[CSG Spring 2010] Research Computing Funding Issues

Alan Crosswell from Columbia kicks off the workshop on Research Computing Funding Issues. The goals of the session are: what works, what are best practices, what are barriers or enablers for best practices?

Agenda:
– Grants Jargon 101 – Alan
– Funding Infrastructure, primarily data centers- Alan
– Funding servers and storeage – Curt
– Funding staff – Greg
– Funding storace and archival life cycle – Serge and Raj
– Summary and reports from related initiatives – Raj

Grants Jargon
– A21: Principles for determining costs applicable to grants, contracts and other agreements with educational institutions. What are allowed and unallowed costs.
– you can’t charge people different rates for the same service.
– direct costs – personnel, equipment, supplies, travel consultants, tuition, central computer charges, core facility charges
– indirect costs a/k/a Facilities adn Admin (F&A) – overhead costs such as heat, administrative salaries, etc.
– negotiated with federal government. Columbia’s rate is 61%. PIs see this as wasted money.
– modified direct costs – substractions include equipment, participant support, GRA tuition, alteration or renovation, subcontracts > $25k.
Faculty want to know why everything they need isn’t included in the indirect cost. Faculty want to know why they can buy servers without paying overhead, but if they buy services from central IT they pay the overhead. Shel notes that CPU or storage as a service is the only logical direction, but how do we do that cost effectively under A21? Dave Lambert says that they negotiated a new agreement with HHS for their new data center. Dave Gift says that at Michigan State they let researchers buy nodes in a condo model, but some think that’s inefficient and not a good model for the future.
Alan asks whether other core shared facilities like gene sequencers are subject to indirect costs.

Campus Data Center Models
– Institutional core research facility – a number that grew out of former NSF supercomputer centers.
– Departmental closet clusters – sucking up lots of electricity that gets tossed back into the overhead.
– Shared data centers between administration and research – Columbia got some stimulus funding for some renovation around NIH research facilities.
– Multi-institution facilities (e.g. RENCI in North Carolina, recent announcement in Massachusets)
– Cloud – faculty go out with credit card and buy cycles on Amazon
– Funding spans the gamut from fully institutionally funded to fully grant funded.

Funding pre-workshop survey results
– 19 of 22 have centrally run research data centers, mostly (15) centrally funded. 9 counts of charge-back, 3 counts of grant funding)
– 18 of 22 respondents have departmentally run research data centers, mostly (14 counts) departmentally funded (3 counts of using charge back, 4 counts of grant funding)
– 14 have inventoried their research data centers
– 10 have gathered systematic data on research computing needs

Dave Lambert – had to create a cost allocation structure for the data centers for the rest of the institution to match what they charge grants for research use, in order to satisfy A21’s requirement to not charge different rates.

Kitty – as universities start revealing the costs of electricity to faculty, people will be encouraged to join the central facility. Dave notes that security often provides another incentive for people because of the visibility of incidents. At Georgetown they now have security office (in IT) review of research grants.

Curt Hillegas from Princeton is talking about Server and Short to Mid-Term Storage Funding
talking about working storage, not long-term archival storage
-some funding has to kick-start the process – either an individual faculty member or central funding. Gary Chapman notes that there’s an argument to be made for central funding of interim funding to keep the resources going between grant cycles.

Bernard says that at Minnesota they’ve done a server inventory and found that servers are located in 225 rooms in 150 different buildings, but only 15% of those are devoted to research. Sally Jackson thinks the same is approximately true at Illinois. At Princeton about 50% of computing is research, and that’s expected to grow.

Stanford is looking at providing their core image as an Amazon Machine Image.

At UC Berkeley they have three supported computational models available and they fund design consulting with PIs before the grant.

Cornell has a fee-for-service model that is starting to work well. At Princeton that has never worked.

Life Cycle management – you gotta kill the thing, to make room for the new. Terry says we need a “cash for computer clunkers” program. You need to offer transition help for researchers.

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