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Interview: How Univa Short Jobs Brings Low Latency to Financial Services

Gary Tyreman, CEO, Univa

With the launch of Univa Small Jobs add-on for Univa Grid Engine, the company, the company offers “the world’s most efficient processing and lowest latency available for important tasks like real-time trading, transactions, and other critical applications.” To learn more, we caught up with Univa President & CEO Gary Tyreman.

Bright Cluster Manager Speeds Deployment of Apache Hadoop

bright-cluster-manager-standard

Today Bright Computing announced a significant update to distribution-agnostic Bright Cluster Manager for Apache Hadoop at the Hadoop Summit North America 2015.

Video: Understanding Hadoop Performance on Lustre

Hadoop Cluster

“In this talk, Seagate presents details on its efforts and achievements around improving Hadoop performance on Lustre including a summary on why and how HDFS and Lustre are different and how those differences affect Hadoop performance on Lustre compared to HDFS, Hadoop ecosystem benchmarks and best practices on HDFS and Lustre, Seagate’s open-source efforts to enhance performance of Lustre within “diskless” compute nodes involving core Hadoop source code modification (and the unexpected results), and general takeaways ways on running Hadoop on Lustre more rapidly.”

Deploying Hadoop on Lustre Storage: Lessons Learned and Best Practices

Hadoop Cluster

In this video from LUG 2015 in Denver, J.Mario Gallegos from Dell presents: Deploying Hadoop on Lustre Storage: Lessons Learned and Best Practices. “Merging of strengths of both technologies to solve big data problems permits harvesting the power of HPC clusters on very fast storage.”

Penguin Computing Launches Scyld ClusterWare for Hadoop

penguin-computing-logo

Today Penguin Computing announced Scyld ClusterWare for Hadoop, adding greater capability to the company’s existing Scyld ClusterWare high performance computing cluster management solution.

Video: SGI UV Finds the Needle in the Big Data Haystack

straw

According to IDC, SGI has shipped approximately 8 percent of of all the Hadoop servers in production today. In fact, did you know that SGI introduced the word “Big Data” to supercomputing in 1996? Jorge Titinger, SGI President and CEO, shares SGI’s history in helping to design, develop, and deploy Hadoop clusters. (NOTE: Straw was substituted for actual hay to avoid any potential allergic reactions.)

Unleashing the Power of Parallel Storage

Hadoop Cluster

From Wall Street to the Great Wall, enterprises and institutions of all sizes are faced with the benefits – and challenges – promised by ‘Big Data’. But before users can take advantage of the near limitless potential locked within their data, they must have affordable, scalable and powerful software tools to manage the data.

Why Does Hadoop Have Such an Uncomfortable Fit in HPC?

Glenn Lockwood

“The evolution of Hadoop has very much been a backwards one; it entered HPC as a solution to a problem which, by and large, did not yet exist. As a result, it followed a common, but backwards, pattern by which computer scientists, not domain scientists, get excited by a new toy and invest a lot of effort into creating proof-of-concept codes and use cases. Unfortunately, this sort of development is fundamentally unsustainable because of its nucleation in a vacuum, and in the case of Hadoop, researchers moved on to the next big thing and largely abandoned their model applications as the shine of Hadoop faded.”

Progress Report on Efficient Integration of Lustre and Hadoop/YARN

goal

Using Hadoop with Lustre provides several benefits, including: Lustre is a real parallel file system, which enables temporary or intermediate data to be stored in parallel on multiple nodes reducing the load on single nodes. In addition, Lustre has its own network protocol, which is more efficient for bulk data transfer than the HTTP protocol. Additionally, because Lustre is a shared file system, each client sees the same file system image, so hardlinks can be used to avoid data transfer between nodes.

Porting Hadoop to HPC

intel

Ralph H. Castain from Intel presented this talk at the Adaptive Computing booth at SC13. “The solution allows customers to leverage both their HPC and big data investments in a single platform, as opposed to operating them in siloed environments. The convergence between big data and HPC environments will only grow stronger as organizations demand data processing models capable of extracting the results required to make data-driven decisions.”