October 24, 2012
NEW YORK, Oct. 24 — On stage yesterday at a Hadoop workshop in New York City MapR Technologies, Inc., the Hadoop technology leader, announced the setting of a new 1TB TeraSort benchmark record of 54 seconds using Google Compute Engine.
This record setting TeraSort benchmark broke the one minute barrier with 1,003 servers, 4,012 cores and 1,003 disks as compared to the previous record set by Yahoo. The prior documented record of 62 seconds was set by Yahoo running Apache Hadoop on 1,460 servers, 11,680 cores and 5,840 disks.
“To set this record in a virtualized cloud environment is a testament to Google Compute Engine’s high performance infrastructure,” said M.C. Srivas, CTO and cofounder of MapR Technologies. “This demonstrates the viability of cloud infrastructures for large-scale workloads. MapR is the clear Hadoop performance leader achieving the record time with 1/6 the disks, 1/3 cores and 2/3 the servers.”
Attendees at O’Reilly Strata Conference + Hadoop World 2012 are invited to visit the MapR booth to learn more about the enterprise-grade Hadoop distribution and this record-breaking benchmark.
About MapR Technologies
MapR delivers on the promise of Hadoop, making managing and analyzing Big Data a reality for more business users. The award-winning MapR Distribution brings unprecedented dependability, speed and ease-of-use to Hadoop. Combined with data protection and business continuity, MapR enables customers to harness the power of Big Data analytics. Leading companies including Amazon, Cisco, EMC and Google partner with MapR to deliver an enterprise-grade Hadoop solution. Investors include Lightspeed Venture Partners, NEA and Redpoint Ventures.
The ever-growing complexity of scientific and engineering problems continues to pose new computational challenges. Thus, we present a novel federation model that enables end-users with the ability to aggregate heterogeneous resource scale problems. The feasibility of this federation model has been proven, in the context of the UberCloud HPC Experiment, by gathering the most comprehensive information to date on the effects of pillars on microfluid channel flow.
Large-scale, worldwide scientific initiatives rely on some cloud-based system to both coordinate efforts and manage computational efforts at peak times that cannot be contained within the combined in-house HPC resources. Last week at Google I/O, Brookhaven National Lab’s Sergey Panitkin discussed the role of the Google Compute Engine in providing computational support to ATLAS, a detector of high-energy particles at the Large Hadron Collider (LHC).
Frank Ding, engineering analysis & technical computing manager at Simpson Strong-Tie, discussed the advantages of utilizing the cloud for occasional scientific computing, identified the obstacles to doing so, and proposed workarounds to some of those obstacles.
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