Scalable Parallel I/O Alternatives for Massively Parallel Partitioned Solver Systems

1. Scalable Parallel I/O Alternatives for Massively Parallel Partitioned Solver Systems Jing Fu, Ning Liu, Onkar Sahni, Ken Jansen, Mark Shephard, Chris Carothers Computer Science Department Scientific Computation Research Center (SCOREC) Rensselaer Polytechnic Institute chrisc@cs.rpi.edu Acknowledgments: Partners: Simmetrix, Acusim, Kitware, IBM NSF PetaApps, DOE INCITE, ITR, CTS; DOE: SciDAC-ITAPS, NERI; AFOSR Industry:IBM, Northrup Grumman, Boeing, Lockheed Martin, Motorola Computer Resources: TeraGrid, ANL, NERSC, RPI-CCNI Scalable Parallel I/O Alternatives

2. Outline • Motivating application: CFD • Blue Gene Platforms • I/O Alternatives • POSIX • PMPIO • syncIO • “reduce blocking” rbIO • Blue Gene Results • Summary Scalable Parallel I/O Alternatives

3. P2 P1 P3 PHASTA Flow Solver Parallel Paradigm • Time-accurate, stabilized FEM flow solver • Input partitioned on a per-processor basis • Unstructured mesh “parts” mapped to cores • Two types of work: • Equation formation • O(40) peer-to-peer non-blocking comms • Overlapping comms with comp • Scales well on many machines • Implicit, iterative equation solution • Matrix assembled on processor ONLY • Each Krylov vector is: • q=Ap (matrix-vector product) • Same peer-to-peer comm of q PLUS • Orthogonalize against prior vectors • REQUIRES NORMS=>MPI_Allreduce • This sets up a cycle of global comms. separated by modest amount of work Scalable Parallel I/O Alternatives

4. Parallel Implicit Flow Solver – IncompressibleAbdominal Aorta Aneurysm (AAA) 32K parts shows modest degradation due to 15% node imbalance Scalable Parallel I/O Alternatives

5. #of cores Rgn imb Vtx imb Time (s) Scaling 32k 1.72% 8.11% 112.43 0.987 128k 5.49% 17.85% 31.35 0.885 AAA Adapted to 109 Elements:Scaling on Blue Gene /P New: @ 294,912 cores  82% scaling But getting I/O done is a challenge… Scalable Parallel I/O Alternatives

6. Blue Gene /L Layout • CCNI “fen” • 32K cores/ 16 racks • 12 TB / 8 TB usable RAM • ~1 PB of disk over GPFS • Custom OS kernel Scalable Parallel I/O Alternatives

7. Blue Gene /P Layout • ALCF/ANL “Intrepid” • 163K cores/ 40 racks • ~80TB RAM • ~8 PB of disk over GPFS • Custom OS kernel Scalable Parallel I/O Alternatives

8. Blue Gene/ P (vs. BG/L) Scalable Parallel I/O Alternatives

9. Blue Gene I/O Archiectures • Blue Gene/L @ CCNI • 1 2-core I/O node per 32 compute nodes • 32K system has 512, 1 Gbit/sec network interfaces • I/O nodes connected 48 GPFS file servers • Servers 0, 2, 4, and 6 are metadata servers • Server 0 does RAS and other duties • 800 TB of storage from 26 IBM DS4200 storage arrays • Split into 240 LUNs, each server has 10 LUNs (7 @ 1MB and 3 @ 128KB) • Peak bandwidth is ~8GB/sec read and 4 GB/sec write • Blue Gene/P @ ALCF • Similar I/O node to compute node ratio • 128 dual core fileservers over Myrinet w/ 4MB GPFS block size • Metadata can be done by any server • 16x DDN 9900  7.5 PB (raw) storage w/ peak bandwidth of 60 GB/sec. Scalable Parallel I/O Alternatives

10. Data 0 Data 1 Data 2 Data N-1 0 1 2 N-1 Block 0 Block 1 Block 2 Block N-1 Non-Parallel I/O: A Bad Approach… • Sequential I/O: • All processes send data to rank 0, and 0 writes it to the file Lacks scaling and results in excessive memory use on rank 0Must think parallel from the start, but that implies data/file partitioning… Scalable Parallel I/O Alternatives

11. 1 POSIX File Per Processor (1PFPP) • Pros: • parallelism, high performance at small core counts • Cons: • lots of small files to manage • LOTS OF METADATA – stress parallel filesystem • difficult to read back data from different number of processes • @ 300K cores yields 600K files • @ JSC  kernel panic!! • PHASTA currently uses this approach… Scalable Parallel I/O Alternatives

