Immutable Application Containers Reproducibility of CAE-computations through Immutable Application Containers
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1 Immutable Application Containers Reproducibility of CAE-computations through Immutable Application Containers HPC Advisory Council - Swiss Workshop 2015
2 About Me >10y iteration so far SysAdmin, SysOps, SysEngineer, R&D Engineer (hyper-scale web-service) Founder of QNIB Solutions Holistic System Management Containerization of SysOps and EndUser Consultancy / Software Design & Development 2
3 Agenda Docker Introduction How I got here QNIBng QNIBTerminal QNIBMonitoring Immutable Application Containers 3
4 Docker in a Nutshell
5 Logistics Pre-1960 Magnitude of different goods Not well suited together Different environments to handle them Transactional cost fairly high 5
6 Container Logistics Standard Container no mixup easy transaction, no matter the environment 6
7 Container Matrix 7
8 Multiple Guests IB SERVICE SERVICE SERVICE Userland (OS) Userland (OS) Userland (OS) KERNEL KERNEL KERNEL HYPERVISOR (Type II) Userland (OS) Userland (Na<ve) HYPERVIS. (Type I) HOST KERNEL IB SERVICE SERVICE Userland (#1) Userland (#2) HOST KERNEL SERVER SERVER Traditional Virtualisation Containerisation 8
9 Interface View Application Library Abstraction Application Userland Syscall Interface Userland Container Kernel Hardware Interface lightweight abstraction w/o IO overhead or startup latency Hosted Hypervisor Native Host Traditional Virtualisation Host Containerisation 9
10 But when it comes to cloud operations, we see the VM as the only truly safe isolation. Until we see foolproof security for containers, we will always double-bag our customers' workloads. McLuckie (Senior product lead for Google Compute Engine, Feb/2015) 10
11 VM vs. Container Isolation Hypervisor vs. Kernel Namespaces Resource Allocation Hypervisor vs. CGroups Security Hypervisor vs. SELinux/AppAmor 11
12 QNIBng
13 IBPM B.Sc. report in
14 QNIBng Qualified Networking w/ InfiniBand (next generation) Log/Performance Monitoring targeted at IB layer 14
15 QNIBTerminal
16 QNIBTerminal Holistic approach to target complete HPC Stack Log/Events Compute kopf es-kopf slurmd compute<n> ls-indexer logstash elk elasticsearch logstash slurmd compute0 kibana slurmctld slurmctld srv discovery /health check consul grafana influxdb grafana influxdb http-proxy nginx Performance Services QNIBMonitoring 16
17 QNIBMonitoring Containerized Monitoring Stack Logstash Stack (ELK) 17
18 QNIBMonitoring Containerized Monitoring Stack Logstash Stack (ELK) Performance Monitoring (Graphite-Universe) 18
19 QNIBMonitoring Containerized Monitoring Stack Logstash Stack (ELK) Performance Monitoring (Graphite-Universe) Combine Log and Performance Information 19
20 QNIBMonitoring Containerized Monitoring Stack Logstash Stack (ELK) Performance Monitoring (Graphite-Universe) Combine Log and Performance Information Service Discovery / Health Checks 20
21 QNIBTerminal SLURM Events Docker Events 21
22 Conclusion Big head-room to connect layers Trace errors / events, which span over multiple layers holistic view on complete stack High potential within System Operation Configure Once Run anywhere Independent Environment Training Integration Test ProofOfConcept to explore new stack components 22
23 MPI-Workloads w/ Docker
24 HPC Testcluster Venus 8 nodes (CentOS 7, 2x 4core XEON, 32GB, Mellanox ConnectX-2) 3 containers on top CentOS6/7, Ubuntu12 SLURM Resource Scheduler 1 native / 3 container partition Multiple Open MPI version installed 1.5, 1.6,
