1. Elastic Cyberinfrastructure for Research Computing
Research Computing Services
Elastic Cyberinfrastructure for Research Computing
Glenn Bresnahan
Research Computing Services
Boston University

2. Elastic Cyberinfrastructure
Research Computing Services
Elastic Cyberinfrastructure
Framework for building sharable computational infrastructure for research computing
Uses the MOC developed Hardware Isolation Layer (HIL) as a core technology
Preserve the best aspects of traditional HPC/HTC computing cluster while providing the elasticity, innovation and market-driven value of the cloud

3. Motivation Experiences with computing environments at the MGHPCC ⃰
Research Computing Services
Motivation
Experiences with computing environments at the MGHPCC ⃰
Experiences with building and supporting large shared HPC computing environments
Initial experiences in deploying HIL resources pools to extend and expand HPC computing environments
⃰ Massachusetts Green High Performance Computing Center

4. MGHPCC Computing Environment
Research Computing Services
MGHPCC Computing Environment
MGHPCC provides shared physical infrastructure and WAN connectivity
Space, pipe, power, and ping
A small number of independent HPC/HTC cluster per member (BU, Harvard, MIT, NEU, UMass)
Evolving shared systems:
Northeast ATLAS Tier 2 (NET2)
Engaging 1 funded by NSF
C3DDB funded by MLSC ⃰
Northeast Storage Exchange (NESE)
Mass Open Cloud
⃰ Massachusetts Life Science Center

5. MGHPCC Computing Environment
Research Computing Services
MGHPCC Computing Environment
MGHPCC provides shared physical infrastructure and WAN connectivity
Space, pipe, power, and ping
A small number of independent HPC/HTC cluster per member (BU, Harvard, MIT, NEU, UMass)
Evolving shared systems:
Northeast ATLAS Tier 2 (NET2) – Traditional HPC/HTC
Engaging 1 funded by NSF– Mostly Traditional HPC
C3DDB funded by MLSC ⃰ – Mostly Traditional HPC
Northeast Storage Exchange (NESE) – Cloud (storage)
Mass Open Cloud – Cloud
⃰ Massachusetts Life Science Center

6. Traditional HPC clusters (BU example)
Research Computing Services
Traditional HPC clusters (BU example)
Heterogeneous hardware: CPU, GPU, fast fabric, parallel file system, back-up/archive
~ 8000 CPU, 100K GPU, Infiniband, 3 PB GPFS, backup to campus
Serve a broad base of diverse researchers university-wide
2,400 user; 524 research projects, 70 departments
Extensive support for researchers
Training, coding, porting, debugging, application support
Multi-pronged financial model
Central university support for shared core (compute, storage, infrastructure, services)
Proportional contributions by campus and/or college
Condo-style environment for direct researcher investment (Buy-in)
Funding agency support for large-scale, multi-user enhancements
Buy-n represents ~60% of SCC resources; >$2M investment

7. Research Computing Challenges
Research Computing Services
Research Computing Challenges
Most research groups do not have the resources/desire to manage their own computing environment
Most researchers are not from traditional computational disciplines
Most research groups are small
Computing in multiple environments is hard
Differences in software stacks, jobs schedulers, policies
Managing multiple copies of data is challenging
IRB and compliance issues
Support is critical
Majority of IT/RCS staff effort is user support
Supporting individualized computing environments is not sustainable
Financial models are important
Not all dollars are equal (CapEx vs OpEx)
PIs represent 74 different departments and centers; traditional computational depts.: Physics, Chemistry; Astro, Engineering, Geosciences
52% of research groups have 3 or fewer users; 70 % have 5 or less

8. Distribution of individuals/project
Research Computing Services
Distribution of individuals/project

9. Elastic Cyberinfrastructure for Regional Research Computing
Research Computing Services
Elastic Cyberinfrastructure for Regional Research Computing
Multiple resource pools
Private HPC clusters, shared HPC clusters, MOC HIL pools, MOC IaaS pools
Storage pools (e.g. NESE)
Robust data center fabric
Compute resources shift from HIL pools to HPC or IaaS clusters on demand
Shared object storage
Common data management, discovery, publishing (e.g. Dataverse)

10. Research Computing Services
ECI Features
Computing environments, including traditional Linux clusters, are elastic, expanding and contracting on demand
New bare-metal or virtualized computing environments and services can be created dynamically from scratch or by stitching together existing resources and services
Shared storage allows researchers to create cross-environment workflows with analysis performed on data in situ
Data management, sharing, discovery, provenance and dissemination are provided as common services
Financial models acknowledge practical need for multiple ownership and researcher investment. Affords development of more sophisticated economic models.

11. Status Current prototypes with ATLAS, NEU HIL pool, Engaging 1, C3DDB
Research Computing Services
Status
Current prototypes with ATLAS, NEU HIL pool, Engaging 1, C3DDB
NESE object storage project ramping up
Funding request to enhance MGHPCC networking
Funding opportunities to expand deployment