalable General Purpose Primitive In-network Tree based aggregation mechanism Large number of groups Multiple simultaneous outstanding operations Applicable to Multiple Use-cases HPC Applications using MPI / SHMEM Distributed Machine Learning applications Scalable High Performance Collective Offload Barrier, Reduce, All-Reduce, Broadcast and more Sum, Min, Max, Min-loc, max-loc, OR, XOR, AND Integer and Floating-Point, 16/32/64 bits 10
11 SHARP Allreduce Performance 11
12 SHARP Allreduce Performance 12
13 The Niagara Supercomputer University of Toronto
14 SHARP AllReduce Performance 1500 Nodes, 60K MPI Ranks, Dragonfly+ Topology
15 ANSYS Fluent ANSYS Fluent software is a well known CFD tool available Fluent includes well-validated physical modeling capabilities to deliver fast, accurate results across the widest range of CFD and multiphysics applications.
16 Test Setup Dell PowerEdge R730 servers Dual socket Intel(R) E5-2697A V4 CPUs at 2.60GHz Mellanox ConnectX-5 EDR 100Gb/s InfiniBand adapters Memory of 256GB DDR4 2400MHz RDIMMs per node 1TB 7.2K RPM SSD 2.5" hard drive per node. Mellanox Switch-IB 2 SB Port 100Gb/s EDR InfiniBand ANSYS Fluent v19 Red Hat Enterprise Linux 7.4 Fluent benchmarks: ice_2m, oil_rig_7m 16
17 ANSYS Fluent - MPI Benchmark (InfiniBand EDR) 35% Not Scaling
18 ANSYS Fluent - MPI Benchmark (InfiniBand EDR) 41%
19 ANSYS Fluent Adapter Comparison 41% 29% 19
20 Data Centric Data Center 20
21 Summary HPC environments impose high performance demands The Co-Design collaboration enables the development of In-Network Computing that breaks the performance and scalability barriers The ANSYS Fluent application was benchmarked for this study to demonstrate the advantages of In-Network Computing technology We have witness nearly 41% performance advantage and linear scalability with InfiniBand In-Network Computing technology
22 2018 HPC-AI Advisory Council Activities HPC-AI Advisory Council More then 400 members Application best practices, case studies Benchmarking center with remote access for users 2018 Conferences Australia (Pawsey Supercomputing Centre) - August Spain (Barcelona Supercomputing Center) - September China (HPC China) October 2018 Competitions HPC-AI APAC Competition: March - August 6th Annual RDMA Competition (China): May - October 7th Annual Student Cluster Competition (ISC, Germany): June For more information info@hpcadvisorycouncil.com
23 Thank You! Ophir Maor Application Performance HPC Advisory Council 23
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