25 MPI µbenchmark osu-micro-benchmarks osu_alltoall with two tasks on two hosts $ mpirun -np 2 -H venus001,venus002 $(pwd)/osu_alltoall # OSU MPI All-to-All Personalized Exchange Latency Test v4.4.1 # Size Avg Latency(us)
26 MPI µbenchmark [result] 5 4 latency [us] Message Size (KB) native cos7 cos6 u
27 MPI µbenchmark [result #2] 2,8 2,1 native cos7 cos6 u12 latency [us] 1,4 0,7 0 gcc gcc gcc gcc ompi ompi ompi ompi distribution
28 HPCG Benchmark mimics thermodynamic application workload LINPACK corrective / successor in the long-term? 28
29 HPCG - Distributions Results 6 5,25 GFLOP/s 4,5 3,75 3 native cos7 cos6 u12 CentOS 7.0 ompi gcc CentOS 6.5 ompi gcc Ubuntu ompi
30 HPCG - Overall Results 6 5,25 native cos7 cos6 u12 GFLOP/s 4,5 3,75 3 gcc gcc gcc gcc ompi ompi ompi ompi distribution
31 Conclusion High potential within end-user application Build once Run anywhere Abstracted user-land enables fine tuning, regardless of bare-metal system But: Will the result be equivalent? 31
32 Motivation Bundled up applications sounds good but how about the consistency of the results? The Container communicates w/ the hosts Kernel results should be fairly stable, since the syscall interface is quite mature 32
33 Test Bed Laptop (VirtualBox) MacBook Pro, Intel Core 16GB RAM, 512GB SSD Workstation AMD Phenom II X4 955 (3.2GHz), 4GB RAM, 64GB SSD HPC Cluster 8x SUN FIRE X2250, each 2x Intel XEON X5472, 32GB RAM, QDR InfiniBand AWS EC2 Public Cloud (XEN) Compute instance c3.xlarge, Intel XEON E5-2680, 7.5 GB RAM, SSD Storage Compute instance c4.4xlarge, Intel XEON E5-2666v3, 30 GB RAM, SSD Storage 33
34 Use-case OpenFOAM tutorial cavity 3 solid walls, 1 moving lid 34
35 Test Case setting Changes done to increase computational effort Mesh size increased from 20x20x1 to 1000x1000x1000 streched vertices dimensions by factor 10 Iterates 50 times with step-with 0.1ms decomposition into 16 cells decompose && mpirun -np16 icofoam parallel 2>&1 > log Each iteration outputs the average cell pressure Sum of the pressure leads to measure of consistency 35
36 OpenFOAM Containers Immutable Application Containers u1204of222: Ubuntu & OpenFOAM u1204of230: Ubuntu & OpenFOAM u1410of231: Ubuntu & OpenFOAM
37 Host Systems MacBook boot2docker (1.4.0), CoreOS 618, 3.19 Workstation CentOS6.6, v Ubuntu12.04, & HPC Cluster venus CentOS7.0alpha, AWS EC2 Ubuntu14.04, CoreOS 494, CoreOS 618, 3.19 Ubuntu14.10, &
38 Results Pressure remains the same among minor releases OpenFOAM 2.2.2: p OpenFOAM 2.3.0/2.3.1: p Runtime varies a lot MacBook Workstation 1xHPC.IB HPC.shm c3.xlarge 2xHPC.IB c4.4xlarge 4xHPC.IB 8xHPC.IB
39 Results [cont] A paper describing the study can be found 39
40 Future Work Cavity iterates towards a deterministic result Use Karman Vortex Street (chaotic, sensitive to random events) Stable behaviour of the user-land within Immutable Container? Diversify Use-Cases EndUser Applications: e.g. EAGER Pipeline (Uni Tübingen) If you got ideas / use-cases -> talk to me How about GPGPU, MICs? The abstraction of Docker should only cover the same architecture 40
41 Conclusion Containers are stable across multiple Harde-ware versions Operating Systems Kernel versions Network Technology (SHM, InfiniBand) Keep images and input data could be enough Vendors are able to fine-tune and certify Distribution vendors Software vendors 41
42 Thank you for your attention Picture Credits p5/6: p7: p25: p32: p38: p40: 42
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