February 27, 2013
LONDON, Feb. 27 — Concurrent Thinking has announced an enterprise version of its concurrentCOMMAND DCIM appliance to provide a resilient solution for monitoring and managing large-scale data centres. Michael Rudgyard, CTO at Concurrent Thinking, said, "This new announcement is a key step towards our goal to provide the most scalable data centre infrastructure management product on the market; further significant and incremental changes will be forthcoming in the next 12 months."
Unlike software-only DCIM offerings, concurrentCOMMAND is delivered as a hardened, single-U server which works in conjunction with multiple concurrentCONTROL devices to supply a complete solution for optimising data centre power efficiency. Key architectural enhancements to product software allow a significantly increased number of real time data centre metrics to be processed with increased resilience in scale-out environments.
The enhanced leverages Concurrent Thinking's existing concurrentCOMMAND and concurrentCONTROL appliance framework, as well as the use of high-performance round-robin databases for storing very large amounts of historical data in a fixed storage space. The appliances provide the capability to monitor and manage power at the distribution board, PDU and server level (including power capping). By collating power measurements with IT equipment utilisation, environmental conditions and cooling overheads, Concurrent's products are able to identify and reduce energy inefficiencies at the building, rack and IT levels.
Concurrent Thinking has also announced a new version of its entry level concurrentCOMMAND appliance, a new concurrentCONTROL appliance with multiple RS232 and RS485 interfaces, and the adoption of 1-wire technology that allows up to 40 temperature, pressure or humidity sensors to be daisy-chained up to 50 metres apart.
The new product release also debuts significant changes to the powerful and intuitive web GUI. These are aimed at improving the user's experience across all Internet connected devices. Key improvements include the adoption of modern HTML5 technologies for faster and more robust viewing, and for rendering graphical information more efficiently in its Data Centre Plan View and Data Centre Rack View, using internet-connected devices. "We believe that highly graphical representations of data not only improves the user experience, but provides powerful ways for identifying high level cost-saving measures ‘at a glance' while being able to drill down rapidly to understand specific problems relating to individual IT devices. Furthermore, graphics become significantly more important at scale," said Michael Rudgyard.
About Concurrent Thinking
Concurrent Thinking is a leading provider of DCIM solutions. Uniquely, the company has applied experience gained through its heritage in High Performance (HPC) and super-computing environments to the datacentre sector. This has resulted in a holistic and vendor neutral solution set with a rapid ROI, that delivers tangible benefits to multiple constituencies within any business that depends upon reliable and efficient datacentre operations.
Source: Concurrent Thinking
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.
May 16, 2013 |
When it comes to cloud, long distances mean unacceptably high latencies. Researchers from the University of Bonn in Germany examined those latency issues of doing CFD modeling in the cloud by utilizing a common CFD and its utilization in HPC instance types including both CPU and GPU cores of Amazon EC2.
May 10, 2013 |
Australian visual effects company, Animal Logic, is considering a move to the public cloud.
May 10, 2013 |
Program provides cash awards up to $10,000 for the best open-source end-user applications deployed on 100G network.
05/10/2013 | Cleversafe, Cray, DDN, NetApp, & Panasas | From Wall Street to Hollywood, drug discovery to homeland security, companies and organizations of all sizes and stripes are coming face to face with the challenges – and opportunities – afforded by Big Data. Before anyone can utilize these extraordinary data repositories, however, they must first harness and manage their data stores, and do so utilizing technologies that underscore affordability, security, and scalability.
04/02/2012 | AMD | Developers today are just beginning to explore the potential of heterogeneous computing, but the potential for this new paradigm is huge. This brief article reviews how the technology might impact a range of application development areas, including client experiences and cloud-based data management. As platforms like OpenCL continue to evolve, the benefits of heterogeneous computing will become even more accessible. Use this quick article to jump-start your own thinking on heterogeneous computing.