Feedback on Big Compute & HPC on Windows Azure
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Is the cloud relevant for high performance workloads ? We answer by sharing our experience : HPC consultants at ANEO have ported and optimized a distributed scientific software developed at Supelec, from their Linux cluster to Microsoft's new cloud technology, Big Compute (InfiniBand nodes interconnect).
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Feedback on Big Compute & HPC on Windows Azure
- 2. Feedback on Big Compute & HPC on Windows Azure Antoine Poliakov HPC Consultant ANEO [email protected] http://blog.aneo.eu Innovation Recherche
- 3. Introduction HPC : a challenge for the cloud • Cloud : on-demand access through a telecommunications network to shared and userconfigurable IT resources • HPC (High Performance Computing) : a branch of computer science conercned with maximizing software efficiency, in particular in terms of execution speed – – – Raw computing power doubles every 1.5 - 2 years Network throughput doubles every 2 - 3 years The compute/network gap doubles every 5 years • HPC in the cloud allows makes computing power accessible to all (SME, research labs, etc.) Fosters innovation • Our question : can the cloud offer sufficient performances for HPC workloads ? – – – #mstechdays CPU : 100% native speed RAM: 99% native speed Network ??? #3 Innovation Recherche
- 4. Introduction 3 ingredients yield an answer through experimentation Technology HPC oriented cloud Use-case HPC software Experiments State of the art of HPC in the cloud #mstechdays #4 Innovation Recherche
- 5. Introduction Experimenting on HPC in the cloud : our approach Identify technologies and partners • HPC software use-case • Efficient cloud computing service Port the applicative HPC code : cluster cloud • Skills improvements • Feedback on the technologies Experiment and measure performances • Scaling • Data transfers #mstechdays #5 Innovation Recherche
- 6. Introduction A collaborative project with 3 complementary actors Consulting firm: organization and technologies HPC Practice: fast/massive information processing for finance and industries Established HPC research teams: Distributed software & big data Machine learning and interactive systems Windows Azure provides a cloud solution aimed at HPC workloads: Azure Big Compute Goals Identify most relevent use-cases for our clients Estimate the complexity of porting and deploying an app Evaluate if the solution is production-ready Goals Is the cloud ready for scientific computing ? Specificities of deploying in the cloud ? Performances Goals Pre-release feedback Inside view of a HPC cluster cloud transition #mstechdays #6 Innovation Recherche
- 7. Introduction Dedicated and competant teams: thank you all! Consulting Ported and deployed the application in the cloud Led the benchmarks Constantinos Makassikis HPC Consultant Research Use-case: distributed audio segmentation Experiments analysis Stéphane Vialle Professor, Computer science Antoine Poliakov HPC Consultant Stéphane Rossignol Assistant Professor, Signal processing Wilfried Kirschenmann HPC Consultant Kévin Dehlinger Computer scientist intern CNAM #mstechdays #7 Provider Created the technical solution Made available notable computational power Innovation Recherche Xavier Pillons Principal Program Manager, Windows Azure CAT
- 8. Presentation contents 1. Technical context 2. Feedback on porting the application 3. Optimizations 4. Results #mstechdays #8 Innovation Recherche
- 9. 1. TECHNICAL CONTEXT a. Azure Big Compute b. ParSon #mstechdays #9 Innovation Recherche
- 10. Azure Big Compute Azure Big Compute = New Azure nodes + HPC Pack New nodes: A8 and A9 • • • • 2x8 snb E5-2670 @2.6Ghz, 112Gb DDR3 @1.6Ghz InfiniBand (network direct @40Gbit/s): RDMA via MS-MPI @3.5Gb/s, 3µs IP over Ethernet @10Gbit/s ; HDD 2Tb @250Mo/s Azure hypervisor HPC Pack • Task scheduler middleware: Cluster Manager + SDK • Tested with 50k cores in Azure • Free Extension Pack : any Windows Server install can be a node #mstechdays #10 Innovation Recherche
- 11. Azure Big Compute HPC Pack : on permise cluster • • #mstechdays N N N N N N N N Administration : hardware + software N N M N Cluster dimensioned w.r.t. maximal workload • AD Active Directory, Manager and nodes in a privately managed infrastructure N #11 Innovation Recherche
