HPC File Systems and Storage. Irena Johnson University of Notre Dame Center for Research Computing

Transcription

1 HPC File Systems and Storage Irena Johnson University of Notre Dame Center for Research Computing

2 HPC (High Performance Computing) Aggregating computer power for higher performance than that of a typical desktop computer/workstation for solving large problems in science, engineering, business Large systems perform calculations Data access is critical HPC: Compute node Head node File System Storage Networking

3 File Collection of data/information: Document Picture Audio or video stream Application Other collection of data

4 Metadata The information that describes the data contained in files: Size Date created Date modified Location on disk Permissions (who can view/modify your file)

5 File System Definition from TLDP (The Linux Documentation Project): "On a UNIX system, everything is a file; if something is not a file, it is a process*. Many Types of File Systems Not all file systems are equal Designed for different uses Data is organized in different ways Some are faster than others Some are more robust/reliable Some support large storage drives *Process - a task (a process is started when a program is initiated)

6 UNIX/Linux File System Hierarchical file structure Tree-structured file system (upside down tree) Everything starts from the root directory / and expands into sub-directories and so forth Unlike Windows which uses drives

7 UNIX/Linux File System / /bin/ /boot/ /dev/ /etc/ /home/ /lib/ /media/ /mnt/ /opt/ /proc/ /bea/ /ed/ /jen/

8 UNIX/Linux File System Serial File System (Traditional) A single server controls the users and data Can be faster for one user No redundancy Simple

9 UNIX/Linux File System Distributed / Parallel File System Data is spread out across many systems on a network Single shared global namespace Supports multiple users (can be distributed) Supports high bandwidth More storage than on a single system Fault tolerant Reliable Scalable Complex

10 Parallel File System Clients Clients Clients Metadata Parallel Read/Write Metadata servers Metadata servers Management Storage Devices

11 Parallel File System Breaks up a data set and distributes (stripes), the blocks to multiple storage drives (local and/or remote servers). Users do not need to know the physical location of the data blocks to retrieve a file. Data access is done via a global namespace. A metadata server stores the file name, location, owner, access permissions. Reads and writes data to distributed storage devices using multiple I/O paths concurrently. Capacity and bandwidth can be scaled. Storage - high availability, mirroring, replication, snapshots.

12 File Systems at CRC AFS (Andrew File System) Developed in 1982, part of the Andrew project at Carnegie Mellon University. Named after Andrew Carnegie and Andrew Mellon Client-server architecture Federated file sharing Provides location independence Scalable Secure (Kerberos for authentication and ACL - access control lists on directories for users and groups) Available for a wide range of heterogeneous systems - UNIX/ Linux, MacOS X, and Microsoft Windows

13 File Systems at CRC Panasas - High Performance Parallel scratch File System /scratch365 Parallel access to data Data is striped across multiple storage nodes, providing increased capacity and/or performance Concurrent reading and writing (scalable performance to individual files) Global Namespace - all compute nodes accessing the storage see the same namespace (same name and pathname); management is done through one system only

14 Overview CRC File Systems Purpose File System Type, Full Name File Access Space Available Aggregated bandwidth (approx.) Globally accessible home and project directories User s Home Directories AFS - crc.nd.edu /afs/crc.nd.edu/user/first/netid Group Directories AFS crc.nd.edu /afs/crc.nd.edu/group/ Pseudo-temporary File System Directly using OpenAFS client (open source) $HOME Directly using OpenAFS client 100GB - 2TB volume 100GB - 2TB volume up to MB/sec per node - Approximately 200 MB/sec aggregated using multiple nodes Panasas High Performance Parallel scratch file system /scratch365/netid Directly using Panasas proprietary pants client 500GB - 1TB MB/sec per node with 1 Gb network Local File Systems Node local temporary scratch file system Local disks /scratch (link to /tmp) Directly - shared with other users on node R GB HP DL160 - d6copt - 100GB IBM/Lenovo nx360m4-400gb Daccssfe - 5TB R815-H700 RAIDctrl MB/sec HP DL160 -d6copt MB/sec IBM/Lenovo MB/sec daccssfe ,000 MB/sec

15 RAID Redundant Array(s) of Inexpensive/Independent Disks Physical disks bound together with hardware or software Used to create larger filesystems out of standard drive technology Configurations optimize cost vs capability RAID Levels: 0, 1, 3, 4, 5, 6, 0+1, 1+0 RAID 0 - striped (performance and capacity) RAID 1 - mirrored (read performance, fault tolerance FT) RAID 5 - striped with distributed parity (performance, capacity, FT N+1) RAID 6 - striped with distributed parity (performance, capacity, FT N+2)

