New Products
SSC is offering a complete package to get you into “the Linux experience”. The package includes a Linux Distribution on CD-ROM, a certificate for a 1-year subscription to Linux Journal, printed copies of Matt Welsh's Linux Installation & Getting Started, 3-hole punched copies of the Linux How-Tos and four of SSC's own publications (Internet Public Access Guide, Beginning Unix command Summary, RS-232 Reference and Bourne Shell Tutorial).
The package sells for $88.95. SSC, P.O. Box 55549, Seattle, WA 98155, (206) 527-3385 or e-mail to sales@ssc.com for more information or a complete SSC catalog.
Fintronic USA, Inc. is pleased to announce the availability of FinSim for the Linux platform. FinSim: A high performance, compiled and interpreted simulation environment that supports both UDL/I and Verilog HDL. The FinSim UDL/I simulator features full language implementation. The FinSim Verilog simulator features full Verilog HDL implementation including gate, switch-level, user defined primitives, behavior, specify blocks, path delays, system tasks and functions, PLI, and VCD.
Currently FinSim runs on Sun Sparc, Digital MIPS, IBM RS/6000 and SGI MIPS workstations. It is also available on Intel x86 platforms running Windows NT, SVR4.2, interactive Unix 3.2 or Linux. It will soon be available on Digital Alpha, Pentium and Hewlett-Packard platforms.
FinSim prices for a Unix workstation range from $15,000 to $25,000 including free upgrades to SDF. The list price for FinSim for a PC platform is two thirds of the price of FinSim for a Unix workstation. Contact Fintronic directly for available discounts and terms.
E-mail: info@fintronic.com fax: +1.415.325.4908 tel: +1.415.325.4474 mail: Fintronic USA, Inc. 1360 Willow Rd., Suite 205 Menlo Park, CA 94025 USA
Fintronic also sells pre-installed, fully configured Linux workstations and notebooks. For information on this service please finger or send mail to linux-sales@fintronic.com.
Realizing the promise of Apache® Hadoop® requires the effective deployment of compute, memory, storage and networking to achieve optimal results. With its flexibility and multitude of options, it is easy to over or under provision the server infrastructure, resulting in poor performance and high TCO. Join us for an in depth, technical discussion with industry experts from leading Hadoop and server companies who will provide insights into the key considerations for designing and deploying an optimal Hadoop cluster.
Sponsored by AMD
If you already use virtualized infrastructure, you are well on your way to leveraging the power of the cloud. Virtualization offers the promise of limitless resources, but how do you manage that scalability when your DevOps team doesn’t scale? In today’s hypercompetitive markets, fast results can make a difference between leading the pack vs. obsolescence. Organizations need more benefits from cloud computing than just raw resources. They need agility, flexibility, convenience, ROI, and control.
Stackato private Platform-as-a-Service technology from ActiveState extends your private cloud infrastructure by creating a private PaaS to provide on-demand availability, flexibility, control, and ultimately, faster time-to-market for your enterprise.
Sponsored by ActiveState
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Free Webinar: Hadoop
How to Build an Optimal Hadoop Cluster to Store and Maintain Unlimited Amounts of Data Using Microservers
Realizing the promise of Apache® Hadoop® requires the effective deployment of compute, memory, storage and networking to achieve optimal results. With its flexibility and multitude of options, it is easy to over or under provision the server infrastructure, resulting in poor performance and high TCO. Join us for an in depth, technical discussion with industry experts from leading Hadoop and server companies who will provide insights into the key considerations for designing and deploying an optimal Hadoop cluster.
Some of key questions to be discussed are:
- What is the “typical” Hadoop cluster and what should be installed on the different machine types?
- Why should you consider the typical workload patterns when making your hardware decisions?
- Are all microservers created equal for Hadoop deployments?
- How do I plan for expansion if I require more compute, memory, storage or networking?




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