- 12. Azure Big Compute HPC Pack : in the Azure Big Compute cloud • Active Directory and manager in the cloud (VMs) • Nodes allocation and pricing on demand • Admin : software only PaaS nodes IaaS VM Remote desktop/CLI #mstechdays #12 M Innovation Recherche N N N N N AD N N N N N N N
- 13. Azure Big Compute HPC Pack : hybrid deployment • Active Directory and manager on premise • Nodes both in the datacenter and in the cloud • Local dimensioning w.r.t. average load Dynamic cloud dimensioning: absorbs peaks • Admin: software + hardware N N N N N N N N N N #13 VPN M Innovation Recherche N N N N N AD N N N N #mstechdays N N N N N
- 14. ParSon ParSon: an audio segmentation scientific software • ParSon = audio segmentation algorithm : voice / music 1. Supervised training on known audio samples to calibrate the classifier 2. Classification based on spectral analysis (FFT) on sliding windows Digital audio ParSon Segmentation and classification #mstechdays #14 Innovation Recherche voice music
- 15. ParSon ParSon is distributed with OpenMP + MPI 6. Get outputs Data Control 4. MPI Exec 2. Reserves N computers OAR 5. Tasks with heavy intercommunications 1. Upload input files NAS #mstechdays #15 3. Input deployment Reserved computers Innovation Recherche Linux cluster
- 16. ParSon Performances are limited by data transfers Best runtime (s) 2048 512 128 IO bound 32 Nodes read from NAS en réseau, à froid Nodes read froid en local, à locally 8 1 4 16 Number of nodes #mstechdays #16 Innovation Recherche 64 256
- 17. 2. PORTING THE APPLICATION a. Porting C++ code: Linux Windows b. Porting distribution strategy: Cluster HPC Cluster Manager c. Porting and adapting deployment scripts #mstechdays #17 Innovation Recherche
- 18. Standards conformance = easy Linux Windows porting • ParSon and Visual conform to the C++ standard few code changes • Dependencies are the standard libraries and cross-platform scientific libraries : libsnd, fftw • Thanks to MS-MPI, inter-process communication code doesn’t change • Visual Studio natively supports OpenMP • The only task left was translating build files: Makefiles Visual C++ projects #mstechdays #18 Innovation Recherche Porting
- 19. Porting ParSon in the cluster 6. Get output Data Control 4. MPI Exec 2. Reserves N computers OAR 5. Run and inter-com. 1. Upload input file NAS #mstechdays #19 3. Input deployment Reserved computers Innovation Recherche Linux cluster
- 20. Porting ParSon dans le Cloud Azure 6. Get output IaaS PaaS 4. MPI Exec 2. Reserves N nodes HPC Cluster Manager AD Domain controller 5. Run and inter-com. 1. Upload input file HPC pack SDK Azure Storage #mstechdays #20 3. Input deployment Provisioned A9 nodes Innovation Recherche PaaS Big Compute Data Control
- 21. Porting Deployment within Azure At every software update : package + send in the cloud 1. Send to manager – – Either with Azure Storage Set-AzureStorageBlobContent Get-AzureStorageBlobContent hpcpack create ; hpcpack upload hpcpack download Or with normal transfert : internet accessible fileserver : FileZilla, etc. 2. Packaging script: mkdir, copy, etc. ; hpcpack create 3. Send to Azure storage: hpcpack upload At every node provisioning : local copy 1. Remotely execute on nodes from the manager with clusrun 2. hpcpack download 3. powershell -command "Set-ExecutionPolicy RemoteSigned" Invoke-Command -FilePath … -Credential … Start-Process powershell -Verb runAs -ArgumentList … 4. Installation : %deployedPath%deployScript.ps1 #mstechdays #21 Innovation Recherche
- 22. Porting This first working setup has some limitations • Transferring the input file is longer than sequential computation on a single thread • On many cores, computation times is negligible compared to transfers • WAV format headers and ParSon code limit input size to 4Gb #mstechdays #22 Innovation Recherche
- 24. Optimizations Methodology : suppress the bottleneck Identified bottleneck is the input file transfer 1. Disk write throughput: 300 Mb/s We use a RAMFS 2. Accès Azure Storage : QoS 1.6 Gb/s Download only once from the storage account, then broadcast through InfiniBand 3. Large input files: 60 Gb FLAC c8 lossless compression halves size + not limited to 4Gb Declare all counters as 64 bits ints in C++ code #mstechdays #24 Innovation Recherche
