Scheduling Strategies for HPC as a Service (HPCaaS) for Bio-Science Applications
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1 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 Distributed Computing (HPI-DC)
2 Note The following research was performed under the HPC Advisory Council activities Special thanks to AMD, Dell, Mellanox Technologies For more info please refer to Testing center: HPC Advisory Council HPC Center 2
3 HPC Advisory Council WW HPC organization (90 members) Bridges the gap between HPC usage and its potential Provides best practices and a support/development center Explores future technologies and future developments Explores advanced topics HPC in a cloud, HPCaaS etc. Leading edge solutions and technology demonstrations For more info: 3
4 HPC Advisory Council 4
5 HPC Centers Evolution App1 App2 App3 App1 App2 App3 From equipment warehouse to service provider From dedicated HW per application to application services Higher productivity, simplicity and efficiency 5
6 Research Objectives Investigate HPC as a Service for bio-science applications In particular NAMD and CPMD Performance and productivity impact Cluster interconnect effects on applications performance For specific information on NAMD and CPMD, please refer to additional studies at 6
7 Car-Parrinello Molecular Dynamics (CPMD) A parallelized implementation of density functional theory (DFT) Particularly designed for ab-initio molecular dynamics Brings together methods Classical molecular dynamics Solid state physics Quantum chemistry CPMD supports MPI and Mixed MPI/SMP CPMD is distributed and developed by the CPMD consortium 7
8 NAMD A parallel, object-oriented molecular dynamics software Designed for high-performance simulation of large biomolecular systems Millions of atoms Developed by the joint collaboration of the Theoretical and Computational Biophysics Group (TCB) and the Parallel Programming Laboratory (PPL) at the University of Illinois at Urbana-Champaign NAMD is distributed free of charge with source code 8
9 Test Cluster Configuration Dell PowerEdge SC node cluster Quad-Core AMD Opteron 2382 ( Shanghai ) CPUs Mellanox InfiniBand ConnectX DDR HCAs and InfiniBand DDR Switch Memory: 16GB memory, DDR2 800MHz per node OS: RHEL5U2, OFED 1.3 InfiniBand SW stack MPI: Open MPI 1.3, Platform MPI Compiler: GCC Benchmark Application: CPMD 3.13 Benchmark Dataset: C120 NAMD 2.6 with fftw3 libraries and Charm Benchmark Dataset: ApoA1 (92,224 atoms, 12A cutoff) 9
10 InfiniBand Roadmap Industry Standard Hardware, software, cabling, management Design for clustering and storage interconnect Performance 40Gb/s node-to-node 120Gb/s switch-to-switch 1us application latency Most aggressive roadmap in the industry Reliable with congestion management Efficient RDMA and Transport Offload Kernel bypass CPU focuses on application processing Scalable for Petascale computing & beyond End-to-end quality of service Virtualization acceleration I/O consolidation Including storage The InfiniBand Performance Gap is Increasing 60Gb/s 20Gb/s 120Gb/s 40Gb/s 240Gb/s (12X) 80Gb/s (4X) Ethernet Fibre Channel 10
11 Quad-Core AMD Opteron Processor Performance Quad-Core Enhanced CPU IPC 4x 512K L2 cache 8 GB/S 6MB L3 Cache 8 GB/S Direct Connect Architecture HyperTransport Technology Up to 24 GB/s peak per processor 8 GB/S 8 GB/S Floating Point PCI-E PCI-E 128-bit FPU per core Bridge Bridge 4 FLOPS/clk peak per core 8 GB/S Integrated Memory Controller USB Up to 12.8 GB/s I/O Hub DDR2-800 MHz or DDR2-667 MHz PCI Scalability 48-bit Physical Addressing Compatibility Same power/thermal envelopes as 2 nd / 3 rd generation AMD Opteron CPU Dual Channel Reg DDR2 11 November5,
12 Dell PowerEdge Servers helping Simplify IT System Structure and Sizing Guidelines 24-node cluster build with Dell PowerEdge SC 1435 Servers Servers optimized for High Performance Computing environments Building Block Foundations for best price/performance and performance/watt Dell HPC Solutions Scalable Architectures for High Performance and Productivity Dell's comprehensive HPC services help manage the lifecycle requirements. Integrated, Tested and Validated Architectures Workload Modeling Optimized System Size, Configuration and Workloads Test-bed Benchmarks ISV Applications Characterization Best Practices & Usage Analysis 12
13 NAMD Benchmark Results Productivity Case 1: Dedicated hardware resource for NAMD Input Date: ApoA1 Benchmark comprises 92K atoms of lipid, protein, and water Models a bloodstream lipoprotein particle One of the most used data sets for benchmarking NAMD Increasing number of concurrent jobs increases cluster productivity InfiniBand enables higher performance and productivity NAMD (ApoA1) - GigE NAMD (ApoA1) - InfiniBand DDR Jobs/Day Jobs/Day Number of Nodes Number of Nodes Higher is better 1 Job 2 Jobs 1 Job 2 Jobs 13
14 CPMD Benchmark Results Productivity Case 2: Dedicated hardware resource for CPMD Benchmark Data: C carbon atoms Running two jobs in parallel increases cluster productivity InfiniBand enables higher performance and scalability than GigE CPMD C120 - GigE CPMD C120 - InfiniBand DDR Jobs/Day Jobs/Day Number of Nodes Number of Nodes 1 Job 2 Jobs 1 Job 2 Jobs Higher is better 14
