GRID Testing and Profiling. November 2017

Size: px
Start display at page:

Download "GRID Testing and Profiling. November 2017"

Transcription

1 GRID Testing and Profiling November 2017

2 2 GRID C++ library for Lattice Quantum Chromodynamics (Lattice QCD) calculations Developed by Peter Boyle (U. of Edinburgh) et al. Hybrid MPI+OpenMP plus NUMA aware socket optimizations When building GRID, a set of test and benchmark programs also gets built One benchmark program in particular, Benchmark_ITT, exercises the library in various ways and prints several performance measures For more information Grid: A next generation data parallel C++ QCD library: Grid: data parallel library for QCD: 05

3 Test Cluster Configuration Thor cluster Dell PowerEdge R730/R node cluster Dual Socket Intel Xeon 16-core CPUs E5-2697A 2.60 GHz Mellanox ConnectX-5 EDR 100Gb/s InfiniBand adapters Mellanox Switch-IB 2 SB Port 100Gb/s EDR InfiniBand switches Memory: 256GB DDR4 2400MHz RDIMMs per node 1TB 7.2K RPM SATA 2.5" hard drives per node Helios cluster Supermicro SYS-6029U-TR4 16-node cluster Dual Socket Intel Xeon Gold GHz Mellanox ConnectX-5 EDR 100Gb/s InfiniBand/VPI adapters Mellanox Switch-IB 2 SB Port 100Gb/s EDR InfiniBand switches Memory: 192GB DDR4 2677MHz RDIMMs per node 1TB 7.2K RPM SSD 2.5" hard drive per node 3

4 4 Getting GRID GRID is available from GitHub: Two packages are needed: GMP ( and MPFR ( For the DiRAC ITT benchmark, a slightly different tarball was provided wget tar zxvf dirac-itt-fix1.tar.gz cd Grid-dirac-ITT-fix1./bootstrap.sh

5 5 Configuring GRID After loading the appropriate software modules (Intel or compilers and HPC-X 2.0 for the example below and the results shown later on): mkdir build_hpcx cd build_hpcx../configure --enable-precision=single --enable-simd=avx2 \ --enable-comms=mpi3-auto --enable-mkl CXX=mpicxx \ --prefix=/<path-to-installation-directory>/grid_hpcx20 The DiRAC benchmark instructions called for running the program in single precision, but we also ran it in double precision (as suggested in the README file from the GitHub distribution) because that is what was initially used to guide the build AVX512 and KNL are among the other possible options for --enable-simd

6 6 Building and Testing GRID Building and testing is straight forward: make 2>&1 tee make_i17hpcx20.log make check 2>&1 tee check_i17hpcx20.log make install 2>&1 tee install_i17hpcx20.log NOTE: It may be necessary to add -lrt to the end of the line starting with LIBS = in each of sixteen Makefile files ({,*/,*/*/}Makefile) NOTE: If the build server or node has processors that don t support the SIMD option configured, the make check step will fail. Nonetheless, the executables will have been produced

7 7 Running Benchmark_ITT Requires several arguments: --mpi M.N.P.Q, where M N P Q and M*N*P*Q is the total number of ranks; each of M, N, P and Q must be a power of two --shm size, where size is the size (in kb) of shared memory segments that will be allocated (1024 for 4 and fewer nodes, 2048 for 8 or more worked fine) --threads nthreads, where nthreads is the number of OpenMP threads --shm-hugetlb (for Xeon Phi runs)

8 Launch Command Generic launch command for HPC-X /usr/bin/time -p mpirun -np $ranks --map-by ppr:$rpn:node \ --bind-to $object -report-bindings --display-map \ -mca coll_hcoll_enable 0 -mca mtl ^mxm \ -x UCX_RC_VERBS_TM_ENABLE=y -mca pml ucx \ -x UCX_TLS=$transport,self,shm \ /path-to-installation-directory/bin/benchmark_itt \ --mpi M.N.P.Q --shm threads $OMP_NUM_THREADS $ranks is the total number of MPI ranks (= M*N*P*Q) $rpn is the number of MPI ranks per node $object is none if $rpn is 1 and socket otherwise $transport is rc or rc_x 8

9 Notes HPC-X 2.0 / UCX It introduced two new accelerated transports, dc_x and rc_x, that use enhanced verbs The best transports for GRID turned out to be rc_x and rc In the mpirun example, we enabled hardware tag matching (UCX_RC_VERBS_TM_ENABLE)that perform the tag matching lookup in hardware instead of software Selection of a pml (point-to-point management layer) The UCX pml and the MXM pml are mutually exclusive; thus, MXM was disabled Single vs. Double Precision Single Precision numbers uses 32 bits while Double Precision numbers uses 64 bits The double precision calculations generates more data transfers for the same test size The differences in the results presented later in the presentation emphasis the need for fast accelerated interconnect for best performance 9

10 MPI Profiling (IPM 2.06) 10

11 MPI Profiling (IPM 2.06) 11

12 12 Performance Measures At the end of the run, Benchmark_ITT writes a Memory benchmark results table, a Communications benchmark table (for runs on two or more nodes), and an overall floating point performance report Memory benchmark example: Memory benchmark = Benchmarking a*x + y bandwidth L bytes GB/s Gflop/s seconds GB/s / node

13 13 Performance Measures (cont.) Communications benchmark example: Communications benchmark ================== = Benchmarking threaded STENCIL halo exchange in 4 dimensions ================== L Ls bytes MB/s uni (err/min/max) MB/s bidi (err/min/max) Per Node Summary table Ls=16 L Wilson DWF4 DWF

