Communications - December 2009

bad idea for one basic reason: It must perform moderately complex computation at least as fast as a computer’s “main” CPU, yet manufacturers insist it be cheap due to the cost “multiplier” effect when a computer includes many network links. To show that solving this problem is impossible, consider the following proof by contradiction:

Assume an NFE processor with requisite attributes—moderately general-purpose, fast, and cheap—with most computer manufacturers using it as a main processor, not as a lowly NFE and so in need of even more network bandwidth.

Many computer engineers have long understood, as O’Dell wrote, that the most efficient way to implement a network-protocol software stack is to use one or more CPUs of an N-way SMP, but users strongly resist this idea when they discover they’ve paid big for what, from an application point of view, is only an N-1-way computer; note, too, popular “low-end” cases in which N = 2 or 4. Apparently, Sun Microsystems (“the network is the computer”) hasn’t left much of an impression on IT managers. The result is that NFE startups continue to waste engineering talent by trying to pour a quart of technology into this particular pint jar.

Scott marovich, Palo Alto, CA

how to address Big Data

I want to thank Adam Jacobs for cataloging the important issues in the management of large volumes of data in his article “The Pathologies of Big Data” (Aug. 2009). But please know that innovations are also being made by relational database vendors. One recently released product is the HP Oracle Database Machine ( http://www.oracle.com/ database/ database-machine.html) that processes large volumes of data using massive horizontal parallelism across a shared-nothing storage grid. Pipeline (vertical) parallelism is enabled by offloading data processing to a storage grid, so the amount of data that must be shipped back from the storage grid to the database grid is reduced as well. The storage grid and database grid are connected through a high-speed Infini-band interconnect.

A single DM has an I/O bandwidth of 14GB/sec in the first version of the

product (with uncompressed data). If the data is compressed, the effective bandwidth is much greater, depending on compression ratio. (Jacobs’s experiment would have run much faster on the DM.) Multiple DMs can be connected to increase data capacity, along with corresponding network/comput-ing capacity.

Oracle’s Automatic Storage Management (ASM) is an integral part of DM, providing automatic load balancing across all nodes in the storage grid. ASM also provides fault-tolerance through dual or triple mirroring. The Oracle database provides high-avail-ability and disaster-recovery features, and ASM enables sequential I/Os through its allocation strategies (such as large allocations).

One of the best ways to improve query performance (assuming the most optimal access method is used) is to avoid I/O altogether. The Oracle database provides rich partitioning strategies that enable skipping large chunks of data that do not qualify as a query scan.

Moving data from production (OLTP systems) to a specialized data store adds to a system’s total cost of ownership, as one would otherwise be managing two different data stores with poor or no integration. DM solves the big-data problem without a special data store just for data analysis; DM provides a single view of the data.

Though I am a technical member of the Oracle Exadata development team, my aim here is not to plug the product but report that the big-data problem is indeed being tackled, particularly by relational database vendors.

umesh Panchaksharaiah, Richmond, CA

my Generation Samuel Greengard’s news story “Are We Losing Our Ability to Think Critically” (July 2009) is inspiring as a basis for future work. I am coordinating an interdisciplinary seminar on the collective construction of knowledge (http:// seminario.edusol.info in Spanish), including two topics Greengard might be able to bridge: One is free software in a democratic society, inciting people to be more politically active and involved, despite being (usually) independent of political parties and other traditional

means of shaping society. The other is how motivation and peer-recognition function in these communities; such free-culture communities have much in common with scientific communities, despite starting off with completely different motivations.

My generation (born in the 1970s), including many people in the free software movement, has directly experienced the great shift computing and networking have brought the world, fully embracing the technologies. The greatest difference between people who are just users of computing and those striving to make it better depends on who has the opportunity to appropriate it beyond, say, the distraction level, the blind Google syndrome, or the simple digestion of “piles of data and information [that] do not equate to greater knowledge and better decision making.”

Thanks to Greengard for sparking some useful thoughts.

Gunnar Wolf, mexico City

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