- 25. Optimizations Accelerating local data access with a RAM filesystem • RAMFS = filesystem stored in a RAM block – – • ImDisk – – • Lightweight: driver + service + command line Open-source but signed for Win64 Scripted silent install : – – – – • Very fast Limited capacity, non persistent hpcpack create … rundll32 setupapi.dll,InstallHinfSection DefaultInstall 128 disk.inf Start-Service -inputobject $(get-service -Name imdisk) imdisk.exe -a -t vm -s 30G -m F: -o rw format F: /fs:ntfs /x /q /Y $acl = Get-Acl F: $acl.AddAccessRule(…FileSystemAccessRule("Everyone","Write", …)) Set-Acl F: $acl Run at every node provisioning #mstechdays #25 Innovation Recherche
- 26. Optimizations Accelerating input file deployment • All standard transfer systems go through the Ethernet interface – Azure Storage access via Azure and HPC Pack SDKs – Windows share or CIFS network drive – Standard file transfer protocols: FTP, NFS, etc. • The simplest way to leverage InfiniBand is through MPI 1. On one node: download the input file: Azure RAMFS 2. mpiexec broadcast.exe : 1 process per node • We developped a command line utility in C++ / MPI • If id = 0, reads RAMFS, by 4mb blocs and sends to other nodes through InfiniBand : MPI_Bcast • If id ≠ 0, recieve data blocs and save them on RAMFS • Uses Win32 API: faster than standard library abstractions 3. Input data is in the RAM of all nodes, accessible as a file from the application #mstechdays #26 Innovation Recherche
- 28. Results Computations scale well, especially for bigger files Computation efficiency for different input sizes Computation time (sec, log) Real speedup / ideal speedup Computation time scaling (log-log plot) Number of cores (log) Number of cores (log) #mstechdays #28 Innovation Recherche
- 29. Results Input file transfer make global scaling worse Efficiency for compute only and including transfers Time decomposition, for an hour of input audio + - Real speedup / ideal speedup Time (sec, log) Raw compute Number of cores (log) #mstechdays #29 Number of cores (log) Innovation Recherche
- 30. Broadcast time (sec, log) Download time (min) Consistent storage throughput (220Mb/s), latency may be high Broadcast constant @700 Mb/sAsure storage download performances Broadcast time scaling Number of machines File size (Gb) #mstechdays #30 Innovation Recherche Results
- 32. Our feedback on the Big Compute technology • HPC standards conformance: C++, OpenMP, MPI – • • Nodes administration – Azure storage latency sometimes high – Azure storage limited QoS users must implement multiple account striping – HDDs are slow (for HPC), even on A9 Ported in 10 work days Compute: CPU, RAM Network: InfiniBand between nodes Reactive support – • Data transfers Solid performances – – • • Community, Microsoft – Nodes ↔ Manager transfers must go through Azure storage: less convenient than conventional remote file systems Intuitive user interface – – manage.windowsazure.com HPC Cluster Manager • Everything is scriptable & programmable • Cloud is more flexible than cluster • Unified management of cloud and on-premise #mstechdays #32 • Provisioning time must be taken into account (~7min) Innovation Recherche
- 33. Azure Big Compute for research and business Predictable, pay what you use cost model Modern design, extensive documentation, efficient support Decreased need for administration – but still needed on the software side For research • • Access to compute without any barrier paperwork, finance, etc. • A super computer for all, without investment • Elastic scaling : on-demand sizing Start your workload in minutes • Interoperable with Windows clusters – Cloud absorbs peaks – Best of both worlds • Datacenters in UE : Ireland + Netherlands – • For business For squeezing a few more before the (extended) deadline for that conference Well suited to researchers in distributed computing – Parametric experiments #mstechdays #33 Innovation Recherche
- 34. Thank you for your attention • Antoine Poliakov [email protected] • Stéphane Vialle [email protected] • ANEO http://aneo.eu http://blog.aneo.eu • Retrouvez nous aux TechDays ! Stand ANEO jeudi 11h30 - 13h Au cœur du SI > Infrastructure moderne avec Azure #mstechdays #34 Thanks All our thanks to Microsoft for lending us the nodes ? A question : don’t hesitate! Innovation Recherche