System input-output, performance aspects March 2009 Guy Chesnot
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1 Headline in Arial Bold 30pt System input-output, performance aspects March 2009 Guy Chesnot
2 Agenda Data sharing Evolution & current tendencies Performances: obstacles Performances: some results and good practices
3 A file system taxonomy Local File System - The generic term for a non-shared file system Examples: XFS, EXT2, EXT3, NTFS, FAT, Distributed File System - The generic term for a client/server or "network" file system where the data is not locally attached to a host. Network File System (NFS) is the most common distributed file system currently in use for Open Systems. Storage Area Network (SAN) File System Provides a means for hosts to share Fiber Channel storage. Examples include: CXFS, IBM s General Parallel File System (GPFS) and Quantum s StorNext File System Parallel File System Meaning a transport by many servers, towards many clients. Examples: pnfs (in the future), Lustre (SUN), Panasas (Panasas)
4 Agenda Data sharing Evolution & current tendencies Good old times! Ups & downs File systems: local, distributed, shared, parallel Performances: obstacles Performances: some results and good practices
5 Good old times? Mainframe: centralized computing No network All data is shared Groups of workstations Local network(s) Some data is shared
6 Ups: Performance In the old way, peak performance was related to a limited number of disks New obstacles appeared Operating system (kernel + local file system manager) Hosts servers Aggregate I/O requests (by our customers) In the old times, Linux 2.4 kernel, below 300 MB/s Current times, Linux 2.6 kernel, over 5 GB/s
7 Ups Parallelism on servers and PCs: multi cores processors Memory size Faster than disk transfer rate > more disks pour to fill memory at the same rate Disk unit capacity Faster than disk transfer rate > less disks for the same data capacity > need to grow the aggregate dik unit transfer rate Volume of data (downfall*) * Naming by IN2P3
8 From distributed to SAN file systems SERVEUR CLIENT CLIENT CLIENT SAN CLIENT CLIENT SAN CLIENT SERVER Asymmetric Distributed shared file system file system Usual server is replaced by a metadata manager Symmetric shared file system Each client is in charge of consistency check.
9 Parallel file system CLIENT CLIENT CLIENT CLIENT CLIENT CLIENT CLIENT CLIENT CLIENT CLIENT CLIENT CLIENT CLIENT CLIENT CLIENT CLIENT Metadata Data SERVER SERVER
10 Lustre, Panasas: a common design Objects Data stored as object Metadata server handles objects instead of blocks Global namespace Parallelism, data access through high performance LANs => goal is linear scalability for capacity and performance Centralized Architecture : for one data center = one file system + one architecture Redundancies to ensure availability in case of hardware failures Better use own client-server protocol
11 Lustre, Panasas: some differences Lustre: Open Source model Software only Hardware (storage, servers) agnostic Panasas: Appliance model Cluster of metadata servers (metadata scalability) Cluster also for storage (data scalability) Built-in redundancies Integration of protocols (network, storage) Different networks of choice What kind of managed clients in the future?
12 Agenda Data sharing Evolution & current tendencies Performances: obstacles Performances: some results and good practices
13 Numbers of clients Tens Distributed, SAN, Parallel file systems Hundreds Distributed, SAN (some), Parallel file systems Thousands Only Parallel file systems can reach these figures
14 What kind of clients? Distributed (NAS: NFS, CIFS): universal access SAN file systems; depending upon architectures GPFS: limited CXFS: large span (UNIXs, LINUXs, Windows s, Mac) Parallel file systems Limited Waiting for pnfs (Godot?)
15 Number of files Scanning hierarchy tree with numerous files Solutions Same external interface Different internal architecture XFS local file system Objects
16 Variable file size Usual data handling : blocs Object is now more popular Hardware level: more clever disks Software level: pnfs, Lustre, Panasas
17 Links congestion Data and metadata on same link Cars and bicycles on the highway Solution Third party transfer, also called out-of-band or split-path Different paths for metadata (requests, status) and data Server initiated data transfer Direct transfer between host and storage Example: Tape library Data path Host Control path
18 Disk technology Better reliability, form factor decrease Cost per Gigabyte Density, doubling every three years (average) Hardly better Transfer rate Access time (30% in 10 years) => lesser than CPU power Solution Parallelize the access Parallel file system
19 Performance scalability limit Distributed file system One server only Data and metadata management on same host > limited number of clients Solutions Clustered NAS = SAN file system Parallel file system
20 Clustered NAS at SGI Client NAS Client NAS Client NAS Client NAS Client NAS Client NAS Client NAS NAS Head Client CXFS Client CXFS Client CXFS Client CXFS Server CXFS SAN Server CXFS failover
21 Performance scalability limit (cont. 1) SAN file system No user mobility On one SAN On a limited number of OS Asymmetric: limited number of metadata servers Wide client heterogeneous Os s Symmetric: more metadata servers Few client heterogeneous Os s Not so good for export via NFS towards light clients Data and metadata management on same hosts Solution Numerous SAN, integrated by a parallel file system