14 14 Performance Measures Overall floating point performance report sample: Comparison point result: Mflop/s per node Comparison point is 0.5*( ) Comparison point robustness: Note that the Comparison point result is given in Mflop/s per node. For the performance graphs that follow, that value was multiplied by the number of nodes and converted to Gflop/s to obtain total Gflop/s

15 15 GRID Double Precision Performance: Interconnects CPU: Xeon E5-2697A v4 (code name Broadwell ) 48% Higher is Better

16 16 GRID Double Precision Performance: Interconnects CPU: Xeon Gold 6138 (code name Skylake ) 27% Higher is Better

17 17 GRID Double Precision Performance: Larger Scale Intel Xeon E5-2697A v4 (code name Broadwell) Interconnect: InfiniBand Higher is Better

18 18 Grid Single Precision Performance: Larger Scale CPU: Xeon Gold 6148 (code name Skylake ) Interconnect: InfiniBand Higher is Better

19 19 References GRID =slides&confid=1805 HPC Advisory Council write-up X+2.0+Boosts+Performance+of+Grid+Benchmark

20 Thank You All trademarks are property of their respective owners. All information is provided As-Is without any kind of warranty. The HPC Advisory Council makes no representation to the accuracy and completeness of the information contained herein. HPC Advisory Council undertakes no duty and assumes no obligation to update or correct any information presented herein

LS-DYNA Performance Benchmark and Profiling. October 2017

LS-DYNA Performance Benchmark and Profiling. October 2017 LS-DYNA Performance Benchmark and Profiling October 2017 2 Note The following research was performed under the HPC Advisory Council activities Participating vendors: LSTC, Huawei, Mellanox Compute resource

More information

LS-DYNA Performance Benchmark and Profiling. April 2015

LS-DYNA Performance Benchmark and Profiling. April 2015 LS-DYNA Performance Benchmark and Profiling April 2015 2 Note The following research was performed under the HPC Advisory Council activities Participating vendors: Intel, Dell, Mellanox Compute resource

More information

LS-DYNA Performance Benchmark and Profiling. October 2017

LS-DYNA Performance Benchmark and Profiling. October 2017 LS-DYNA Performance Benchmark and Profiling October 2017 2 Note The following research was performed under the HPC Advisory Council activities Participating vendors: LSTC, Huawei, Mellanox Compute resource

More information

OpenFOAM Performance Testing and Profiling. October 2017

OpenFOAM Performance Testing and Profiling. October 2017 OpenFOAM Performance Testing and Profiling October 2017 Note The following research was performed under the HPC Advisory Council activities Participating vendors: Huawei, Mellanox Compute resource - HPC

More information

MILC Performance Benchmark and Profiling. April 2013

MILC Performance Benchmark and Profiling. April 2013 MILC Performance Benchmark and Profiling April 2013 Note The following research was performed under the HPC Advisory Council activities Special thanks for: HP, Mellanox For more information on the supporting

More information

GROMACS (GPU) Performance Benchmark and Profiling. February 2016

GROMACS (GPU) Performance Benchmark and Profiling. February 2016 GROMACS (GPU) Performance Benchmark and Profiling February 2016 2 Note The following research was performed under the HPC Advisory Council activities Participating vendors: Dell, Mellanox, NVIDIA Compute

More information

LAMMPS-KOKKOS Performance Benchmark and Profiling. September 2015

LAMMPS-KOKKOS Performance Benchmark and Profiling. September 2015 LAMMPS-KOKKOS Performance Benchmark and Profiling September 2015 2 Note The following research was performed under the HPC Advisory Council activities Participating vendors: Intel, Dell, Mellanox, NVIDIA

More information

Altair OptiStruct 13.0 Performance Benchmark and Profiling. May 2015

Altair OptiStruct 13.0 Performance Benchmark and Profiling. May 2015 Altair OptiStruct 13.0 Performance Benchmark and Profiling May 2015 Note The following research was performed under the HPC Advisory Council activities Participating vendors: Intel, Dell, Mellanox Compute

More information

MM5 Modeling System Performance Research and Profiling. March 2009

MM5 Modeling System Performance Research and Profiling. March 2009 MM5 Modeling System Performance Research and Profiling March 2009 Note The following research was performed under the HPC Advisory Council activities AMD, Dell, Mellanox HPC Advisory Council Cluster Center

More information

SNAP Performance Benchmark and Profiling. April 2014

SNAP Performance Benchmark and Profiling. April 2014 SNAP Performance Benchmark and Profiling April 2014 Note The following research was performed under the HPC Advisory Council activities Participating vendors: HP, Mellanox For more information on the supporting

More information

AcuSolve Performance Benchmark and Profiling. October 2011

AcuSolve Performance Benchmark and Profiling. October 2011 AcuSolve Performance Benchmark and Profiling October 2011 Note The following research was performed under the HPC Advisory Council activities Participating vendors: Intel, Dell, Mellanox, Altair Compute

More information

NAMD Performance Benchmark and Profiling. January 2015

NAMD Performance Benchmark and Profiling. January 2015 NAMD Performance Benchmark and Profiling January 2015 2 Note The following research was performed under the HPC Advisory Council activities Participating vendors: Intel, Dell, Mellanox Compute resource

More information

CPMD Performance Benchmark and Profiling. February 2014

CPMD Performance Benchmark and Profiling. February 2014 CPMD Performance Benchmark and Profiling February 2014 Note The following research was performed under the HPC Advisory Council activities Special thanks for: HP, Mellanox For more information on the supporting

