Slashdot Mirror
Data Sorting World Record — 1 Terabyte, 1 Minute
An anonymous reader writes "Computer scientists from the University of California, San Diego have broken the 'terabyte barrier' — and a world record — when they sorted more than a trillion bytes of data in 60 seconds. During this 2010 'Sort Benchmark' competition, a sort of 'World Cup of data sorting,' the UCSD team also tied a world record for fastest data sorting rate, sifting through one trillion data records in 172 minutes — and did so using just a quarter of the computing resources of the other record holder."
Ah, my mistake - the "trillion data records" refers to a different benchmark. In the 1TB benchmark, records are 100 bytes long.
No, most Top500 machines are composed of commercially available, unmodified parts. More than half use GigE interconnects, though Infiniband has nearly caught up. I'm not sure if you'd count Infiniband as COTS-- at least a couple of years ago, it was fairly involved to configure, and it's not cheap. But anybody who has the money can buy it.
When I read the summary I thought what's the big deal if the 1 TB of data only contained two records 0.5 TB each. Then I saw that kdawson wrote the summary. So I browsed over here and saw that the impressive thing is that they sorted 1,000,000,000,000 records of 100 bytes each with 10 byte keys.
A paper describing the test is here. TritonSort is the abstract method; GraySort and MinuteSort are concrete/benchmarked variations of TritonSort.
As TFA states, this is more about balancing system resources than anything else. The actual sort method used was "a variant of radix sort" (page two of the linked PDF). Everything else was an exercise in how to break up the data into manageable chunks (850MB), distribute the chunks to workers, then merge the results. That was the real breakthrough, I think. But I wonder how much is truly a breakthrough and how much was just taking advantage of modern hardware, since one of the major changes in MinuteSort is simply avoiding a couple of disk writes by keeping everything in RAM (a feat possible because that much RAM is readily available to a single worker).
Regardless, this kind of work is very cool. If web framework developers (for example) paid greater attention to balancing data flow and work performed, we could probably get away with half the hardware footprint in our data centers. As it stands now, too many COTS -- and hence, often-used -- frameworks are out of balance. They read too much data for the task, don't allow processing until it's all read in, etc.. This causes implementors to add hardware to scale up.