15 MPI Collectives in CPMD MPI_AlltoAll is the key collective function in CPMD Number of AlltoAll messages increases dramatically with cluster size MPI Function Distribution (C120 inp-1) Number of Messages (Millions) MPI_Allgather MPI_Allreduce MPI_Alltoall MPI_Bcast MPI_Isend MPI_Recv MPI_Send 4 Nodes 8 Nodes 16 Nodes 15
16 CPMD MPI Message Size Distribution Majority messages are medium size 100% Message Distribution (C120 inp-1) % of Total Alltoall Messages 80% 60% 40% 20% 0% [0..128B] [128B..1KB] [1..8KB] [8..256KB] [256KB..1M] [1M..Infinity] Message Size (Bytes) 4 Nodes 8 Nodes 16 Nodes 16
17 NAMD Message Distribution As number of nodes scales, percentage of small messages increases Percentage of 1KB-256KB messages is relatively consistent for cluster sizes greater than 8 nodes Majority of the messages is in the range of 128B-1KB for cluster size greater than 8 nodes 100% MPI Message Distribution (Apoa1) % of Total Messages 80% 60% 40% 20% 0% [0..128B] [128B..1KB] [1..8KB] [8..256KB] Message Size [256KB..1M] [1M..Infinity] 4 Nodes 8 Nodes 16 Nodes 24 Nodes 17
18 Multiple Applications CPMD and NAMD Case 3 HPC as a Service (HPCaaS) HW platform to serve multiple applications at the same time CMPD and NAMD Multiple test scenarios will be presented in the following slides Each describes different allocation methods of the HW system per service Service refers to a single application At least 2 applications will be served at a given time Each scenario will be to compared to a dedicated HW per applications approach Evaluation metric: productivity (number of jobs per day) 18
19 Multiple Applications CPMD and NAMD Test Scenario: Single Application approach: Two NAMD jobs in parallel for half day then two CPMD jobs for the other half day Multiple Applications approach One CPMD job and one NAMD job simultaneously on the cluster for a full day Case I:4 cores for each application (2 cores on each CPU) Case II: One application per CPU socket Running CPMD and NAMD in parallel improves CPMD productivity Distributing CPMD processes to two sockets has better performance Versus using an entire CPU (socket) per applications NAMD shows negligible productivity difference under the three scenarios 400 CPMD Application Productivity (C120) 3 NAMD Application Productivity (ApoA1) Jobs/Day Jobs/Day Number of Nodes Number of Nodes Single Application Multiple Applications I Multiple Applications II Single Application Multiple Applications I Multiple Applications II 19
20 Multiple Applications CPMD + NAMD Test Scenario: Single Application Two NAMD jobs in parallel for 3/4 day then two CPMD jobs for 1/4 day Multiple Applications One CPMD job and one NAMD job simultaneously on the cluster for a full day 6 cores for NAMD (3 cores on each CPU), 2 cores for CPMD (1 core on each CPU) Running CPMD and NAMD in parallel improves CPMD productivity by up to 61% NAMD shows negligible productivity difference under the two scenarios CPMD Application Productivity (C120) NAMD Application Productivity (ApoA1) Jobs/Day Jobs/Day Number of Nodes Number of Nodes Single Application Multiple Applications Single Application Multiple Applications Higher is better InfiniBand DDR 20
21 Multiple Applications CPMD + NAMD Test Scenario: Single Application Two NAMD jobs in parallel for 1/4 day then two CPMD jobs for 3/4 day Multiple Applications One CPMD job and one NAMD job simultaneously on the cluster for a full day 2 cores for NAMD (1 core on each CPU), 6 cores for CPMD (3 cores on each CPU) Running CPMD with less cores decreases CPMD productivity NAMD shows negligible productivity difference under the two scenarios CPMD Application Productivity (C120) NAMD Application Productivity (ApoA1) Jobs/Day Jobs/Day Number of Nodes Number of Nodes Single Application Multiple Applications Single Application Multiple Applications Higher is better InfiniBand DDR 21
22 Results Summary NAMD Increase number of jobs running on the each node improves productivity InfiniBand provides nearly doubled performance of GigE GigE does not scale beyond 20 nodes CPMD Higher productivity is gained with 2 parallel CPMD jobs on the cluster InfiniBand delivers up to 300% higher productivity vs GigE CPMD and NAMD simultaneously HPC as a Service (HPCaaS) It is feasible and productive to run CPMD and NAMD simultaneously on a single system When enough core were allocated, CPMD productivity was increased NAMD demonstrates same level of productivity NAMD consumes large portion of the systems resources Having more than a single NAMD and CPMD jobs will not increase productivity 22
23 Conclusions HPC as a Service enables greater systems flexibility Eliminates the need for dedicated HW resources per applications Simplifies usage models Enables dynamic allocation per given task Effectively model needs to take into consideration Applications sensitivity points and applications bottlenecks Minimum HW resource requirements per applications Matching up applications with different hardware requirements HPC as a Service for Bio-science applications (CMPD and NAMD) Enables increased or equal productivity versus dedicated HW resource Method: allocation of 4 cores or less for CPMD, 4 cores or more for NAMD Cores per application allocation using both sockets demonstrate higher productivity Better allocation of cores, memory and interconnect resources to minimize contention NAMD requires more or equal compute resources than CPMD 23
24 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 24 24
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