LAMMPS-KOKKOS Performance Benchmark and Profiling. September 2015
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1 LAMMPS-KOKKOS Performance Benchmark and Profiling September 2015
2 2 Note The following research was performed under the HPC Advisory Council activities Participating vendors: Intel, Dell, Mellanox, NVIDIA Compute resource - HPC Advisory Council Cluster Center The following was done to provide best practices LAMMPS performance overview Understanding LAMMPS communication patterns Ways to increase LAMMPS productivity For more info please refer to
3 3 LAMMPS Large-scale Atomic/Molecular Massively Parallel Simulator Classical molecular dynamics code which can model: Atomic, Polymeric, Biological, Metallic, Granular, and coarse-grained systems LAMMPS-KOKKOS package contains Versions of pair, fix, and atom styles that use data structures and macros provided by the Kokkos library LAMMPS runs efficiently in parallel using message-passing techniques Developed at Sandia National Laboratories An open-source code, distributed under GNU Public License
4 Objectives 4 The presented research was done to provide best practices LAMMPS performance benchmarking MPI Library performance comparison Interconnect performance comparison CPUs comparison Optimization tuning The presented results will demonstrate The scalability of the compute environment/application Considerations for higher productivity and efficiency
5 5 Test Cluster Configuration Dell PowerEdge R node (896-core) Thor cluster Dual-Socket 14-Core Intel 2.60 GHz CPUs (BIOS: Maximum Performance, Home Snoop ) Memory: 64GB memory, DDR MHz, Memory Snoop Mode in BIOS sets to Home Snoop OS: RHEL 6.5, MLNX_OFED_LINUX InfiniBand SW stack Hard Drives: 2x 1TB 7.2 RPM SATA 2.5 on RAID 1 Mellanox ConnectX-4 EDR 100Gb/s InfiniBand Adapters Mellanox Switch-IB SB port EDR 100Gb/s InfiniBand Switch Mellanox ConnectX-3 FDR VPI InfiniBand and 40Gb/s Ethernet Adapters Mellanox SwitchX-2 SX port 56Gb/s FDR InfiniBand / VPI Ethernet Switch Dell InfiniBand-Based Lustre Storage based on Dell PowerVault MD3460 and Dell PowerVault MD3420 NVIDIA Tesla K40 (on 8 Nodes) and NVIDIA Tesla K80 GPUs (on 2 Nodes); 1 GPU per node MPI: Mellanox HPC-X v1.3 (based on Open MPI 1.8.7) with CUDA 6.5 and 7.0 support Application: LAMMPS 15May15 Benchmarks: Input data with embedded-atom method (in.eam)
6 6 PowerEdge R730 Massive flexibility for data intensive operations Performance and efficiency Intelligent hardware-driven systems management with extensive power management features Innovative tools including automation for parts replacement and lifecycle manageability Broad choice of networking technologies from Ethernet to InfiniBand Built in redundancy with hot plug and swappable PSU, HDDs and fans Benefits Designed for performance workloads High performance scale-out compute and low cost dense storage in one package Hardware Capabilities Flexible compute platform with dense storage capacity 2S/2U server, 6 PCIe slots Large memory footprint (Up to 768GB / 24 DIMMs) High I/O performance and optional storage configurations HDD options: 12 x or - 24 x x 2.5 HDDs in rear of server Up to 26 HDDs with 2 hot plug drives in rear of server for boot or scratch
7 7 LAMMPS Performance KOKKOS Vars Kokkos variable determines the communication models between host and device The best of kokkos vars used for CPU and GPU tests are different The most favorite kokkos vars for GPU appears to be among #2, 3, or 4 The most favorite kokkos vars for CPU appears to be among #1, 5, 6, or 7 Higher is better 8 Nodes
8 8 LAMMPS Performance CUDA Versions Both CUDA 6.5 and 7.0 versions perform similarly Using the given input data and workload Higher is better 8 Nodes; 1x K40 / Node
9 9 LAMMPS Performance Tesla K40 vs K80 Tesla K80 doubles the performance of K40 using with LAMMPS Demonstrates 110% increase in performance on 2 nodes with 1 GPU per node 110% Higher is better 1 GPU / Node
10 10 LAMMPS Performance Network Interconnects EDR InfiniBand delivers superior scalability in application performance InfiniBand delivers 77% higher performance than 10GbE on 8 nodes Performance of 4 IB nodes outperforms 8 Ethernet (10GbE) nodes Benefits of InfiniBand over Ethernet expect to increase as cluster scales Scalability for Ethernet stops beyond 4 nodes; while InfiniBand continue to scale 77% Higher is better GPU: 1 K40 / Node
11 11 LAMMPS Performance Network Interconnects EDR InfiniBand delivers superior scalability in application performance EDR IB demonstrates an 8% increase on 8 Nodes / 224 Cores Performance gap between FDR and EDR expect to increase as cluster scales 8% Higher is better 28 MPI Processes / Node
12 12 LAMMPS Performance Scalability kokkos_omp demonstrates higher speed up and performance than kokkos_cuda CPU performance outpaces Tesla K40 performance using 28 cores/node With the availability of the Tesla K80, it outperforms CPU; performance gap expect to grow 51% 78% 17% 25% 17% Higher is better CPU: 28 Processes/Node GPU: 1 GPU / Node
13 13 LAMMPS Profiling % of MPI Calls The most time consuming MPI calls for LAMMPS-KOKKOS (cuda): MPI_Send: 67% MPI / 8% Wall MPI_Wait: 18% MPI / 2% Wall MPI_Bcast: 11% MPI / 1% Wall Kokkos_cuda, 8 nodes / 224 processes EDR InfiniBand
14 14 LAMMPS Profiling MPI Message Size Distribution For the most time consuming MPI calls MPI_Send: 48KB (26% MPI) time, 32KB (15%), 56KB (7%) MPI_Wait: 0B (18% MPI time) MPI_Bcast: 4B (10% MPI time) Kokkos_cuda, 8 nodes / 224 processes EDR InfiniBand
15 15 LAMMPS Profiling MPI Memory Consumption Some load imbalance is seen on the processes Memory consumption: About 9GB of memory is used on each compute node for this input data Kokkos_cuda, 8 nodes / 224 processes EDR InfiniBand
16 16 LAMMPS Profiling CUDA Profiler NVIDIA Visual Profiler and nvprof: Profilers for GPUs Shows many Memcpy occurs between host and device throughout the run Kokkos_cuda, 8 nodes / 224 processes EDR InfiniBand
17 17 LAMMPS Summary Performance Compute: cluster of the current generation outperforms system architecture of previous generations Tesla K80 doubles the performance of K40 using with LAMMPS-KOKKOS The KOKKOS vars can be a significant performance implication to the performance of LAMMPS CUDA versions 6.5 and 7.0 performs similarly Network: EDR InfiniBand demonstrates superior scalability in LAMMPS performance EDR IB provides higher performance by 77% over 10GbE, 86% higher over 1GbE on 8 nodes Performance of 4 IB nodes outperforms 8 Ethernet (10GbE) nodes Benefits of InfiniBand over Ethernet expect to increase as cluster scales Scalability for Ethernet stops beyond 4 nodes; while InfiniBand continue to scale MPI Profiles The most time consuming MPI calls for LAMMPS-KOKKOS (cuda): MPI_Send: 67% MPI / 8% Wall MPI_Wait: 18% MPI / 2% Wall MPI_Bcast: 11% MPI / 1% Wall
18 18 18 Thank You HPC Advisory Council 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
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