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Parallel I/OInternational HPC Summer School

July 11, 2018 Elsa GonsiorowskiHPC I/O Specialist, LLNL

LLNL-PRES-751922This work was performed under the auspices of the U.S. Department of Energy by Lawrence LivermoreNational Laboratory under contract DE-AC52-07NA27344. Lawrence Livermore National Security, LLC

Outline

MotivationI/O in ParallelStep 1: Recognize a needStep 2: Existing I/O Libraries and ToolsStep 3: I/O PatternsStep 4: Understand the File SystemStep 6: Profit

Technical Details: MPI I/OPro-Tips!

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Motivation

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Types of I/O

InputLaunching an executable & it’s linked librariesReading configuration fileLoading data files

OutputCheckpointsResults

ScienceMoving files from onemachine to anotherCleaning up after experiments

Everyone interacts with a file system therefore everyone does I/O!

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Types of I/O

InputLaunching an executable & it’s linked librariesReading configuration fileLoading data files

OutputCheckpointsResults

ScienceMoving files from onemachine to anotherCleaning up after experiments

Everyone interacts with a file system therefore everyone does I/O!

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Types of I/O

InputLaunching an executable & it’s linked librariesReading configuration fileLoading data files

OutputCheckpointsResults

ScienceMoving files from onemachine to anotherCleaning up after experiments

Everyone interacts with a file system therefore everyone does I/O!

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Types of I/O

InputLaunching an executable & it’s linked librariesReading configuration fileLoading data files

OutputCheckpointsResults

ScienceMoving files from onemachine to anotherCleaning up after experiments

Everyone interacts with a file system therefore everyone does I/O!

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Why should I care?

Datamovement is expensive andmust be optimized

Total execution time =Computation time

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Why should I care?

Datamovement is expensive andmust be optimized

Total execution time =Computation time

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Why should I care?

Datamovement is expensive andmust be optimized

Total execution time =Computation time+Communication time

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Why should I care?

Datamovement is expensive andmust be optimized

Total execution time =Computation time+Communication time+I/O time

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HPC Storage Stack

GPUMemory (HBM2): 900GB/s

CPUMemory (DDR4): 120 GB/sNode-local storage or /tmp (SSD): 1.1 GB/sPFS (HDD+ SSD +Magic): 40 GB/s

burst buffer"project" storage"campaign store"

HPSS (Tape + Robots): 0.2 GB/s

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HPC Storage Stack

GPUMemory (HBM2): 900GB/sCPUMemory (DDR4): 120 GB/s

Node-local storage or /tmp (SSD): 1.1 GB/sPFS (HDD+ SSD +Magic): 40 GB/s

burst buffer"project" storage"campaign store"

HPSS (Tape + Robots): 0.2 GB/s

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HPC Storage Stack

GPUMemory (HBM2): 900GB/sCPUMemory (DDR4): 120 GB/sNode-local storage or /tmp (SSD): 1.1 GB/s

PFS (HDD+ SSD +Magic): 40 GB/s

burst buffer"project" storage"campaign store"

HPSS (Tape + Robots): 0.2 GB/s

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HPC Storage Stack

GPUMemory (HBM2): 900GB/sCPUMemory (DDR4): 120 GB/sNode-local storage or /tmp (SSD): 1.1 GB/sPFS (HDD+ SSD +Magic): 40 GB/s

burst buffer"project" storage"campaign store"

HPSS (Tape + Robots): 0.2 GB/s

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HPC Storage Stack

GPUMemory (HBM2): 900GB/sCPUMemory (DDR4): 120 GB/sNode-local storage or /tmp (SSD): 1.1 GB/sPFS (HDD+ SSD +Magic): 40 GB/s

burst buffer

"project" storage"campaign store"

HPSS (Tape + Robots): 0.2 GB/s

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HPC Storage Stack

GPUMemory (HBM2): 900GB/sCPUMemory (DDR4): 120 GB/sNode-local storage or /tmp (SSD): 1.1 GB/sPFS (HDD+ SSD +Magic): 40 GB/s

burst buffer"project" storage

"campaign store"HPSS (Tape + Robots): 0.2 GB/s

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HPC Storage Stack

GPUMemory (HBM2): 900GB/sCPUMemory (DDR4): 120 GB/sNode-local storage or /tmp (SSD): 1.1 GB/sPFS (HDD+ SSD +Magic): 40 GB/s

burst buffer"project" storage"campaign store"

