Lustre overview and roadmap to Exascale computing
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2 HPC Advisory Council China Workshop Jinan China, October 26th 2011 Lustre overview and roadmap to Exascale computing Liang Zhen Whamcloud, Inc
3 Agenda Lustre technology overview Lustre roadmap Exascale evolution 3
4 What is Lustre? Open Source (GPL v2) A Distributed Filesystem A Cluster Filesystem A POSIX Filesystem A large scale filesystem A High Performance Filesystem Hardware agnostic 4
5 Scalability and Performance Large Scale File size not limited by storage partition size One file can be up to 320TB today, larger soon Huge numbers of clients High Performance Lustre used by: 8 of Top 10 supercomputing sites, 70 of top 100 Lustre has won many bandwidth challenges Well tuned small systems can beat NFS for some workloads Full cache coherency, no cache expiry Metadata speed: > 20k/sec simple file creates with proper network, faster in recent future 5
6 Distributed Filesystem Lustre aggregates multiple servers into a single filesystem namespace The namespace is then exported to clients across a network Client/Server design Servers can have client processes Modular storage can be added on the fly WAN many groups use Lustre over at least a campus distance 6
7 The Big Picture
8 Server View of Filesystem Servers have targets Targets storage + filesystem use standard block devices Backend filesytem Ldiskfs is based on ext3/ext4 with additional features On-disk file layout Lustre MDT stores the directory tree MDT file inodes do not contain file data, only metadata plus Extended Attributes (EAs) The EA holds a list of tuples Each tuple represents an object on an OST (stripe or complete file) Tuple contains OST index: Object id Object ID is inode number, FID in 2.x 8
9 Client View of the Filesystem Clients do not require direct access to storage Client sees one namespace Client sees Logical Object Volume (LOV) The LOV hides details of file data layout Simple allocation of resources to users Lustre clients can re-export filesystem NFS export via knfsd from kernel CIFS export via Samba from userspace POSIX semantics Few exceptions (distributed atime updates too costly) 9
10 Basic Lustre IO Operation File open Clients LOV Directory Operations, file open/close metadata, and concurrency File I/O and file locking MDS Recovery, file status and file creation OSS 10
11 Scalable filesystem (1/2) Distributed File objects are distributed across storage server for both load balancing and bandwidth OSSs are completely independent MDS only hold metadata All configuration owned by MGS Performance / capacity grows nearly linearly with hardware Expandable Storage can be added to a live filesystem 11
12 Scalable filesystem (2/2) Distributed Locking Provide POSIX semantics Fairly complex locking, reasonable scaling Lock revocation flushes data from client cache Timeouts It is necessary to have an upper bound on transaction time adaptive timeouts RPC clients can request timeout extension if they are making progress with a request Object-Oriented Design Network, on-disk filesystem abstracted from core Lustre (LNET, ldiskfs, etc) File data is stored as one or more objects much of the state in both clients and servers is maintained and manipulated as objects 12
13 Lustre Networking LNet is a message passing library Async message Scalable and high performance connectionless Includes zero-copy libraries, RDMA support 95%+ of raw bandwidth Layered software module LNet & LND (Lustre Network Driver) LND support different network stacks: tcp/ip, infiniband Routing Simultaneous availability of multiple network types 13
14 Whamcloud Lustre Roadmap (1/2) Maintenance Releases every quarter /2.1.1 RHEL6 client support 24TB ext4 LUNs Feature Releases Full RHEL6 support Async journal commits 128TB ext4 LUNs Stability enhancements Imperative Recovery Dirop SMP Scaling Wide Striping Server Stack SMP Scaling Statahead performance Online check/scrub OSD restructuring Distributed namespace HSM Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q Berlin Buzzwords 14
15 Whamcloud Lustre Roadmap (2/2) 1.8.x and 2.x 1.8.x stable and wildly used 2.x is built on a new architectural code base 2.x roadmap Imperative recovery Stat performance improvements Single MDS performance improvements Distributed Namespace NRS Wide striping OSD restructuring 15
16 Exascale computing Exascale computing OPS/sec, millions of nodes Seymour Cray once said a supercomputer is a device for turning compute-bound problems into I/O-bound problems Current architecture storage is completely segregated from the compute resources and are connected via a network interconnect Current architecture has persisted as we scaled from gigascales to petascales 16
17 Exascale I/O technology drivers Hardware Node Concurrency: O(1,000) Memory bandwidth: ~500GB/s System Compute nodes : O(1,000,000) Total concurrency: O(1,000,000,000) Total memory: ~10PB Total storage: ~500PB Total I/O bandwidth: ~50TB/s Software (Meta)data explosion Billions of entities Complex relationships OODB Query / search Schemas / Inheritance Storage management Migration / Archive 17
18 Software engineering Stabilization effort required non-trivial Expensive/scarce scale development and test resources Build on existing components when possible LNET (network abstraction), OSD API (backend storage abstraction) Implement new subsystems when required Distributed Application Object Storage (DAOS) Layered model Core features Simple but powerful Middleware Puts complexity where it s easiest to debug Supports different application domains Tools Generic data management and administration Application domain specific browsers and query engines 18
19 Exascale filesystem Conventional namespace Works at human scale Administration, security, accounting Supports legacy data and applications DAOS Containers Works at exascale Embedded in conventional namespace Provides storage for application domain specific object schemas (data + metadata) Storage pools Administration and accounting 19
20 Software stack DAOS containers Application data and metadata Object resilience Data management DAOS API Userspace export of DAOS functionality Userspace Application domain libraries Domain-specific API style Implementation policy Applications and tools Backup and restore Query, search and analysis 20
21 Core components development plan Unified storage pools Combine MDT/OST functionality, administer by usage Health network Timely failure notification & server collectives Much much more scalable Userspace DLM API Locking support for schema implementations DAOS Sharded object index Accounting stats aggregation Collective open Collection merge/split/duplicate/migrate Userspace object collection API 21
22 Thank You Whamcloud, Inc.
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