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TPC Council Launches First Vendor-Neutral Big Data Benchmark

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Today the Transaction Processing Performance Council (TPC) today announced the immediate availability of TPCx-HS, developed to provide verifiable performance, price / performance, availability, and optional energy consumption metrics of big data systems.

Python’s Role in Big Data Analytics: Past, Present, and Future

Travis Oliphant

In this video from EuroPython 2014, Travis Oliphant from Continuum Analytics presents: Python’s Role in Big Data Analytics: Past, Present, and Future.

Supercomputing Technologies for Big Data Challenges

Ferhat Hatay

In this special guest feature, Ferhat Hatay from Fujitsu writes that supercomputing technologies developed for data-intensive scientific computing can be a powerful tool for taking on the challenges of Big Data. We all feel it, data use and growth is explosive. Individuals and businesses are consuming — and generating — more data every day. The […]

Google Mesa: Geo-Replicated, Near Real-Time, Scalable Data Warehousing

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Over at GigaOM, Derrick Harris writes that Google has developed a data warehousing system called Mesa that is designed to handle real-time data while maintaining performance even if an entire data center goes offline.

Satoshi Matsuoka Presents: A Look at Big Data in HPC

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In this video from the DDN User Group at ISC’14, Satoshi Matsuoka from the Tokyo Institute of Technology presents: A Look at Big Data in HPC. “HPC has been dealing with big data for all of its existence. But it turns out that the recent commercial emphasis on big data, has coincided with a fundamental change in the sciences as well. As scientific instruments and facilities produce large amounts of data in an unprecedented rate, the HPC community is reacting to this, with revisiting architecture, tools, and services to address this growth in data.”

Catalyst Supercomputer at Livermore Open for Big Data Innovation

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“The increased storage capacity of the system (in both volatile and nonvolatile memory) represents the major departure from classic simulation-based computing architectures common at DOE laboratories and opens new opportunities for exploring the potential of combining floating point focused capability with data analysis in one environment. The machine’s expanded DRAM and fast, persistent NVRAM are well suited to a broad range of big data problems including bioinformatics, business analytics, machine learning and natural language processing.”

Marc Hamilton Looks at China HPC

“Like the US, Japan, and Europe, China still has plans to build giant HPC systems like Tianhe. However, increasingly these systems are being looked at to support commercial HPC workloads like machine vision in a cloud environment in addition to just scientific data processing.”

The Scary Side of AI and Big Data

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Douglas Eadline writes that recent big investments in AI technology by IBM and Google show that intelligent systems are the future of big business. The problem is, these advancements could come at the expense of our privacy.

Porting Hadoop to HPC

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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.”

How CRISP is Tackling the Data Deluge in International Science

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“CRISP is helping to standardize data-access through a common identity system, or federated identity, allowing identities to transcend facilities or countries, and providing scientists with a single online identity or login to access all their data in one place – regardless of where it’s from.”