More information

Altair RADIOSS Performance Benchmark and Profiling. May 2013

Altair RADIOSS Performance Benchmark and Profiling. May 2013 Altair RADIOSS Performance Benchmark and Profiling May 2013 Note The following research was performed under the HPC Advisory Council activities Participating vendors: Altair, AMD, Dell, Mellanox Compute

More information

The Effect of In-Network Computing-Capable Interconnects on the Scalability of CAE Simulations

The Effect of In-Network Computing-Capable Interconnects on the Scalability of CAE Simulations The Effect of In-Network Computing-Capable Interconnects on the Scalability of CAE Simulations Ophir Maor HPC Advisory Council ophir@hpcadvisorycouncil.com The HPC-AI Advisory Council World-wide HPC non-profit

More information

ABySS Performance Benchmark and Profiling. May 2010

ABySS Performance Benchmark and Profiling. May 2010 ABySS Performance Benchmark and Profiling May 2010 Note The following research was performed under the HPC Advisory Council activities Participating vendors: AMD, Dell, Mellanox Compute resource - HPC

More information

GROMACS Performance Benchmark and Profiling. September 2012

GROMACS Performance Benchmark and Profiling. September 2012 GROMACS Performance Benchmark and Profiling September 2012 Note The following research was performed under the HPC Advisory Council activities Participating vendors: AMD, Dell, Mellanox Compute resource

More information

GROMACS Performance Benchmark and Profiling. August 2011

GROMACS Performance Benchmark and Profiling. August 2011 GROMACS Performance Benchmark and Profiling August 2011 Note The following research was performed under the HPC Advisory Council activities Participating vendors: Intel, Dell, Mellanox Compute resource

More information

CP2K Performance Benchmark and Profiling. April 2011

CP2K Performance Benchmark and Profiling. April 2011 CP2K Performance Benchmark and Profiling April 2011 Note The following research was performed under the HPC Advisory Council activities Participating vendors: AMD, Dell, Mellanox Compute resource - HPC

More information

OCTOPUS Performance Benchmark and Profiling. June 2015

OCTOPUS Performance Benchmark and Profiling. June 2015 OCTOPUS Performance Benchmark and Profiling June 2015 2 Note The following research was performed under the HPC Advisory Council activities Special thanks for: HP, Mellanox For more information on the

More information

ANSYS Fluent 14 Performance Benchmark and Profiling. October 2012

ANSYS Fluent 14 Performance Benchmark and Profiling. October 2012 ANSYS Fluent 14 Performance Benchmark and Profiling October 2012 Note The following research was performed under the HPC Advisory Council activities Special thanks for: HP, Mellanox For more information

More information

ICON Performance Benchmark and Profiling. March 2012

ICON Performance Benchmark and Profiling. March 2012 ICON Performance Benchmark and Profiling March 2012 Note The following research was performed under the HPC Advisory Council activities Participating vendors: Intel, Dell, Mellanox Compute resource - HPC

More information

NAMD Performance Benchmark and Profiling. February 2012

NAMD Performance Benchmark and Profiling. February 2012 NAMD Performance Benchmark and Profiling February 2012 Note The following research was performed under the HPC Advisory Council activities Participating vendors: AMD, Dell, Mellanox Compute resource -

More information

STAR-CCM+ Performance Benchmark and Profiling. July 2014

STAR-CCM+ Performance Benchmark and Profiling. July 2014 STAR-CCM+ Performance Benchmark and Profiling July 2014 Note The following research was performed under the HPC Advisory Council activities Participating vendors: CD-adapco, Intel, Dell, Mellanox Compute

More information

AMBER 11 Performance Benchmark and Profiling. July 2011

AMBER 11 Performance Benchmark and Profiling. July 2011 AMBER 11 Performance Benchmark and Profiling July 2011 Note The following research was performed under the HPC Advisory Council activities Participating vendors: AMD, Dell, Mellanox Compute resource -

More information

CP2K Performance Benchmark and Profiling. April 2011

CP2K Performance Benchmark and Profiling. April 2011 CP2K Performance Benchmark and Profiling April 2011 Note The following research was performed under the HPC Advisory Council HPC works working group activities Participating vendors: HP, Intel, Mellanox

More information

AcuSolve Performance Benchmark and Profiling. October 2011

AcuSolve Performance Benchmark and Profiling. October 2011 AcuSolve Performance Benchmark and Profiling October 2011 Note The following research was performed under the HPC Advisory Council activities Participating vendors: AMD, Dell, Mellanox, Altair Compute

More information

HYCOM Performance Benchmark and Profiling

HYCOM Performance Benchmark and Profiling HYCOM Performance Benchmark and Profiling Jan 2011 Acknowledgment: - The DoD High Performance Computing Modernization Program Note The following research was performed under the HPC Advisory Council activities

More information

Himeno Performance Benchmark and Profiling. December 2010

Himeno Performance Benchmark and Profiling. December 2010 Himeno Performance Benchmark and Profiling December 2010 Note The following research was performed under the HPC Advisory Council activities Participating vendors: AMD, Dell, Mellanox Compute resource

More information

Performance Analysis of LS-DYNA in Huawei HPC Environment

Performance Analysis of LS-DYNA in Huawei HPC Environment Performance Analysis of LS-DYNA in Huawei HPC Environment Pak Lui, Zhanxian Chen, Xiangxu Fu, Yaoguo Hu, Jingsong Huang Huawei Technologies Abstract LS-DYNA is a general-purpose finite element analysis