16 Data Storage How information is kept in a digital format that may be retrieved later Computers/Laptops/Tablets/Smartphones/other devices - all store data Hard drive/disk/flash drive/ssd (solid state data)/cloud Is not the same as RAM memory * Hard drive - think long term memory * RAM - think short term memory

17 Data Storage Types File-based storage Block-based storage Object-based storage

18 File Storage Also called file-level or file-based storage You use file storage when you access documents/pictures saved in files on your computer Data is stored as a single piece of information inside a file, inside a directory A single path to data Hierarchical in nature (called tree-structured system) Oldest type of storage Inexpensive Simple

19 Block Storage Breaks a file into individual blocks of data The blocks are stored as separate pieces of data No need for file-folder structure because each block of data has a unique address The smaller blocks of data spread out to where is most efficient The storage system software pulls all the blocks back together to assemble the file when accessed The more data you need to store, the better

20 Block Storage Used in storage-area network (SAN) environments where data is stored in volumes (blocks) Data is divided into blocks (can be different sizes) which are stored separately on hard drive(s) Consistent I/O performance, low latency connectivity More expensive, complex Good for data that has to be frequently accessed and updated Usage examples: database storage; applications like Java

21 Object Storage Also called object-based storage Files are broken into units called objects and spread out among hardware The objects are kept in a single repository, instead of being kept as files in directories or as blocks on servers The blocks of data that make up a file, the metadata is kept into a storage pool Unique identifier assigned to the object Cost efficient: you only pay for what you use Usage examples: big data, web applications, backup archives Good for data that doesn t need to be modified (just READ)

22 File/Block/Object Storage Comparison File-based storage Block-based storage Object-based storage Transaction units Files Blocks Objects Protocols CIFS, NFS SCSI, FiberChannel, SATA Metadata File-system attributes Recommended for Shared file data Strength Simplified access and management of shared files File-system attributes Transactional data, frequently changing data High performance Web services (XMLbased messaging) Custom metadata Static file data, cloud storage Scalable, distributed access

23 SAN (Storage Area Network) dedicated high-speed network that interconnects and shares pools of storage devices to multiple servers each server accesses the shared storage as if it were directly attached to it raw storage is treated as a pool of resources which can be centrally managed and allocated highly scalable - capacity can be added as needed disadvantages: cost and complexity Clients Clients Clients Network Storage Network Metadata server

24 NAS (Network Attached Storage) dedicated file storage device that provides nodes within same network file-based storage via Ethernet connection storage appliance, connected to a network switch reliable, flexible highly scalable network storage speed Clients Clients Clients NAS Storage Network Network

25 Panasas - object-based storage cluster Performance improves with scale - linear scalability Data protection improves with scale Scalable storage - easy to access, deploy and manage

26 Panasas - ActiveStor Parallel scale-out NAS storage appliance Complete hardware and software storage solution Implements: Parallel, object-based filesystem Global namespace Strict client cache coherency Network Attached Storage (NAS) - Panasas DirectFlow (pnfs, CIFS, NFS) - rpm package for Linux (also MAC OS supported) Scale-out NAS - serving parallel access to data (data is striped across multiple storage nodes, providing increased capacity and/or performance) Parallel File System - concurrent reading and writing (data for a single file is striped across multiple storage nodes to provide scalable performance to individual files) Global Namespace - all compute nodes accessing the storage see the same namespace (same name and pathname); management is done through 1 system only

27 Panasas 1 node of Panasas architecture ActiveStor16 Hybrid - Storage media is a combination of HDD and SDD: - Hard Drives - for larger files - SSD (Flash) - for small files or metadata (no moving parts - solid state) Scalable data solution Redundant power modules - two Redundant battery module (power backup) Redundant switch modules (connected to the network, provide access to storage) Storage blade: 2 HDD, 1 Flash, CPU, RAM Mem Director blade: serves File system metadata, legacy protocol (NFS, CIFS) 1 shelf = 1 Director Blade + 10 Storage Blades

28 Panasas Storage blade: 2 HDD, 1 Flash, CPU, RAM Mem Director blade 1. Serves File System metadata (file s location, owner, permissions, size of file) 2. The gateway to the storage for standard legacy protocols (NFS, CIFS) DirectFlow - direct access between compute clients and storage

29 ActiveStor16-7 Shelves: - 2 Director Blade per shelf - 9 Storage Blades per shelf Panasas at CRC Total Director Blades: 14 Total Storage Blades: 63 Total Capacity TB Data space Used TB Metadata Used 8.59 TB Free Space TB