Posts Tagged ‘SuperMUC’

IBM Technical Computing Tackles Big Data

October 26, 2012

IBM Technical Computing, also referred to as high performance computing (HPC), bolstered its Platform Computing Symphony product for big data mainly by adding enterprise-ready InfoSphere BigInsights Hadoop capabilities. The Platform Symphony product now includes Apache Hadoop, map/reduce and indexing capabilities, application accelerators, and development tools. IBM’s recommended approach to simplifying and accelerating big data analytics entails the integration of Platform Symphony, General Parallel File System (GPFS), Intelligent Cluster, and DCS3700 storage.

This is not to say that IBM is leaving the traditional supercomputing and HPC market. Its Sequoia supercomputer recently topped the industry by delivering over 16 petaflops of performance.  Earlier this year it also unveiled the new LRZ SuperMUC system, built with IBM System x iDataPlex direct water cooled dx360 M4 servers encompassing more than 150,000 cores to provide a peak performance of up to three petaflops.  SuperMUC, run by Germany’s Bavarian Academy of Science’s Leibniz Supercomputing Centre, will be used to explore the frontiers of medicine, astrophysics, quantum chromodynamics, and other scientific disciplines.

But IBM is intent on broadening the scope of HPC by pushing it into mainstream business. With technical computing no longer just about supercomputers the company wants to extend technical computing to diverse industries. It already has a large presence in the petroleum, life sciences, financial services, automotive, aerospace, defense, and electronics for compute-intensive workloads. Now it is looking for new areas where a business can exploit technical computing for competitive gain.  Business analytics and big data are the first candidates that come to mind.

When it comes to big data, the Platform Symphony product already has posted some serious Hadoop benchmark results:

  • Terasort , a big data benchmark that tests the efficiency MapReduce clusters in handling very large datasets—Platform Symphony used 10x less cores
  • SWIM, a benchmark developed at UC Berkley that simulates real-world workload patterns on Hadoop clusters—Platform Symphony ran 6x faster
  • Sleep, a standard measure to compare core scheduling efficiency of MapReduce workloads—Platform Symphony came out 60x faster.

Technical computing at IBM involves System x, Power, System i, and PureFlex—just about everything except z. And it probably could run on the z too through x or p blades in the zBX.

Earlier this month IBM announced a number of technical computing enhancements including a high-performance, low-latency big data platform encompassing IBM’s Intelligent Cluster, Platform Symphony, IBM GPFS, and System Storage DCS3700. Specifically for Platform Symphony is a new low latency Hadoop multi-cluster capability that scales to 100,000 cores per application and shared memory logic for better big data application performance.

Traditionally, HPC customers coded their own software to handle the nearly mind-boggling complexity of the problems they were trying to solve. To expand technical computing to mainstream business, IBM has lined up a set of ISVs to provide packaged applications covering CAE, Life Science, EDA, and more. These include Rogue Wave, ScaleMP, Ansys, Altair, Accelrys, Cadence, Synopsys, and others.

IBM also introduced the new Flex System HPC Starter Configuration, a hybrid system that can handle both POWER7 and System x.  The starter config includes the Flex Enterprise Chassis, an Infiniband (IB) chassis switch, Power7 compute node, and an IB expansion card for Power or x86 nodes. Platform Computing software handles workload management and optimizes resources. IBM describes it as a high density, price/performance offering but hasn’t publicly provided any pricing. Still, it should speed time to HPC.

As technical computing goes mainstream it will increasingly focus on big data and Hadoop.  Compute-intensive, scientific-oriented companies already do HPC. The newcomers want to use big data techniques to identify fraud, reduce customer churn, make sense of customer sentiment, and similar activities associated with big data. Today that calls for Hadoop which has become the de facto standard for big data, although that may change going forward as a growing set of alternatives to Hadoop gain traction.


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