More information

CESM (Community Earth System Model) Performance Benchmark and Profiling. August 2011

CESM (Community Earth System Model) Performance Benchmark and Profiling. August 2011 CESM (Community Earth System Model) Performance Benchmark and Profiling August 2011 Note The following research was performed under the HPC Advisory Council activities Participating vendors: Intel, Dell,

More information

LAMMPS Performance Benchmark and Profiling. July 2012

LAMMPS Performance Benchmark and Profiling. July 2012 LAMMPS Performance Benchmark and Profiling July 2012 Note The following research was performed under the HPC Advisory Council activities Participating vendors: AMD, Dell, Mellanox Compute resource - HPC

More information

Scheduling Strategies for HPC as a Service (HPCaaS) for Bio-Science Applications

Scheduling Strategies for HPC as a Service (HPCaaS) for Bio-Science Applications Scheduling Strategies for HPC as a Service (HPCaaS) for Bio-Science Applications Sep 2009 Gilad Shainer, Tong Liu (Mellanox); Jeffrey Layton (Dell); Joshua Mora (AMD) High Performance Interconnects for

More information

n N c CIni.o ewsrg.au

n N c CIni.o ewsrg.au @NCInews NCI and Raijin National Computational Infrastructure 2 Our Partners General purpose, highly parallel processors High FLOPs/watt and FLOPs/$ Unit of execution Kernel Separate memory subsystem GPGPU

More information

The Impact of Inter-node Latency versus Intra-node Latency on HPC Applications The 23 rd IASTED International Conference on PDCS 2011

The Impact of Inter-node Latency versus Intra-node Latency on HPC Applications The 23 rd IASTED International Conference on PDCS 2011 The Impact of Inter-node Latency versus Intra-node Latency on HPC Applications The 23 rd IASTED International Conference on PDCS 2011 HPC Scale Working Group, Dec 2011 Gilad Shainer, Pak Lui, Tong Liu,

More information

Outline. Motivation Parallel k-means Clustering Intel Computing Architectures Baseline Performance Performance Optimizations Future Trends

Outline. Motivation Parallel k-means Clustering Intel Computing Architectures Baseline Performance Performance Optimizations Future Trends Collaborators: Richard T. Mills, Argonne National Laboratory Sarat Sreepathi, Oak Ridge National Laboratory Forrest M. Hoffman, Oak Ridge National Laboratory Jitendra Kumar, Oak Ridge National Laboratory

More information

Performance Optimizations via Connect-IB and Dynamically Connected Transport Service for Maximum Performance on LS-DYNA

Performance Optimizations via Connect-IB and Dynamically Connected Transport Service for Maximum Performance on LS-DYNA Performance Optimizations via Connect-IB and Dynamically Connected Transport Service for Maximum Performance on LS-DYNA Pak Lui, Gilad Shainer, Brian Klaff Mellanox Technologies Abstract From concept to

More information

NAMD Performance Benchmark and Profiling. November 2010

NAMD Performance Benchmark and Profiling. November 2010 NAMD Performance Benchmark and Profiling November 2010 Note The following research was performed under the HPC Advisory Council activities Participating vendors: HP, Mellanox Compute resource - HPC Advisory

More information

NAMD GPU Performance Benchmark. March 2011

NAMD GPU Performance Benchmark. March 2011 NAMD GPU Performance Benchmark March 2011 Note The following research was performed under the HPC Advisory Council activities Participating vendors: Dell, Intel, Mellanox Compute resource - HPC Advisory

More information

Dell EMC Ready Bundle for HPC Digital Manufacturing Dassault Systѐmes Simulia Abaqus Performance

Dell EMC Ready Bundle for HPC Digital Manufacturing Dassault Systѐmes Simulia Abaqus Performance Dell EMC Ready Bundle for HPC Digital Manufacturing Dassault Systѐmes Simulia Abaqus Performance This Dell EMC technical white paper discusses performance benchmarking results and analysis for Simulia

More information

Maximizing Cluster Scalability for LS-DYNA

Maximizing Cluster Scalability for LS-DYNA Maximizing Cluster Scalability for LS-DYNA Pak Lui 1, David Cho 1, Gerald Lotto 1, Gilad Shainer 1 1 Mellanox Technologies, Inc. Sunnyvale, CA, USA 1 Abstract High performance network interconnect is an

More information

UAntwerpen, 24 June 2016

UAntwerpen, 24 June 2016 Tier-1b Info Session UAntwerpen, 24 June 2016 VSC HPC environment Tier - 0 47 PF Tier -1 623 TF Tier -2 510 Tf 16,240 CPU cores 128/256 GB memory/node IB EDR interconnect Tier -3 HOPPER/TURING STEVIN THINKING/CEREBRO

More information

Application Performance on Dual Processor Cluster Nodes

Application Performance on Dual Processor Cluster Nodes Application Performance on Dual Processor Cluster Nodes by Kent Milfeld milfeld@tacc.utexas.edu edu Avijit Purkayastha, Kent Milfeld, Chona Guiang, Jay Boisseau TEXAS ADVANCED COMPUTING CENTER Thanks Newisys

More information

LAMMPSCUDA GPU Performance. April 2011

LAMMPSCUDA GPU Performance. April 2011 LAMMPSCUDA GPU Performance April 2011 Note The following research was performed under the HPC Advisory Council activities Participating vendors: Dell, Intel, Mellanox Compute resource - HPC Advisory Council

