Douglas Thain, John Bent, Andrea Arpaci-Dusseau, Remzi Arpaci-Dusseau, and Miron Livny WiND and Condor Projects 6 May 2003 Pipeline and Batch Sharing in Grid Workloads

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

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

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

3 types of I/O › Endpoint: unique input and output › Pipeline: ephemeral data › Batch: shared input data

Outline › Goals and intro › Applications › Methodology › Results › Implications

Six (plus one) target scientific applications › BLAST - biology › IBIS - ecology › CMS - physics › Hartree-Fock - chemistry › Nautilus - molecular dynamics › AMANDA -astrophysics › - astronomy

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

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

IBIS inputs analyze forecast climate data 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.

CMS configuration cmkin raw events geometry 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. cmsim triggered events configuration

Hartree-Fock problem 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

Nautilus initial state 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 physics

AMANDA inputs corsika raw events 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. corama standard events mmc noisy events physics mmc triggered events ice tables geometry

work unit setiathome analysis 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.

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.

Resources Consumed Relatively modest. Max BW is 7 MB/s for HF.

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

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

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

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

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

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

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

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

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). – –