22 Performance scalability limit (cont. 2) Parallel file system Metadata parallelism Solution : possible, product architecture is not an obstacle Cache consistency improvement Solution : nothing new in pnfs until now Higher complexity pnfs Layouts management Solution: Lustre, Panasas (one kind of layout)
23 pnfs: Layouts One common client for different storage back ends client retrieves a layout from the metadata server layout maps file to storage devices and network addresses client uses layout for I/O SERVER layout 1. SBC (blocks) 2. OSD (objects) 3. NFS (files) 4. PVFS2 (files) 5. Future backend Application client Client pnfs Layout Driver
24 Performance scalability limit (cont. 3) Parallel file system Metadata parallelism Solution : possible, product architecture is not an obstacle Cache consistency improvement Solution : nothing new in pnfs until now Higher complexity pnfs Layouts management Solution: Lustre, Panasas (one kind of layout) Protocol stacking Solution: nothing, this is a cost for scalability; besides Faster networks and NIC s Faster disks and servers
25 Application Bibliothèque d E/S Gestion vnodes Client pnfs Pilote layout Noyau Pilote réseau Liens réseau network Application Bibliothèque d E/S Client de système de fichiers partagés Noyau Pilote réseau Liens réseau Local file system Distributed file system SAN file system Parallel file system network Liens réseau: Ethernet, IB, Noyau Application Bibliothèque d E/S network network Cartes d interface réseau Gestionnaire de système de fichiers partagés Pilote réseau Gestionnaire de système de fichiers IP Gestionnaire de cache mémoire UDP / TCP / RDMA Gestionnaire de volumes logiques XDR Pilote disque RPC Cartes d interface (SCSI, iscsi, IB) NFS v2/3 / pnfs Lien / Fabrique Démons NFS Contrôleur de disques (RAID, CTQ..) Disque
26 Agenda Data sharing Evolution & current tendencies Performances: obstacles Performances: some results and good practices
27 Some performances on SGI systems Enhanced version of NFSv3 Ethernet based: Scaling to 16 CPU, 16 GigEthernet ports, and 1.8 GB/sec IPoIB and NFS RDMA read: up to 3.7 GB/s Write: up to 1.4 GB/s SAN file system Demonstrated to >45GB/sec Parallel file system Panasas: Scales to > 10 GB/second Lustre: Scales to > 50 GB/second
28 Parallel I/O Requires Parallel Storage: example FLUENT 12 Panasas and ANSYS Alliance Has Produced Parallel I/O for FLUENT 12 Serial I/O Scheme Parallel I/O Scheme 12 FLUENT 12: Offers support for Panasas Commercial Release in April 09 Source: Barb Hutchings Presentation at SC07, Nov 2007, Reno, NV
29 Single Job: Scalability of Solver + Data File Write Time (Seconds) of Solver + Data File Write FLUENT Comparison of Panasas vs. NFS on University of Cambridge Cluster Unsteady external aero for 111 MM cell truck; 5 time steps with 100 iterations, and a single.dat file write x Time of Solver + Data File Write x 1.7x 2.0x 1318 Lower is better x FLUENT 12/Panasas FLUENT 6.3/NFS NOTE: Growing PanFS advantage with more cores 1.5x 2.3x 1790 NFS NFS NFS Truck Aero 111M Cells NOTE: Read times are not included in these results Number of Cores
30 Single Job: Computational Profile of Solver vs. Write FLUENT Comparison of Panasas vs. NFS on University of Cambridge Cluster Unsteady external aero for 111 MM cell truck; 5 time steps with 100 iterations, and a single.dat file write Time (Seconds) of Solver + Data File Write Job Profiles of Solver % -- Write % 1.8x % 3% Solver NOTE: Solver times about same for both as expected % Solver Data Write 56% 2.0x % Solver Lower is better 4% % Write 51% Solver Number of Cores NFS Write FLUENT 6.3 NFS Solver FLUENT 6.3 Panasas Write FLUENT 12 Panasas Solver FLUENT 12 NOTE: About 50% of NFS time spent writing data 2.3x % 8% % 50% Truck Aero 111M Cells NOTE: Read times are not included in these results
31 Whenever the source code cannot be changed Tuning of the I/O infrastructure (network included) Tuning at library level MPI-IO Application level memory Caches (the most efficient): FFIO at SGI Better use proprietary client of the Parallel file system rather than a standard, poorly implemented Lustre DirectFlow (Panasas) No NFS! Lower latency Higher scalability
32 Whenever the source code cannot be changed : caches System System System user space user space user space All files Cache per "file system" Dynamic window per file file system file system file system
33 Whenever the source code can be changed Parallelize I/O operations Can be directed to all physical storage (OSS inside Lustre, StorageBlades inside Panasas) Simultaneously Independently File and i/o sizes Higher is better (8 MB is optimal) One i/o on a parallel file system ~= 100 ms (without cache) Whether the i/o is 4 kb or 8 MB Do not share! Number of clients per storage entity Best ratio optimal is one client per entity (StorageBlade in Panasas, in Lustre, a disk, a RAID group ) Too many clients for one entity overload the entity Too many entities for one client is not better Parallel file system = network configuration Aim aggregate performance, do not bother with performance per client
34 Prospective future
35 Thank you for your attention 2008 SGI. All rights reserved. SGI, Altix and the SGI logo are registered trademarks and Innovation for Results is a trademarks of SGI in the U.S. and/or other countries worldwide. All other trademarks mentioned herein are the property of their respective owners.
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