More information

Birds of a Feather Presentation

Birds of a Feather Presentation Mellanox InfiniBand QDR 4Gb/s The Fabric of Choice for High Performance Computing Gilad Shainer, shainer@mellanox.com June 28 Birds of a Feather Presentation InfiniBand Technology Leadership Industry Standard

More information

HPC Architectures. Types of resource currently in use

HPC Architectures. Types of resource currently in use HPC Architectures Types of resource currently in use Reusing this material This work is licensed under a Creative Commons Attribution- NonCommercial-ShareAlike 4.0 International License. http://creativecommons.org/licenses/by-nc-sa/4.0/deed.en_us

More information

Performance Optimizations for LS-DYNA with Mellanox HPC-X Scalable Software Toolkit

Performance Optimizations for LS-DYNA with Mellanox HPC-X Scalable Software Toolkit Performance Optimizations for LS-DYNA with Mellanox HPC-X Scalable Software Toolkit Pak Lui 1, David Cho 1, Gilad Shainer 1, Scot Schultz 1, Brian Klaff 1 1 Mellanox Technologies, Inc. 1 Abstract From

More information

OPEN MPI WITH RDMA SUPPORT AND CUDA. Rolf vandevaart, NVIDIA

OPEN MPI WITH RDMA SUPPORT AND CUDA. Rolf vandevaart, NVIDIA OPEN MPI WITH RDMA SUPPORT AND CUDA Rolf vandevaart, NVIDIA OVERVIEW What is CUDA-aware History of CUDA-aware support in Open MPI GPU Direct RDMA support Tuning parameters Application example Future work

More information

NEMO Performance Benchmark and Profiling. May 2011

NEMO Performance Benchmark and Profiling. May 2011 NEMO Performance Benchmark and Profiling May 2011 Note The following research was performed under the HPC Advisory Council HPC works working group activities Participating vendors: HP, Intel, Mellanox

More information

2008 International ANSYS Conference

2008 International ANSYS Conference 2008 International ANSYS Conference Maximizing Productivity With InfiniBand-Based Clusters Gilad Shainer Director of Technical Marketing Mellanox Technologies 2008 ANSYS, Inc. All rights reserved. 1 ANSYS,

More information

APENet: LQCD clusters a la APE

APENet: LQCD clusters a la APE Overview Hardware/Software Benchmarks Conclusions APENet: LQCD clusters a la APE Concept, Development and Use Roberto Ammendola Istituto Nazionale di Fisica Nucleare, Sezione Roma Tor Vergata Centro Ricerce

More information

EARLY EVALUATION OF THE CRAY XC40 SYSTEM THETA

EARLY EVALUATION OF THE CRAY XC40 SYSTEM THETA EARLY EVALUATION OF THE CRAY XC40 SYSTEM THETA SUDHEER CHUNDURI, SCOTT PARKER, KEVIN HARMS, VITALI MOROZOV, CHRIS KNIGHT, KALYAN KUMARAN Performance Engineering Group Argonne Leadership Computing Facility

More information

Application Performance Optimizations. Pak Lui

Application Performance Optimizations. Pak Lui Application Performance Optimizations Pak Lui 2 140 Applications Best Practices Published Abaqus CPMD LS-DYNA MILC AcuSolve Dacapo minife OpenMX Amber Desmond MILC PARATEC AMG DL-POLY MSC Nastran PFA AMR

More information

Advances of parallel computing. Kirill Bogachev May 2016

Advances of parallel computing. Kirill Bogachev May 2016 Advances of parallel computing Kirill Bogachev May 2016 Demands in Simulations Field development relies more and more on static and dynamic modeling of the reservoirs that has come a long way from being

More information

Determining Optimal MPI Process Placement for Large- Scale Meteorology Simulations with SGI MPIplace

Determining Optimal MPI Process Placement for Large- Scale Meteorology Simulations with SGI MPIplace Determining Optimal MPI Process Placement for Large- Scale Meteorology Simulations with SGI MPIplace James Southern, Jim Tuccillo SGI 25 October 2016 0 Motivation Trend in HPC continues to be towards more

More information

arxiv: v2 [hep-lat] 3 Nov 2016

arxiv: v2 [hep-lat] 3 Nov 2016 MILC staggered conjugate gradient performance on Intel KNL arxiv:1611.00728v2 [hep-lat] 3 Nov 2016 Department of Physics, Indiana University, Bloomington IN 47405, USA E-mail: ruizli@umail.iu.edu Carleton

More information

Benchmark results on Knight Landing (KNL) architecture

Benchmark results on Knight Landing (KNL) architecture Benchmark results on Knight Landing (KNL) architecture Domenico Guida, CINECA SCAI (Bologna) Giorgio Amati, CINECA SCAI (Roma) Roma 23/10/2017 KNL, BDW, SKL A1 BDW A2 KNL A3 SKL cores per node 2 x 18 @2.3

More information

Clustering Optimizations How to achieve optimal performance? Pak Lui

Clustering Optimizations How to achieve optimal performance? Pak Lui Clustering Optimizations How to achieve optimal performance? Pak Lui 130 Applications Best Practices Published Abaqus CPMD LS-DYNA MILC AcuSolve Dacapo minife OpenMX Amber Desmond MILC PARATEC AMG DL-POLY

More information

Maximize Performance and Scalability of RADIOSS* Structural Analysis Software on Intel Xeon Processor E7 v2 Family-Based Platforms

