February 21, 2011
The beginning of 2011 has produced a swift increase in the number of independent benchmarking efforts to find the “fairest one of all” in the land of cloud providers. Recent additions to the list of opponents battling it out for CPU and I/O supremacy included Joyent and its results from a battery of benchmarks announced this past week.
Cloud provider Joyent announced the results of a round of third-party benchmarking efforts conducted by the IMS Company that demonstrate that its own cloud computing services are faster and more efficient in terms of their use of infrastructure than Amazon’s EC2.
According to Joyent, following their memory and disk I/O and CPU tests comparing the Joyent SmartMachines, Windows Virtual Machines and Linux Virtual Machines to Amazon EC2 servers with the same specs their SmartMachine’s disk I/O is up to 14 times faster than Amazon EC2’s; their Linux Virtual Machine CPU is up to five times faster than EC2 and their Windows VM disk I/O is up to four times faster than a similar EC2 server.
Joyent’s approach to cloud computing is a bit different than one of its main competitors, Amazon Web Services. The company claims that to shed the problems of “legacy virtualization providers” and virtualizes the cloud operating system, but the hardware. The selling point they frequently tout is that via this method of service delivery, they can enable “faster I/O and CPU response times and in this way, users of Joyent cloud services don’t have to compromise to get the full benefits of the cloud.”
Joyent has been eager to prove its dominance in the cloud provider category but was not included in a critical benchmarking effort from CloudHarmony. The IMS Company behind the benchmark noted that their procedures for arriving at their results were intended to follow CloudHarmony’s efforts as closely as possible. There is more about independent cloud provider benchmarking service CloudHarmony here and exhaustive explanations of the benchmarks here.
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 23, 2013 |
The study of climate change is one of those scientific problems where it is almost essential to model the entire Earth to attain accurate results and make worthwhile predictions. In an attempt to make climate science more accessible to smaller research facilities, NASA introduced what they call ‘Climate in a Box,’ a system they note acts as a desktop supercomputer.
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.
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.