HPSS (Tape + Robots): 0.2 GB/s

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HPC Storage Stack

GPUMemory (HBM2): 900GB/sCPUMemory (DDR4): 120 GB/sNode-local storage or /tmp (SSD): 1.1 GB/sPFS (HDD+ SSD +Magic): 40 GB/s

burst buffer"project" storage"campaign store"

HPSS (Tape + Robots): 0.2 GB/s

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HPC Storage Stack

GPUMemory (HBM2): 900GB/s per GPUCPUMemory (DDR4): 120 GB/s per socketNode-local storage (SSD): 1.1 GB/s per nodePFS (HDD+ SSD +Magic): 40 GB/s shared by a system

burst buffer"project" storage"campaign store"

HPSS (Tape + Robots): 0.2 GB/s shared by a center

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File SystemsLaptop

1 user1.1 GB/s

Network FileSystem (NFS)

m servers, n clientshome directory2 GB/s throughput280K IOPS

Parallel File System(PFS)

Used byHPC jobsSystem specificscratch or project storage40GB/s throughputMillions of IOPS

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Parallel File System

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Parallel File System

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Parallel File System

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I/O in Parallel

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Steps for Dealing with I/O

1. Recognize the need

Get some data out of the applicationGet some data out of the application fasterDeal with files efficiently

2. Investigate I/O libraries and tools, onemay be common inyour field.

3. Implement an I/O pattern4. Understand the file system you are working on5. ???6. Profit!

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Steps for Dealing with I/O

1. Recognize the needGet some data out of the application

Get some data out of the application fasterDeal with files efficiently

2. Investigate I/O libraries and tools, onemay be common inyour field.

3. Implement an I/O pattern4. Understand the file system you are working on5. ???6. Profit!

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Steps for Dealing with I/O

1. Recognize the needGet some data out of the applicationGet some data out of the application faster

Deal with files efficiently2. Investigate I/O libraries and tools, onemay be common inyour field.

3. Implement an I/O pattern4. Understand the file system you are working on5. ???6. Profit!

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Steps for Dealing with I/O

1. Recognize the needGet some data out of the applicationGet some data out of the application fasterDeal with files efficiently

2. Investigate I/O libraries and tools, onemay be common inyour field.

3. Implement an I/O pattern4. Understand the file system you are working on5. ???6. Profit!

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Steps for Dealing with I/O

1. Recognize the needGet some data out of the applicationGet some data out of the application fasterDeal with files efficiently

2. Investigate I/O libraries and tools, onemay be common inyour field.

3. Implement an I/O pattern4. Understand the file system you are working on5. ???6. Profit!

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Steps for Dealing with I/O

1. Recognize the needGet some data out of the applicationGet some data out of the application fasterDeal with files efficiently

2. Investigate I/O libraries and tools, onemay be common inyour field.

3. Implement an I/O pattern

4. Understand the file system you are working on5. ???6. Profit!

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Steps for Dealing with I/O

1. Recognize the needGet some data out of the applicationGet some data out of the application fasterDeal with files efficiently

2. Investigate I/O libraries and tools, onemay be common inyour field.

3. Implement an I/O pattern4. Understand the file system you are working on

5. ???6. Profit!

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Steps for Dealing with I/O

1. Recognize the needGet some data out of the applicationGet some data out of the application fasterDeal with files efficiently

2. Investigate I/O libraries and tools, onemay be common inyour field.

3. Implement an I/O pattern4. Understand the file system you are working on5. ???

6. Profit!

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Steps for Dealing with I/O

1. Recognize the needGet some data out of the applicationGet some data out of the application fasterDeal with files efficiently

2. Investigate I/O libraries and tools, onemay be common inyour field.

3. Implement an I/O pattern4. Understand the file system you are working on5. ???6. Profit!

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Step 1: Recognize a need

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Profiling

DarshanTau

Attend tomorrow’s performance analysis session!

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Profiling

DarshanTau

Attend tomorrow’s performance analysis session!

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Step 2: Existing Libraries + Tools

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Parallel I/O Libraries and Tools

Reading &Writing Files:HDF5PnetCDFOthers: ADIOS, TyphonIO,SILOMPI-IO

Managing Files:SpindlempiFileUtilsSCR

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Library: HDF5

Hierarchical Data FormatFile-system in a fileDatasets: multidimensional arrays of a homogeneous typeGroups: container structures which can hold datasets andother groupsOfficial support for C, C++, Fortran 77, Fortran 90, JavaImplementations in R, Perl, Python, Ruby, Haskell,Mathematica, MATLAB, etc.

