Fast Database Emerges from MIT Class, GPUs and Student’s Invention by Ian B. Murphy.
Details the invention of MapD by Todd Mostak.
From the post:
MapD, At A Glance:
MapD is a new database in development at MIT, created by Todd Mostak.
- MapD stands for “massively parallel database.”
- The system uses graphics processing units (GPUs) to parallelize computations. Some statistical algorithms run 70 times faster compared to CPU-based systems like MapReduce.
- A MapD server costs around $5,000 and runs on the same power as five light bulbs.
- MapD runs at between 1.4 and 1.5 teraflops, roughly equal to the fastest supercomputer in 2000.
- MapD uses SQL to query data.
- Mostak intends to take the system open source sometime in the next year.
Sam Madden (MIT) describes MapD this way:
Madden said there are three elements that make Mostak’s database a disruptive technology. The first is the millisecond response time for SQL queries across “huge” datasets. Madden, who was a co-creator of the Vertica columnar database, said MapD can do in milliseconds what Vertica can do in minutes. That difference in speed is everything when doing iterative research, he said.
The second is the very tight coupling between data processing and visually rendering the data; this is a byproduct of building the system from GPUs from the beginning. That adds the ability to visualize the results of the data processing in under a second. Third is the cost to build the system. MapD runs in a server that costs around $5,000.
“He can do what a 1000 node MapReduce cluster would do on a single processor for some of these applications,” Madden said.
Not a lot of technical detail but you could start learning CUDA while waiting for the open source release.
At 1.4 to 1.5 teraflops on $5,000 worth of hardware, how will clusters will retain their customer base?
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