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Disk Directed I/O for MIMD Multiprocessors

Disk Directed I/O for MIMD Multiprocessors. David Kotz Department of Computer Science Dartmouth College. Overview. Introduction Collective I/O Implementation Experiments Results Example Conclusions Questions. Introduction. Scientific applications Traditional I/O Disk directed I/O.

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Disk Directed I/O for MIMD Multiprocessors

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  1. Disk Directed I/O for MIMD Multiprocessors David Kotz Department of Computer Science Dartmouth College

  2. Overview • Introduction • Collective I/O • Implementation • Experiments • Results • Example • Conclusions • Questions

  3. Introduction • Scientific applications • Traditional I/O • Disk directed I/O

  4. MIMD Architecture With CPs and IOPs ( Message Passing)

  5. Collective I/O • Drawbacks of traditional file system • Independent requests • Separate file-system call • Data declustering • Collective I/O Interface • CPs cooperate • Provides a high level interface • Enhances performance

  6. Collective I/O ( Contd..) • Implementation alternatives • Traditional caching • Two phase I/O • Disk directed I/O • Traditional Caching No explicit interface Each application calls IOP • Two phase I/O CPs collectively determine and carry out the optimized approach Data layout must be same in the processors and in the disks

  7. Collective I/O (Contd..) • Traditional Caching

  8. Collective I/O ( Contd.. ) • Two Phase I/O

  9. Collective I/O ( Contd.. ) • Disk-directed I/O

  10. Collective I/O ( Contd.. ) • Disk-directed I/O • The I/O can confirm not only to the logical layout but also to the physical layout. • If the disks are RAIDS, the I/O can be organized to perform full stripe writes for maximum performance. • Only one I/O request to each IOP. • There is no communication among the IOPs • Disk scheduling is improved by sorting the block list for each disk • Two buffers per CP per disk per file

  11. Implementation • Files were striped across all disks , block by block • Each IOP served more than one disks • Message passing and DMA • Each message was encoded • Each request contained reply action • Memget and Memput messages • Proteus simulator on DEC-5000 workstation

  12. Implementation ( Contd.. ) • Simulation parameters

  13. Implementation ( Contd.. ) • Implementing DDIO • IOP creates new thread for each request • Thread computes disk blocks, sorts based on location and informs to disk threads • Allocates two one-block buffers for each local disk • Creates a thread to manage each buffer • Implementing traditional caching • CPs didn’t cache or prefetch data • CPs send concurrent requests to relevant IOPs • Each IOP mainatained double buffer to satisfy requests from each CP to each disk

  14. Experiments • Different configurations • File access patterns • Disk layout • Number of CPs • Number of IOPs • Number of disks • File and disk layout • 10 MB file was striped across disks block by block • Both contiguous and random layouts were used

  15. Experiments ( Contd.. ) • 1D and 2D matrices are used • Access patterns • NONE -each dimension of the array could be mapped to entirely one CP • BLOCK-distributed among CPs in contiguous blocks • CYCLIC-distributed round-robin among the CPs • Record size of 8 bytes and 8192 bytes are used • HPF array distribution

  16. Experiment ( Contd.. ) • Contiguous disk layout

  17. Example • LU Decomposition • In solving linear systems of equations • N x N matrix • Decomposed into L-lower triangular and U-upper triangular • LU=M • Columns are stored in processor’s memory • Each processor’s subset of columns is called “slab”

  18. Example ( Contd.. ) • Performance measurement • 8 CPs, 8 IOPs and 8 disks one for each IOP • 4MB matrix data • Slab size 16, 32 or 128 columns • Random or contiguous layout • Block size 1KB, 4KB or 8KB • Traditional file system used 128 blocks of total cache • Disk directed file system used 16 blocks of total buffer space • Results • DDIO always improved the performance of the LU decomposition when both contiguous and random layouts are used

  19. Related work • PIFS • Data flow is controlled by the file system rather than by the application. • Jovian collective-I/O library • Combines fragmented requests from many CPs into larger requests that can be passed to the IOPs. • Transparent Informed prefetching ( TIP ) • Enables applications to submit detailed hints about their future file activity to the file system, which can use the hints for accurate, aggressive prefetching. • TickerTAIP RAID controller • Uses collaborative execution similar to disk directed file system.

  20. Conclusions • Disk-directed I/O avoided many of the pitfalls inherent in the traditional caching method, such as thrashing, extraneous disk-head movements etc. • Presorts disk requests to optimize head movement and had smaller buffer requirements. • It is most valuable when making large, collective transfers of data between multiple disks and multiple memories.

  21. Questions • What is collective I/O ? • What are the advantages of disk-directed I/O ?

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