Maximize Performance and Scalability of RADIOSS* Structural Analysis Software on Intel Xeon Processor E7 v2 Family-Based Platforms Maximize Performance and Scalability of RADIOSS* Structural Analysis Software on Family-Based Platforms Executive Summary Complex simulations of structural and systems performance, such as car crash simulations,

More information

CUDA Accelerated Linpack on Clusters. E. Phillips, NVIDIA Corporation

CUDA Accelerated Linpack on Clusters. E. Phillips, NVIDIA Corporation CUDA Accelerated Linpack on Clusters E. Phillips, NVIDIA Corporation Outline Linpack benchmark CUDA Acceleration Strategy Fermi DGEMM Optimization / Performance Linpack Results Conclusions LINPACK Benchmark

More information

Interconnect Your Future

Interconnect Your Future Interconnect Your Future Smart Interconnect for Next Generation HPC Platforms Gilad Shainer, August 2016, 4th Annual MVAPICH User Group (MUG) Meeting Mellanox Connects the World s Fastest Supercomputer

More information

Erkenntnisse aus aktuellen Performance- Messungen mit LS-DYNA

Erkenntnisse aus aktuellen Performance- Messungen mit LS-DYNA 14. LS-DYNA Forum, Oktober 2016, Bamberg Erkenntnisse aus aktuellen Performance- Messungen mit LS-DYNA Eric Schnepf 1, Dr. Eckardt Kehl 1, Chih-Song Kuo 2, Dymitrios Kyranas 2 1 Fujitsu Technology Solutions

More information

Making Supercomputing More Available and Accessible Windows HPC Server 2008 R2 Beta 2 Microsoft High Performance Computing April, 2010

Making Supercomputing More Available and Accessible Windows HPC Server 2008 R2 Beta 2 Microsoft High Performance Computing April, 2010 Making Supercomputing More Available and Accessible Windows HPC Server 2008 R2 Beta 2 Microsoft High Performance Computing April, 2010 Windows HPC Server 2008 R2 Windows HPC Server 2008 R2 makes supercomputing

More information

LS-DYNA Best-Practices: Networking, MPI and Parallel File System Effect on LS-DYNA Performance

LS-DYNA Best-Practices: Networking, MPI and Parallel File System Effect on LS-DYNA Performance 11 th International LS-DYNA Users Conference Computing Technology LS-DYNA Best-Practices: Networking, MPI and Parallel File System Effect on LS-DYNA Performance Gilad Shainer 1, Tong Liu 2, Jeff Layton

More information

Performance of Mellanox ConnectX Adapter on Multi-core Architectures Using InfiniBand. Abstract

Performance of Mellanox ConnectX Adapter on Multi-core Architectures Using InfiniBand. Abstract Performance of Mellanox ConnectX Adapter on Multi-core Architectures Using InfiniBand Abstract...1 Introduction...2 Overview of ConnectX Architecture...2 Performance Results...3 Acknowledgments...7 For

More information

Introduc)on to Hyades

Introduc)on to Hyades Introduc)on to Hyades Shawfeng Dong Department of Astronomy & Astrophysics, UCSSC Hyades 1 Hardware Architecture 2 Accessing Hyades 3 Compu)ng Environment 4 Compiling Codes 5 Running Jobs 6 Visualiza)on

More information

LS-DYNA Performance on Intel Scalable Solutions

LS-DYNA Performance on Intel Scalable Solutions LS-DYNA Performance on Intel Scalable Solutions Nick Meng, Michael Strassmaier, James Erwin, Intel nick.meng@intel.com, michael.j.strassmaier@intel.com, james.erwin@intel.com Jason Wang, LSTC jason@lstc.com

More information

Bei Wang, Dmitry Prohorov and Carlos Rosales

Bei Wang, Dmitry Prohorov and Carlos Rosales Bei Wang, Dmitry Prohorov and Carlos Rosales Aspects of Application Performance What are the Aspects of Performance Intel Hardware Features Omni-Path Architecture MCDRAM 3D XPoint Many-core Xeon Phi AVX-512

More information

DELL EMC ISILON F800 AND H600 I/O PERFORMANCE

DELL EMC ISILON F800 AND H600 I/O PERFORMANCE DELL EMC ISILON F800 AND H600 I/O PERFORMANCE ABSTRACT This white paper provides F800 and H600 performance data. It is intended for performance-minded administrators of large compute clusters that access

More information

IFS RAPS14 benchmark on 2 nd generation Intel Xeon Phi processor

IFS RAPS14 benchmark on 2 nd generation Intel Xeon Phi processor IFS RAPS14 benchmark on 2 nd generation Intel Xeon Phi processor D.Sc. Mikko Byckling 17th Workshop on High Performance Computing in Meteorology October 24 th 2016, Reading, UK Legal Disclaimer & Optimization

More information

INTRODUCTION TO THE ARCHER KNIGHTS LANDING CLUSTER. Adrian

INTRODUCTION TO THE ARCHER KNIGHTS LANDING CLUSTER. Adrian INTRODUCTION TO THE ARCHER KNIGHTS LANDING CLUSTER Adrian Jackson adrianj@epcc.ed.ac.uk @adrianjhpc Processors The power used by a CPU core is proportional to Clock Frequency x Voltage 2 In the past, computers

More information

GPUs and Emerging Architectures

GPUs and Emerging Architectures GPUs and Emerging Architectures Mike Giles mike.giles@maths.ox.ac.uk Mathematical Institute, Oxford University e-infrastructure South Consortium Oxford e-research Centre Emerging Architectures p. 1 CPUs