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Library: PNetCDF

Built on netCDF andMPI-IOnetCDF:

self-describing, machine-independent formatdesigned for arrays of scientific datanetCDF is implemented in C, C++, Fortran 77, Fortran 90,Java, R, Perl, Python, Ruby, Haskell, Mathematica, MATLAB,etc.

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Library: MPI-IO

API for interacting with files withMPI conceptsblocking vs. non-blockingcollective vs. non-collective

Lower level than other librariesFine-grain control of files and offsetsC and Fortran interfacesSeparate effort from regularMPI

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Tool: Spindle

Scalable dynamic library and Python loadingCaches linked librariesLife saver for NFS issues

https://github.com/hpc/spindle

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Tool: mpiFileUtils

Use parallel processes to perform file operationsExecutedwithin a job allocationdbcast: broadcast a file from PFS to node-local storagedcp: copymultiple file in paralleldrm: delete files in parallelmanymore

https://github.com/hpc/mpifileutils

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Library: SCR

Scalable Checkpoint RestartEnable checkpointing applications totake advantage of system storagehierarchiesEfficient file movement betweenstorage layersData redundancy operations

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Step 3: I/O Patterns

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Parallel I/O Patterns

Single file, accessed by 1 task

Single shared file, accessed by all tasksMany shared files, accessed by groups of tasks

Baton-passingCoordinated "View"

Many independent files, accessed by a subset of tasksOne file per process

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Parallel I/O Patterns

Single file, accessed by 1 taskSingle shared file, accessed by all tasks

Many shared files, accessed by groups of tasks

Baton-passingCoordinated "View"

Many independent files, accessed by a subset of tasksOne file per process

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Parallel I/O Patterns

Single file, accessed by 1 taskSingle shared file, accessed by all tasksMany shared files, accessed by groups of tasks

Baton-passingCoordinated "View"

Many independent files, accessed by a subset of tasksOne file per process

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Parallel I/O Patterns

Single file, accessed by 1 taskSingle shared file, accessed by all tasksMany shared files, accessed by groups of tasks

Baton-passing

Coordinated "View"Many independent files, accessed by a subset of tasksOne file per process

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Parallel I/O Patterns

Single file, accessed by 1 taskSingle shared file, accessed by all tasksMany shared files, accessed by groups of tasks

Baton-passingCoordinated "View"

Many independent files, accessed by a subset of tasksOne file per process

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Parallel I/O Patterns

Single file, accessed by 1 taskSingle shared file, accessed by all tasksMany shared files, accessed by groups of tasks

Baton-passingCoordinated "View"

Many independent files, accessed by a subset of tasks

One file per process

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Parallel I/O Patterns

Single file, accessed by 1 taskSingle shared file, accessed by all tasksMany shared files, accessed by groups of tasks

Baton-passingCoordinated "View"

Many independent files, accessed by a subset of tasksOne file per process

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Step 4: Understand the PFS

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Parallel File SystemPolicies

Allocation: howmuch space you have

Backups: if backups or snapshots are createdPurges: when data is deletedConfiguration: I/O pattern system is configured for

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Parallel File SystemPolicies

Allocation: howmuch space you haveBackups: if backups or snapshots are created

Purges: when data is deletedConfiguration: I/O pattern system is configured for

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Parallel File SystemPolicies

Allocation: howmuch space you haveBackups: if backups or snapshots are createdPurges: when data is deleted

Configuration: I/O pattern system is configured for

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Parallel File SystemPolicies

Allocation: howmuch space you haveBackups: if backups or snapshots are createdPurges: when data is deletedConfiguration: I/O pattern system is configured for

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Parallel File Systems

BlackMagic: IBM’s GPFS (general parallel file system)Closed sourceaka Elastic Scale Storage™ or Spectrum Scale™HPC users do not have knobs to tune

WhiteMagic: LustreOpen sourceUsers can deviate from default behavior

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Parallel File Systems

BlackMagic: IBM’s GPFS (general parallel file system)Closed sourceaka Elastic Scale Storage™ or Spectrum Scale™HPC users do not have knobs to tune

WhiteMagic: LustreOpen sourceUsers can deviate from default behavior

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Lustre Striping

HDDs are logically grouped intoOSTs (Object StorageTargets)Users can stripe a file across multiple OSTs

Explicitly take advantage of multiple OSTsDepends on the total amount of I/O you are doingThere is a system default

