A PLFS Plugin for HDF5 for Improved I/O Performance and

AnalysisKshitij Mehta1, John Bent2, Aaron Torres3, Gary

Grider3, Edgar Gabriel1

1 University of Houston, Texas2 EMC Corp.3 Los Alamos National Lab

DISCS 2012

Talk Outline● Background

– HDF5– PLFS

● Plugin– Goals and Design

● Semantic Analysis● Experiments and Results● Conclusion

HDF5 – An Overview● Hierarchical Data Format● Data model, File format, and API● Tool for managing complex data● Widely used in industry and academia● User specifies data objects and logical relationship

between them● HDF5 maintains data structures, memory

management, metadata creation, file I/O

HDF5 – An Overview (II)● Parallel HDF5

– Build with an MPI library– File create, dataset create, group create etc. are

collective calls● User can select POSIX I/O, or parallel I/O

using MPI-IO (individual/collective)● File portable between access by sequential,

PHDF5

File

HDF5 – An Overview (III)

Group

Group

D1 D2

D3

Metadata D1 D2 D3

.h5 file

PEs

HDF5 – An Overview (IV)● File is a top level object, collection of objects● Dataset is a multi-dimensional array

– Dataspace● Number of dimensions● Size of each dimension

– Datatype● Native (int, float, etc.)● Compound (~struct)

● Group is a collection of objects (groups, datasets, attributes)● Attributes used to annotate user data● Hyperslab selection

– Specify offset, stride in the dataspace– e.g. write selected hyperslab from matrix in memory to selected

hyperslab in dataset in file

HDF5 Virtual Object Layer (VOL)

● Recently introduced by the HDF group

● New abstraction layer, intercepts API calls

● Forwards calls to object plugin● Allows third party plugin

development ● Data can be stored in any

format– netCDF, HDF4 etc.

Public API

.h5netCDF

Object Plugin

Opportunities in HDF5• Preserve semantic information about HDF5

objects• Single .h5 file a black box• Allows performing post-processing on individual

HDF5 objects

• Improve I/O performance on certain file systems• N-1 access often results in sub-optimal I/O

performance on file systems like Lustre

PLFS

• Parallel Log Structured File System developed at LANL, CMU, EMC

• Middleware positioned between application and underlying file system

• Transforms N-1 access pattern into N-N• Processes write to separate files, sufficient

metadata maintained to re-create the original shared file

• Demonstrated benefits on many parallel file systems

Goals of the new plugin

• Store data in a new format, different from the native single file format• Preserves semantic information• Perform additional analysis and optimizations

• Use PLFS to read/write data objects• Tackles performance problem due to N-1 access

Plugin Design• Implementation for various object functions • Provide a raw mapping of HDF5 objects to the

underlying file system• HDF5 file, groups stored as directories• Datasets as PLFS files• Attributes as PLFS files stored as dataset_name.attr_name

• Use PLFS API calls in the plugin• PLFS Xattrs store dataset metadata (datatype,

dataspace,..)• Xattrs provide key-value type access

PLFS Plugin● Relative path describes relationship between objects● User still sees the same API

File

Group

Group

D1 D2

D3

File/

Group/

D1

D2

Group/

D3

Semantic Analysis (I)• Active Analysis

• Application can provide a data parser function• PLFS applies function on the streaming data• Function outputs key-value pairs which can be

embedded in extensible metadata• e.g. recording the height of the largest wave in

ocean data within each physical file• Quick retrieval of the largest wave, since only need

to search extensible metadata• Extensible metadata can be stored on burst buffers

for faster retrieval

Active Analysis (II)

PE PLFSdata

ParserParser Outpu

t

FS

Burst Buffer

Semantic Analysis (II)• Semantic Restructuring

• Allows re-organizing data into a new set of PLFS shards

• e.g. assume ocean model stored row-wise• Column-wise access expensive• Analysis routine can ask for “column-wise re-

ordering”• PLFS knows what it means, since it knows the

structure• Avoids application having to restructure data

by calculating a huge list of logical offsets

Semantic Restructuring (II)

Restructure

HDF5 Datasets

“Re-order wave lengths recorded in October 2012 in column-major (Hour x Day)”

Experiments and Results● Lustre FS, 12 OSTs, 1M stripe size● HDF5 performance tool “h5perf”● Multiple processes write data to multiple

datasets in a file● Bandwidth values presented are average of 3

runs● 1,2,4,8,32,64 PEs

– 4 PEs/node max● 10 datasets, minimum total data size 64G● Comparing MPI-IO Lustre, Plugin, AD_PLFS

(PLFS MPI-IO driver)● Individual I/O (non-collective) tests

Write Contiguous

• Aligned transfer size of 1M• For almost all cases, plugin better than

MPI-IO , AD_PLFS shows best performance

Write Interleaved

• Unaligned transfer size of (1M + 10 bytes)• Plugin performance > MPI-IO

Read Performance

• Contiguous reads ( 1M ) and Interleaved reads ( 1M+10 bytes )

• Similar trend as in writes• MPI-IO < Plugin < AD_PLFS

Conclusion● New plugin for HDF5 developed using

PLFS API● New output format allows for Semantic

Analysis● Using PLFS improves I/O performance● Tests show plugin performs better than

MPI-IO in most cases, AD_PLFS shows best performance

● Future Work: Use AD_PLFS API calls in the plugin instead of native PLFS API calls, provide collective I/O in the plugin

Thank You

Acknowledgements:• Quincey Koziol, Mohamad Chaarawi – HDF group• University of Dresden for access to Lustre FS

• Why not use AD_PLFS on default .h5 file ?• Changing output format allows for

semantic analysis• Provides a more object-based storage

(DOE fast forward proposal – EMC, Intel, HDF working towards an object stack)

Questions