FLOPS Fall Flat for Intelligence Agency

FLOPS Fall Flat for Intelligence Agency by Nicole Hemsoth.

From the post:

The Intelligence Advanced Research Projects Activity (IARPA) is putting out some RFI feelers in hopes of pushing new boundaries with an HPC program. However, at the core of their evaluation process is an overt dismissal of current popular benchmarks, including floating operations per second (FLOPS).

To uncover some missing pieces for their growing computational needs, IARPA is soliciting for “responses that illuminate the breadth of technologies” under the HPC umbrella, particularly the tech that “isn’t already well-represented in today’s HPC benchmarks.”

The RFI points to the general value of benchmarks (Linpack, for instance) as necessary metrics to push research and development, but argues that HPC benchmarks have “constrained the technology and architecture options for HPC system designers.” More specifically, in this case, floating point benchmarks are not quite as valuable to the agency as data-intensive system measurements, particularly as they relate to some of the graph and other so-called big data problems the agency is hoping to tackle using HPC systems.

Responses are due by Apr 05, 2013 4:00 pm Eastern.

Not that I expect most of you to respond to this RFI but I mention it as a step in the right direction for the processing of semantics.

Semantics are not native to vector fields and so every encoding of semantics in a vector field is a mapping.

As is every extraction of semantic from a vector field is the reverse of that mapping process.

The impact of this mapping/unmapping of semantics to and from a vector field on interpretation are unclear.

As mapping and unmapping decisions are interpretative, it seems reasonable to conclude there is some impact. How much isn’t known.

Vector fields are easy for high FLOPS systems to process but do you want a fast inaccurate answer or one that bears some resemblance to reality as experienced by others?

Graph databases, to name one alternative, are the current rage, at least according to graph database vendors.

But saying “graph database,” isn’t the same as usefully capturing semantics with a graph database.

Or processing semantics once captured.

What we need is an alternative to FLOPS that represents effective processing of semantics.

Suggestions?

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