More information

Performance and Energy Efficiency of the 14 th Generation Dell PowerEdge Servers

Performance and Energy Efficiency of the 14 th Generation Dell PowerEdge Servers Performance and Energy Efficiency of the 14 th Generation Dell PowerEdge Servers This white paper details the performance improvements of Dell PowerEdge servers with the Intel Xeon Processor Scalable CPU

More information

Ravindra Babu Ganapathi

Ravindra Babu Ganapathi 14 th ANNUAL WORKSHOP 2018 INTEL OMNI-PATH ARCHITECTURE AND NVIDIA GPU SUPPORT Ravindra Babu Ganapathi Intel Corporation [ April, 2018 ] Intel MPI Open MPI MVAPICH2 IBM Platform MPI SHMEM Intel MPI Open

More information

NUMA-aware OpenMP Programming

NUMA-aware OpenMP Programming NUMA-aware OpenMP Programming Dirk Schmidl IT Center, RWTH Aachen University Member of the HPC Group schmidl@itc.rwth-aachen.de Christian Terboven IT Center, RWTH Aachen University Deputy lead of the HPC

More information

Resources Current and Future Systems. Timothy H. Kaiser, Ph.D.

Resources Current and Future Systems. Timothy H. Kaiser, Ph.D. Resources Current and Future Systems Timothy H. Kaiser, Ph.D. tkaiser@mines.edu 1 Most likely talk to be out of date History of Top 500 Issues with building bigger machines Current and near future academic

More information

Intel MIC Programming Workshop, Hardware Overview & Native Execution. IT4Innovations, Ostrava,

Intel MIC Programming Workshop, Hardware Overview & Native Execution. IT4Innovations, Ostrava, , Hardware Overview & Native Execution IT4Innovations, Ostrava, 3.2.- 4.2.2016 1 Agenda Intro @ accelerators on HPC Architecture overview of the Intel Xeon Phi (MIC) Programming models Native mode programming

More information

Intel MIC Programming Workshop, Hardware Overview & Native Execution LRZ,

Intel MIC Programming Workshop, Hardware Overview & Native Execution LRZ, Intel MIC Programming Workshop, Hardware Overview & Native Execution LRZ, 27.6.- 29.6.2016 1 Agenda Intro @ accelerators on HPC Architecture overview of the Intel Xeon Phi Products Programming models Native

More information

Multicore Performance and Tools. Part 1: Topology, affinity, clock speed

Multicore Performance and Tools. Part 1: Topology, affinity, clock speed Multicore Performance and Tools Part 1: Topology, affinity, clock speed Tools for Node-level Performance Engineering Gather Node Information hwloc, likwid-topology, likwid-powermeter Affinity control and

More information

Parallel Applications on Distributed Memory Systems. Le Yan HPC User LSU

Parallel Applications on Distributed Memory Systems. Le Yan HPC User LSU Parallel Applications on Distributed Memory Systems Le Yan HPC User Services @ LSU Outline Distributed memory systems Message Passing Interface (MPI) Parallel applications 6/3/2015 LONI Parallel Programming

More information

arxiv: v1 [hep-lat] 1 Dec 2017

arxiv: v1 [hep-lat] 1 Dec 2017 arxiv:1712.00143v1 [hep-lat] 1 Dec 2017 MILC Code Performance on High End CPU and GPU Supercomputer Clusters Carleton DeTar 1, Steven Gottlieb 2,, Ruizi Li 2,, and Doug Toussaint 3 1 Department of Physics

More information

Accelerating MPI Message Matching and Reduction Collectives For Multi-/Many-core Architectures Mohammadreza Bayatpour, Hari Subramoni, D. K.

Accelerating MPI Message Matching and Reduction Collectives For Multi-/Many-core Architectures Mohammadreza Bayatpour, Hari Subramoni, D. K. Accelerating MPI Message Matching and Reduction Collectives For Multi-/Many-core Architectures Mohammadreza Bayatpour, Hari Subramoni, D. K. Panda Department of Computer Science and Engineering The Ohio

More information

RADIOSS Benchmark Underscores Solver s Scalability, Quality and Robustness

RADIOSS Benchmark Underscores Solver s Scalability, Quality and Robustness RADIOSS Benchmark Underscores Solver s Scalability, Quality and Robustness HPC Advisory Council studies performance evaluation, scalability analysis and optimization tuning of RADIOSS 12.0 on a modern

More information

Mellanox GPUDirect RDMA User Manual

Mellanox GPUDirect RDMA User Manual Mellanox GPUDirect RDMA User Manual Rev 1.0 www.mellanox.com NOTE: THIS HARDWARE, SOFTWARE OR TEST SUITE PRODUCT ( PRODUCT(S) ) AND ITS RELATED DOCUMENTATION ARE PROVIDED BY MELLANOX TECHNOLOGIES AS-IS

More information

Advanced Threading and Optimization

Advanced Threading and Optimization Mikko Byckling, CSC Michael Klemm, Intel Advanced Threading and Optimization February 24-26, 2015 PRACE Advanced Training Centre CSC IT Center for Science Ltd, Finland!$omp parallel do collapse(3) do p4=1,p4d

More information

MPI Optimizations via MXM and FCA for Maximum Performance on LS-DYNA

MPI Optimizations via MXM and FCA for Maximum Performance on LS-DYNA MPI Optimizations via MXM and FCA for Maximum Performance on LS-DYNA Gilad Shainer 1, Tong Liu 1, Pak Lui 1, Todd Wilde 1 1 Mellanox Technologies Abstract From concept to engineering, and from design to

