Locality-driven High-level I/O Aggregation for Processing Scientific Datasets Jialin Liu, Bradly Crysler, Yin Lu, Yong Chen Oct. 07. 2013 Data-Intensive Scalable Computing Laboratory (DISCL)

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

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.

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 Processes collaboration: No Calls collaboration : No Collective I/O Processes collaboration: Yes Calls collaboration : No Noblocking I/O 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 Contention on Storage Server without Aware of Locality

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

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}

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: s; 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

Performance Improved with Hila Improvement with Hila Performance Improved with Hila

FASM Improved with Hila Improvement with Hila FASM Improved with Hila

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.

Thanks Q&A Locality-driven High-level I/O Aggregation for Processing Scientific Datasets Thanks Q&A http://discl.cs.ttu.edu