Use the correct striping for your use case

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Lustre Striping Commands

$ lfs setstripe -c 4 -s 4M testfile2$ lfs getstripe ./testfile2./testfile2lmm_stripe_count: 4lmm_stripe_size: 4194304lmm_stripe_offset: 21

obdidx objid objid group50 8916056 0x880c58 038 8952827 0x889bfb 0

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Lustre Striping Commands

$ lfs getstripe ./testfile./testfilelmm_stripe_count: 2lmm_stripe_size: 1048576lmm_stripe_offset: 50

obdidx objid objid group21 8891547 0x87ac9b 013 8946053 0x888185 057 8906813 0x87e83d 044 8945736 0x888048 0

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Step 6: Profit

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Steps for Dealing with I/O

1. Recognize the needGet some data out of the applicationGet some data out of the application fasterDeal with files efficiently

2. Investigate I/O libraries and tools, onemay be common inyour field.

3. Implement an I/O pattern4. Understand the file system you are working on5. ???6. Profit!

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Technical Details: MPI I/O

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Locking and Atomicity

$ export BGLOCKLESSMPIO_F_TYPE=1

int MPI_File_set_atomicity ( MPI_File mpi_fh, int flag );

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Opening Files

int MPI_File_open(MPI_Comm comm, const char *filename,int amode, MPI_Info info, MPI_File *fh);

AMode DescriptionMPI_MODE_RDONLY read onlyMPI_MODE_RDWR reading andwritingMPI_MODE_WRONLY write onlyMPI_MODE_CREATE create the fileMPI_MODE_EXCL error if file already existsMPI_MODE_DELETE_ON_CLOSE delete file on closeMPI_MODE_UNIQUE_OPEN file will not be concurrently openedMPI_MODE_SEQUENTIAL file will only be accessed sequentiallyMPI_MODE_APPEND position of all file pointers to end

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Organizing Data

Use MPI_Datatype to define the structure of your dataCorresponds to C struct

Read andwrite instances of this dataUse MPI_File_set_view for working with non-contiguousdata in a shared file

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UsefulMPI Function

offset = (long long) 0;MPI_Exscan(&contribute, &offset, 1, MPI_LONG_LONG,

MPI_SUM, file_comm);

Rank 0 1 2 3 4contribute 3 4 2 7 3offset 0 3 7 9 16

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UsefulMPI Function

offset = (long long) 0;MPI_Exscan(&contribute, &offset, 1, MPI_LONG_LONG,

MPI_SUM, file_comm);

Rank 0 1 2 3 4contribute 3 4 2 7 3

offset 0 3 7 9 16

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UsefulMPI Function

offset = (long long) 0;MPI_Exscan(&contribute, &offset, 1, MPI_LONG_LONG,

MPI_SUM, file_comm);

Rank 0 1 2 3 4contribute 3 4 2 7 3offset 0 3 7 9 16

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Accessing Files withMPI

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Accessing Files withMPI

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Accessing Files withMPILevel 0independent file ops, explicit offset, sequential dataLevel 1collective file ops, explicit offset, sequential dataLevel 2independent file ops, derived or non-contiguous dataLevel 3collective file ops, derived or non-contiguous data

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MPI I/O& Lustre

Can be built by HPC resource providers with Lustreintegration

mpi_info_set(myinfo, "striping_factor", stripe_count);mpi_info_set(myinfo, "striping_unit", stripe_size);mpi_info_set(myinfo, "cb_nodes", num_writers);

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Pro-Tips!

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Pro-Tip!

StepOneProfile your code. Fix up the I/O until it doesn’t suck.

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Pro-Tip!

Be SmartDon’t re-invent I/O, use an existing library or tool.

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Pro-Tip!

Working with File SystemsUse the PFS for Parallel I/O, do NOT use NFS.

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Pro-Tip!

I/O PatternCreate 1 file per node andmake this a tune-able parameter.

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Pro-Tip!

Ask an ExpertFind the "I/O person" at your HPC center and ask for guidance.

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This document was prepared as an account of work sponsored by an agency of the United States government. Neitherthe United States government nor Lawrence Livermore National Security, LLC, nor any of their employeesmakes anywarranty, expressed or implied, or assumes any legal liability or responsibility for the accuracy, completeness, orusefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringeprivately owned rights. Reference herein to any specific commercial product, process, or service by trade name,trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, orfavoring by the United States government or Lawrence Livermore National Security, LLC. The views and opinions ofauthors expressed herein do not necessarily state or reflect those of the United States government or LawrenceLivermore National Security, LLC, and shall not be used for advertising or product endorsement purposes.