1. Locality-driven High-level I/O Aggregation
for Processing Scientific Datasets
Jialin Liu, Bradly Crysler, Yin Lu, Yong Chen
Oct
Data-Intensive Scalable Computing Laboratory (DISCL)

2. Outline Introduction Motivation Hila: High Level I/O Aggregation
Evaluation
Conclusion and Future Work

3. 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.
GCRM, 100 million collumns, 128
levels per column, 50 km
Accessing and analyzing the data
reveals poor I/O performance due to
the logical-physical mismatching.

4. 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

5. Motivation
I/O methods in scientific I/O libraries(PnetCDF, ADIOS, HDF5):
Independent I/O
Processes collaboration: No
Calls collaboration : No
Collective I/O
Processes collaboration: Yes
Calls collaboration : No
Noblocking I/O
Processes collaboration: Yes
Calls collaboration : Yes

6. 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
Contention on Storage Server without Aware of Locality

7. Conclusion: Overlapping Should be Removed
Performance with Overlapping Calls
Conclusion: Overlapping Should be Removed

8. Idea: High level I/O Aggregation
Physical
Layout
start{0,0,0}
length{100,200,200}
start{10,20,100}
length{10,300,400}
Call0
Call1
Logical Input
Decomposition
Physical
Layout
sub0
sub2
start{0,0,0}
length{100,200,100}
sub1
sub3
start{0,0,100}
length{100,200,100}
start{10,20,100}
length{10,150,400}
start{10,170,100}
length{10,150,400}

9. 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

10. Hila: High Level I/O Aggregation
Way to figure out the physical layout
Sub-correlation Function
Sub-correlation Set
Lustre Striping: stripe size: s; stripe count: l;
Dataset : Dimension: d; subsets size: m

11. Hila Algorithm: Prior Step
Prior Step: calculate sub-correlation set, one time analysis

12. Hila Algorithm: Decomposition
Main Steps: Request Decomposition and Aggregation

13. Performance Improved with Hila
Improvement with Hila
Performance Improved with Hila

14. FASM Improved with Hila
Improvement with Hila
FASM Improved with Hila

15. Conclusion and Future Work
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.

16. Thanks Q&A Locality-driven High-level I/O Aggregation
for Processing Scientific Datasets
Thanks
Q&A