More information

BNL FY17-18 Procurement

BNL FY17-18 Procurement BNL FY17-18 Procurement USQCD ll-hands Meeting JLB pril 28-29, 2017 Robert Mawhinney Columbia University Co Site rchitect - BNL 1 BGQ Computers at BNL USQCD half-rack (512 nodes) 2 racks RBRC 1 rack of

More information

unleashed the future Intel Xeon Scalable Processors for High Performance Computing Alexey Belogortsev Field Application Engineer

unleashed the future Intel Xeon Scalable Processors for High Performance Computing Alexey Belogortsev Field Application Engineer the future unleashed Alexey Belogortsev Field Application Engineer Intel Xeon Scalable Processors for High Performance Computing Growing Challenges in System Architecture The Walls System Bottlenecks Divergent

More information

HPC and AI Solution Overview. Garima Kochhar HPC and AI Innovation Lab

HPC and AI Solution Overview. Garima Kochhar HPC and AI Innovation Lab HPC and AI Solution Overview Garima Kochhar HPC and AI Innovation Lab 1 Dell EMC HPC and DL team charter Design, develop and integrate HPC and DL Heading systems Lorem ipsum dolor sit amet, consectetur

More information

COSC 6385 Computer Architecture - Multi Processor Systems

COSC 6385 Computer Architecture - Multi Processor Systems COSC 6385 Computer Architecture - Multi Processor Systems Fall 2006 Classification of Parallel Architectures Flynn s Taxonomy SISD: Single instruction single data Classical von Neumann architecture SIMD:

More information

IXPUG 16. Dmitry Durnov, Intel MPI team

IXPUG 16. Dmitry Durnov, Intel MPI team IXPUG 16 Dmitry Durnov, Intel MPI team Agenda - Intel MPI 2017 Beta U1 product availability - New features overview - Competitive results - Useful links - Q/A 2 Intel MPI 2017 Beta U1 is available! Key

More information

OPEN MPI AND RECENT TRENDS IN NETWORK APIS

OPEN MPI AND RECENT TRENDS IN NETWORK APIS 12th ANNUAL WORKSHOP 2016 OPEN MPI AND RECENT TRENDS IN NETWORK APIS #OFADevWorkshop HOWARD PRITCHARD (HOWARDP@LANL.GOV) LOS ALAMOS NATIONAL LAB LA-UR-16-22559 OUTLINE Open MPI background and release timeline

More information

Performance and Energy Usage of Workloads on KNL and Haswell Architectures

Performance and Energy Usage of Workloads on KNL and Haswell Architectures Performance and Energy Usage of Workloads on KNL and Haswell Architectures Tyler Allen 1 Christopher Daley 2 Doug Doerfler 2 Brian Austin 2 Nicholas Wright 2 1 Clemson University 2 National Energy Research

More information

Single-Points of Performance

Single-Points of Performance Single-Points of Performance Mellanox Technologies Inc. 29 Stender Way, Santa Clara, CA 9554 Tel: 48-97-34 Fax: 48-97-343 http://www.mellanox.com High-performance computations are rapidly becoming a critical

More information

Xeon Phi Native Mode - Sharpen Exercise

Xeon Phi Native Mode - Sharpen Exercise Xeon Phi Native Mode - Sharpen Exercise Fiona Reid, Andrew Turner, Dominic Sloan-Murphy, David Henty, Adrian Jackson Contents June 19, 2015 1 Aims 1 2 Introduction 1 3 Instructions 2 3.1 Log into yellowxx

More information

ADINA DMP System 9.3 Installation Notes

ADINA DMP System 9.3 Installation Notes ADINA DMP System 9.3 Installation Notes for Linux (only) Updated for version 9.3.2 ADINA R & D, Inc. 71 Elton Avenue Watertown, MA 02472 support@adina.com www.adina.com ADINA DMP System 9.3 Installation

More information

High Performance Linpack Benchmark on AMD EPYC Processors

High Performance Linpack Benchmark on AMD EPYC Processors High Performance Linpack Benchmark on AMD EPYC Processors This document details running the High Performance Linpack (HPL) benchmark using the AMD xhpl binary. HPL Implementation: The HPL benchmark presents

More information

INTRODUCTION TO THE ARCHER KNIGHTS LANDING CLUSTER. Adrian

INTRODUCTION TO THE ARCHER KNIGHTS LANDING CLUSTER. Adrian INTRODUCTION TO THE ARCHER KNIGHTS LANDING CLUSTER Adrian Jackson a.jackson@epcc.ed.ac.uk @adrianjhpc Processors The power used by a CPU core is proportional to Clock Frequency x Voltage 2 In the past,

More information

Improving Application Performance and Predictability using Multiple Virtual Lanes in Modern Multi-Core InfiniBand Clusters

Improving Application Performance and Predictability using Multiple Virtual Lanes in Modern Multi-Core InfiniBand Clusters Improving Application Performance and Predictability using Multiple Virtual Lanes in Modern Multi-Core InfiniBand Clusters Hari Subramoni, Ping Lai, Sayantan Sur and Dhabhaleswar. K. Panda Department of

More information

Atos ARM solutions for HPC

Atos ARM solutions for HPC Atos ARM solutions for HPC Eric Eppe Head of Solution Marketing & Portfolio HPC & Quantum Global Business Line Tuesday, March 7th, HPC User Forum, TERATEC Atos HPC and ARM A long time engagement 2012 2013

More information