Jialin Liu, Bradly Crysler, Yin Lu, Yong ChenOct. 15. 2013@U-REaSON Seminar

Data-Intensive Scalable Computing Laboratory (DISCL)

Locality-driven High-level I/O Aggregation for Processing Scientific Datasets

1

Introduction

Scientific simulations nowadays generate a few terabytes (TB) of data in a single run and the data sizes are expected to reach petabytes (PB) in the near future. VPIC, Vector Particle in Cell, Plasma physics, 26 bytes per particle, 30TB

Accessing and analyzing the data reveals poor I/O performance due to the logical-physical mismatching.

Introduction

Scientific Datasets and Scientific I/O Libraries PnetCDF, HDF5, ADIOS

PnetCDF

MPI-IO

Parallel File Systems

Scientific I/O libraries allow users to specify array-based logical input Logical-physical mismatching

Motivation

I/O methods in scientific I/O libraries(PnetCDF, ADIOS, HDF5):

Independent I/O

Collective I/O

Nonblocking I/O

Processes collaboration: No Calls collaboration : No

Processes collaboration: Yes Calls collaboration : No

Processes collaboration: Yes Calls collaboration : Yes

Motivation

Contention on Storage Server without Aware of Locality

…

Call0

…

Call1

…

Calli

…

Two Phase Collective I/O

…ag00 ag01 ag02 ag03

… … …

ag10 ag11 ag12 ag13 agi0 agi1 agi2 agi3

Performance with Overlapping Calls

Conclusion: Overlapping Should be Removed

Idea: High level I/O Aggregation

start{0,0,0}length{100,200,100}

start{0,0,100}length{100,200,100}

start{10,20,100}length{10,150,400}

start{10,170,100}length{10,150,400}

PhysicalLayoutsub0

sub2

sub0sub2

sub1

sub3

sub1sub3

PhysicalLayout

start{0,0,0}length{100,200,200}

start{10,20,100}length{10,300,400}

Call0

Call1

Logical Input Decomposition

Idea: High level I/O Aggregation

Basic Idea Figure out the overlapping among requests Eliminate the overlapping before doing I/O

Challenges How to decompose the requests How to aggregate the sub-arrays at a high level

Hila: High Level I/O Aggregation

Way to figure out the physical layout Sub-correlation Function

Sub-correlation Set

Lustre Striping: stripe size: t; stripe count: l; Dataset : Dimension: d; subsets size: m

Hila Algorithm: Prior Step

Prior Step: calculate sub-correlation set, one time analysis

Hila Algorithm: Decomposition

Main Steps: Request Decomposition and Aggregation

Improvement with Hila

Performance Improved with Hila

Improvement with Hila

FASM Improved with Hila

Conclusion and Future Work

Conclusion The mismatching between logical access and physical layout

can lead to poor performance. We propose the locality-driven high-level aggregation approach

(HiLa) to facilitate the existing I/O methods by eliminating the overlapping among sub-array requests.

Future Work Apply to write operations Integrate with file systems.

Locality-driven High-level I/O Aggregationfor Processing Scientific Datasets

ThanksQ&A

http://discl.cs.ttu.edu