AMBER 11 Performance Benchmark and Profiling. July 2011
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1 AMBER 11 Performance Benchmark and Profiling July 2011
2 Note The following research was performed under the HPC Advisory Council activities Participating vendors: AMD, Dell, Mellanox Compute resource - HPC Advisory Council Cluster Center For more info please refer to
3 AMBER Application AMBER Software for analyzing large-scale molecular dynamics (MD) simulation trajectory data Reads either CHARMM or AMBER style topology/trajectory files as input, and its analysis routines can scale up to thousands of compute cores or hundreds of GPU nodes with either parallel or UNIX file I/O AMBER has dynamic memory management, and each code execution can perform a variety of different structural, energetic, and file manipulation operations on a single MD trajectory at once The code is written in a combination of Fortan90 and C, and its GPU kernels are written with NVIDIA's CUDA API to achieve maximum GPU performance 3
4 Objectives The following was done to provide best practices AMBER performance benchmarking Understanding AMBER communication patterns Ways to increase AMBER productivity Compilers and MPI libraries comparisons The presented results will demonstrate The scalability of the compute environment The capability of AMBER to achieve scalable productivity Considerations for performance optimizations 4
5 Test Cluster Configuration Dell PowerEdge R node (528-core) cluster AMD Opteron 6174 Series processors (codenamed Magny-Cour ) 2.2 GHz 4 CPU sockets per server node Mellanox ConnectX-2 VPI adapters for 40Gb/s QDR InfiniBand and 10Gb/s Ethernet Mellanox MTS3600Q 36-Port 40Gb/s QDR InfiniBand switch Memory: 128GB memory per node DDR3 1333MHz OS: RHEL 5.5, MLNX-OFED InfiniBand SW stack MPI: Open MPI with KNEM 0.9.6, Platform MPI Compilers: PGI 10.9, GNU Compilers Application: AMBER 11 (PMEMD), AmberTools 1.5 Benchmark workload: Cellulose_production_NVE_256_128_128 (408,609 atoms) 5
6 About Dell PowerEdge R node cluster HPC Advisory Council Test-bed System New 11-node 528 core cluster - featuring Dell PowerEdge R815 servers Replacement system for Dell PowerEdge SC1435 (192 cores) cluster system following 2 years of rigorous benchmarking and product EOL System to be redirected to explore HPC in the Cloud applications Workload profiling and benchmarking Characterization for HPC and compute intense environments Optimization for scale, sizing and configuration and workload performance Test-bed Benchmarks RFPs Customers/Prospects, etc ISV & Industry standard application characterization Best practices & usage analysis 6
7 About Dell PowerEdge Platform Advantages Best of breed technologies and partners Combination of AMD Opteron 6000 Series platform and Mellanox ConnectX InfiniBand on Dell HPC. Solutions provide the ultimate platform for speed and scale Dell PowerEdge R815 system delivers 4 socket performance in dense 2U form factor Up to 48 core/32dimms per server 1008 core in 42U enclosure Integrated stacks designed to deliver the best price/performance/watt 2x more memory and processing power in half of the space Energy optimized low flow fans, improved power supplies and dual SD modules Optimized for long-term capital and operating investment protection System expansion Component upgrades and feature releases 7
8 AMBER Performance MPI Platform MPI enables better scalability than Open MPI Seen up to 86% better performance with Platform MPI Tuned Platform MPI provides better performance for InfiniBand Seen an speed improvement of up to 28% at 8-node Tuning parameters used: -cpu_bind -e MPI_RDMA_MSGSIZE=65536,65536, e MPI_RDMA_NSRQRECV=2048 -e MPI_RDMA_NFRAGMENT=128 28% 86% Higher is better 48 Cores/Node 8
9 AMBER Performance Compilers Executable generated by the GNU compilers runs slightly faster Up to 3% faster is seen than with PGI Using the default optimization and linker flags: PGI: -fast -O3 -fastsse GNU: -O3 3% Higher is better Platform MPI 48 Cores/Node 9
10 AMBER Performance CPU Frequency Higher CPU core frequency enables higher job performance Up to 11-14% better job performance between 2200MHz vs 1800MHz Shows the performance is somewhat affected by the change in the CPU frequency, and the percentage of Computation versus MPI Communication 14% 11% Higher is better 48 Cores/Node 10
11 AMBER Profiling MPI/User Time Ratio Large data communications are seen Shows heavy data communications between parallel tasks More communications than computation happens between 4 to 8 node MPI time stays constant while CPU time reduces as more nodes in cluster Demonstrate the importance of the interconnect to handle addition network throughput InfiniBand QDR 48 Cores/Node 11
12 AMBER Profiling Number of MPI Calls The most used MPI functions are MPI_Isend, MPI_Irecv, MPI_Waitany These non-blocking MPI calls allows computation while communications take place Each of these dominates as a third of MPI calls used on a 8-node job The number of calls accelerates rapidly as the cluster scales 12
13 AMBER Profiling Time Spent of MPI Calls The time in communications is taken place in the following MPI functions: MPI_Waitany (36%) MPI_Allreduce (25%) MPI_Allgather (22%) MPI_Bcast (6%) 13
14 AMBER Profiling MPI Message Sizes Majority of the MPI messages are small and median message sizes In the ranges of less than 64 bytes and between 4K and 16K 14
15 AMBER Profiling Data Transfer By Process Data transferred to each MPI rank is consistent for any number of processes Shows large amount of data transfers happened Amount of data transfer to each rank is reduced as more nodes are in the job 15
16 AMBER Profiling Aggregated Data Transfer Aggregated data transfer refers to: Total amount of data being transferred in the network between all MPI ranks collectively The total data transfer increases steadily as the cluster scales Hugh amount of data being sent and received across the network As a compute node being added, more data communications will take place InfiniBand QDR 16
17 Summary AMBER is a compute and data communications intensive application Which has a high demand for CPU power and good network throughput Using InfiniBand enables good scalability by spreading workload to compute nodes MPI: Better scalability is seen with Platform MPI than with Open MPI CPU: Shows higher job productivity when using CPU with higher core frequency MPI Communications: Large data communications take place between MPI processes 17
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 Mellanox undertakes no duty and assumes no obligation to update or correct any information presented herein 18 18
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