12. New Partitioned Solver Parallel I/O Format • Assumes data accessed in a coordinated manner • File: master header + series of data blocks • Each data block has header and data • Ex: 4 parts w/ 2 fields per part • Allows for different processor config: • (1 core @ 4 parts), • (2 core @ 2 parts) • (4 cores @ 1 part) • Allows for 1 to many files to control metadata overheads Scalable Parallel I/O Alternatives

13. MPI_File alternatives: PMPIO • PMPIO  “poor man’s parallel I/O” from “silo” mesh and field library • Divides app into groups of writers • w/i a group only 1 writer at a time to a file • Passing of a “token” ensures synchronization w/i a group • Support for HDF5 file format • Uses MPI_File_read/write_at routines Scalable Parallel I/O Alternatives

14. MPI_File alternatives: syncIO • Flexible design allows variable number files and procs/writers per file • Within a file, can be configured to write on “block size boundries” which are typically 1 to 4MB. • Implemented using collective I/O routines : e.g., MPI_File_write_at_all_begin Scalable Parallel I/O Alternatives

15. MPI_File alternatives: rbIO • Rb  “reduced blocking” • Targets “checkpointing” • Divides application into workers and writers with 1 writer MPI task per group of workers. • Workers send I/O to writers over MPI_Isend and are free to continue – • e.g., hides the latency of blocking parallel I/O • Writers then perform blocking MPI_File_write_at operation using MPI_SELF communicator Scalable Parallel I/O Alternatives

16. BG/L: 1PFPP w/ 7.7 GB data Scalable Parallel I/O Alternatives

17. BG/L: PMPIO w/ 7.7 GB data HDF5 Peak: 600MB/sec RAW MPI-IO Peak: 900 MB/sec Scalable Parallel I/O Alternatives

18. BG/L: syncIO w/ 7.7 GB data Write Performance Peak: 1.3 GB/sec Read Performance Peak: 6.6 GB/sec Scalable Parallel I/O Alternatives

19. BG/P: syncIO w/ ~60 GB data Scalable Parallel I/O Alternatives

20. BG/L: rbIO actual BW w/ 7.7 GB data Scalable Parallel I/O Alternatives

21. BG/L: rbIO perceived BW w/ 7.7 GB data ~22 TB/sec ~11 TB/sec Scalable Parallel I/O Alternatives

22. BG/P: rbIO actual BW w/ ~60 GB data ~17.9 GB/sec Scalable Parallel I/O Alternatives

23. BG/P: rbIO perceived BW w/ ~60 GB data ~21 TB/sec Scalable Parallel I/O Alternatives

24. Related Work • A. Nisar, W. Liao, and A. Choudhary, “Scaling Parallel I/O Performance through I/O Delegate and Caching System,” in Proceedings of the 2008 ACM/IEEE conference on Supercomputing, 2008. • Performance “rbIO” inside MPI via threads and using upto 10% compute cores as I/O workers • Benchmark studies (hightlight just a few…) • H Yu et al [18] – BG/L: 2 GB/sec @ 1K • Saini et al [19] – 512 NEC SX-8 cores – I/O was not scalable when all processors access a shared file. • Larkin et al [17] – large performance drop at 2K core count for CrayXT3/XT4 • Lang et al [30] – large I/O study across many benchmarks on Intrepid/BG-P. Found 60 GB/s read and 45 GB/s write. In practice, Intrepid has a peak I/O rate of around 35 GB/sec Scalable Parallel I/O Alternatives

25. Summary and Future Work • We examine several parallel I/O approaches.. • 1 POSIX File per Proc: < 1 GB/sec on BG/L • PMPIO: < 1 GB/sec on BG/L • syncIO – all processors write as groups to different files • BG/L: 6.6 GB/sec read, 1.3 GB/sec write • BG/P: 11.6 GB/sec read, 25 GB/sec write • rbIO – gives up 3 to 6% of compute nodes to hide latency of blocking parallel I/O. • BG/L: 2.3 GB/sec actual write, 22 TB/sec perceived write • BG/P: ~18 GB/sec actual write, ~22 TB/sec perceived write • Good trade-off on Blue Gene • All procs to 1 file does not yield good performance even if aligned. • Performance “sweet spot” for syncIO depends significantly on I/O architecture and so file format must be tuned accordingly • BG/L @ CCNI has a metadata bottleneck and must adjust # of files according – e.g., 32 to 128 writers • BG/P @ ALCF can sustain much higher performance, but requires more files – e.g., 1024 writers • Suggest collective I/O is sensitive to underlying file system performance. • For rbIO, we observed that 1024 writers was the best performance so far for both BG/L and BG/P platforms.. • Future Work – impact on performance of different filesystems • Leverage Darshan logs @ ALCF to better understand Intrepid performance • More experiments on Blue Gene/P under PVFS, CrayXT5 under Lustre Scalable Parallel I/O Alternatives