Pipeline and Batch Sharing in Grid Workloads

1. Pipeline and Batch Sharingin Grid Workloads

2. Goals • Study diverse range of scientific apps • Measure CPU, memory and I/O demands • Understand relationships btwn apps • Focus is on I/O sharing

3. Batch-Pipelined workloads • Behavior of single applications has been well studied • sequential and parallel • But many apps are not run in isolation • End result is product of a group of apps • Commonly found in batch systems • Run 100s or 1000s of times • Key is sharing behavior btwn apps

4. Batch width Shared dataset Pipeline Pipeline sharing Shared dataset Batch-Pipelined Sharing

5. 3 types of I/O • Endpoint: unique input and output • Pipeline: ephemeral data • Batch: shared input data Shared dataset

6. Outline • Goals and intro • Applications • Methodology • Results • Implications

7. Six (plus one) target scientific applications • BLAST - biology • IBIS - ecology • CMS - physics • Hartree-Fock - chemistry • Nautilus - molecular dynamics • AMANDA -astrophysics • SETI@home - astronomy

8. Common characteristics • Diamond-shaped storage profile • Multi-level working sets • logical collection may be greater than that used by app • Significant data sharing • Commonly submitted in large batches

9. BLAST search string genomic database blastp BLAST searches for matching proteins and nucleotides in a genomic database. Has only a single executable and thus no pipeline sharing. matches

10. IBIS inputs climate data analyze IBIS is a global-scale simulation of earth’s climate used to study effects of human activity (e.g. global warming). Only one app thus no pipeline sharing. forecast

11. CMS configuration CMS is a two stage pipeline in which the first stage models accelerated particles and the second simulates the response of a detector. This is actually just the first half of a bigger pipeline. cmkin raw events cmsim geometry configuration triggered events

12. problem Hartree-Fock setup initial state HF is a three stage simulation of the non-relativistic interactions between atomic nuclei and electrons. Aside from the executable files, HF has no batch sharing. argos integral scf solutions

13. initial state Nautilus physics nautilus intermediate Nautilus is a three stage pipeline which solves Newton’s equation for each molecular particle in a three-dimensional space. The physics which govern molecular interactions is expressed in a shared dataset. The first stage is often repeated multiple times. bin2coord coordinates rasmol visualization

14. inputs AMANDA physics corsika AMANDA is a four stage astrophysics pipeline designed to observe cosmic events such as gamma-ray bursts. The first stage simulates neutrino production and the creation of muon showers. The second transforms into a standard format and the third and fourth stages follow the muons’ paths through earth and ice. raw events corama standard events ice tables mmc noisy events geometry mmc triggered events

15. SETI@home SETI@home is a single stage pipeline which downloads a work unit of radio telescope “noise” and analyzes it for any possible signs that would indicate extraterrestrial intelligent life. Has no batch data but does have pipeline data as it performs its own checkpointing. work unit setiathome analysis

16. Methodology • CPU behavior tracked with HW counters • Memory tracked with usage statistics • I/O behavior tracked with interposition • mmap was a little tricky • Data collection was easy. • Running the apps was challenge.

17. Absolute Resources Consumed • Wide range of runtimes. Modest memory usage.

18. Absolute I/O Mix • Only IBIS has significant ratio of endpoint I/O.

19. Relative I/O Mix • Modest BW requirements. Max is < 8.

20. Observations about individual applications • Modest buffer cache sizes sufficient • Max is AMANDA, needs 500 MB • Large proportion of random access • IBIS, CMS close to 100%, HF ~ 80% • Amdahl and Gray balances skewed • Drastically overprovisioned in terms of I/O bandwidth and memory capacity

21. Observations about workloads • These apps are NOT run in isolation • Submitted in batches of 100s to 1000s • Large degree of I/O sharing • Significant scalability implications

22. Scalability of batch width Storage center (1500 MB/s) Commodity disk (15 MB/s)

23. Batch elimination Storage center (1500 MB/s) Commodity disk (15 MB/s)

24. Pipeline elimination Storage center (1500 MB/s) Commodity disk (15 MB/s)

25. Endpoint only Storage center (1500 MB/s) Commodity disk (15 MB/s)

26. Conclusions • Grid applications do not run in isolation • Relationships btwn apps must be understood • Scalability depends on semantic information • Relationships between apps • Understanding different types of I/O

27. Questions? • For more information: • Douglas Thain, John Bent, Andrea Arpaci-Dusseau, Remzi Arpaci-Dusseau and Miron Livny, Pipeline and Batch Sharing in Grid Workloads, in Proceedings of High Performance Distributed Computing (HPDC-12). • http://www.cs.wisc.edu/condor/doc/profiling.pdf • http://www.cs.wisc.edu/condor/doc/profiling.ps